WO2021152030A1 - Compilateur pour données d'analyse - Google Patents

Compilateur pour données d'analyse Download PDF

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Publication number
WO2021152030A1
WO2021152030A1 PCT/EP2021/052014 EP2021052014W WO2021152030A1 WO 2021152030 A1 WO2021152030 A1 WO 2021152030A1 EP 2021052014 W EP2021052014 W EP 2021052014W WO 2021152030 A1 WO2021152030 A1 WO 2021152030A1
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WIPO (PCT)
Prior art keywords
analysis
specifiers
data
node
instruction
Prior art date
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PCT/EP2021/052014
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English (en)
Inventor
Mouhamad KAWAS
Bassel ALKHATIB
Nadine NEHME
Original Assignee
Medicus Ai Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from EP20182720.1A external-priority patent/EP3929929A1/fr
Application filed by Medicus Ai Gmbh filed Critical Medicus Ai Gmbh
Publication of WO2021152030A1 publication Critical patent/WO2021152030A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Definitions

  • the present invention relates to the field of compilers and the field of processing of biological or medical samples.
  • Instructions for processing of the sample or analyses to be performed must be processed by a laboratory performing said analyses.
  • a problem can for example occur if a system or entity requesting said analyses does not provide instructions for the processing and the analyses so that the instructions can be processed by a data-processing system of the laboratory.
  • a result node i.e. a system that should receive the result for further processing.
  • the result node can then process and/or output the result.
  • the result node must be able to process the result. Again, a problem occurs if the result provided from the laboratory is not compatible to the result node.
  • a common approach for the above-mentioned problem is a replacement of equipment so as to only use equipment using compatible data types or formats. This can limit a flexibility of usable equipment in a laboratory, be a problem during replacement of equipment or even hinder automated processes, if not all components are available using same data types or formats.
  • Another approach is reprogramming deployed equipment, which may require significant efforts and always brings a risk of malfunctions due to errors in the modified software.
  • EP1200839A1 discloses an integrated clinical laboratory software system for testing a specimen.
  • US20110119309A1 discloses a gateway which enables medical (including genetic and genomic) laboratories and health care providers (collectively “clients”) to communicate electronic messages with each other without developing and maintaining an interface for each peer.
  • the system may comprise a compiler node.
  • the compiler node may be configured for processing data.
  • the system may further comprise at least one or a plurality of analysis node(s).
  • the system may further comprise at least one or a plurality of instruction node(s).
  • the instruction node(s) may be configured for generating instructions, particularly instructions for at least one or a plurality of analyses of at least one or a plurality of samples.
  • the samples may be samples originating from the human body.
  • the samples may comprise samples of a bodily fluid, such as blood, urine or saliva.
  • the samples may for example also comprise samples of at least one tissue from the human body, such as from a biopsy.
  • the term "analysis" is intended to refer to measuring in the classical sense, but for example also to chemical, bio-chemical or biologic analysis of an object, which generates information about at least one physical, chemical, biological or bio-medical feature of the object.
  • the object can for example be a sample.
  • the measuring can be direct, such as measuring a temperature by means of a sensing unit configured for sensing the temperature. It can also refer to indirectly determining data based on sensed data, for example, an acceleration can be indirectly measured by measuring a corresponding velocity and generating a derivate with respect to the time, or by an adapted sensing unit that senses a force of an object with known inertia against the accelerated object.
  • a concentration of a substance A in a mixture B is measured by determining the amount of substance A in a known or determined amount of substance B. As discussed above, this can for example be the case when measuring a concentration of cholesterol in blood of a user.
  • the system further may comprise at least one or a plurality of result node(s).
  • the compiler node may comprise a parser.
  • the parser may be configured for parsing data, particularly data that is not originally machine readable.
  • the parser may for example be configured for optical character recognition (OCR).
  • OCR optical character recognition
  • the parser may be at least partially implemented in software.
  • the compiler node may be configured for performing an instruction compilation step.
  • the compiler node may be configured for performing an association generating step.
  • the analysis node(s) may comprise at least one control component and a plurality of analysis components. Each of the analysis node(s) may comprise at least one control component and a plurality of analysis components.
  • the analysis node(s) may each comprise at least one data-transmission component.
  • the data-transmission component may be configured for sending and/or receiving data.
  • the compiler node may be configured for receiving first analysis instruction data from the instruction node(s).
  • the compiler node may be configured for generating second analysis instruction data and for accessing a data structure for storing analysis specifiers of different types.
  • Analysis specifiers are intended to refer to at least one of analysis instructions and indications of a (performed) analysis.
  • the types may be types of indications of analyses, such as a machine-interpretable indication.
  • An analysis instruction can for example correspond to a sample for which the analysis instruction is to be performed.
  • An indication of an analysis can for example correspond to a numeric value to indicate a result of an analysis.
  • analysis instructions may refer to instructions to perform at least one or a plurality of analyses.
  • the analysis instructions may for example relate to an analysis to be performed, a type of a sample to be analyzed, a preparation of the sample and/or a variable to be determined, such as a concentration of a substance, such as cholesterol, in a sample, such as blood of the user.
  • the analysis instructions may be specific to one or more analysis nodes.
  • an analysis specifier of a first type may comprise text such as "Aluminum in Serum or Plasma in mol/ml", "High Density Lipid Cholesterol” or “Cholesterol High Density Lipoprotein”.
  • An analysis specifier of another type may comprise a numeric code such as "14593-8" or another text, such as "Aluminium in Serum Oder Plasma in mol/ml”.
  • the compiler node may be configured for transmitting the second analysis instruction data to the analysis node(s). For example, a part of the second analysis instruction data may be transmitted to an analysis node A, another part may be transmitted to an analysis node B, and still another part may be transmitted to an analysis node C.
  • the transmitting may comprise sending on request of an analysis node ("pull") or initiated by the compiler node ("push").
  • the first analysis instruction data may comprise analysis node data.
  • the analysis node data comprise an indication of at least one of the analysis node(s).
  • the analysis node data may comprise an indication of an analysis node to which an instruction or a part of the instruction data is to be transmitted.
  • the indication may also comprise a criterion for an analysis node to which a part of the instruction data is to be transmitted, e.g. a test or a set of tests that the analysis node must perform.
  • the compiler node may be configured for transmitting the second analysis instruction data to the analysis node(s) indicated by the analysis node data of the first analysis instruction data.
  • the first analysis instruction data may comprise first analysis instructions. A part of the first analysis instructions may be of a type A.
  • the type A may for example correspond to a system of representing an analysis or an analysis instruction.
  • an analysis instruction of a type A may be "High Density Lipid Cholesterol".
  • the analysis node(s) may be configured for providing the first analysis instructions.
  • the second analysis instruction data may comprise second analysis instructions.
  • a part of the second analysis instructions may be of the type B.
  • an analysis instruction of a type B may be "Cholesterol HDL (High Density Lipoprotein)".
  • a part of the first analysis instructions may of a type B.
  • a part of the second analysis instructions may be of a type C, such as " HDC (High- Density Cholesterol)".
  • the analysis node data may comprise an indication of at least one or a plurality of type(s) of the second analysis instructions.
  • the indication may be configured for determining a type to which a part of the second analysis instructions must correspond so as to be processed by one of the analysis node(s).
  • the indication may for example indicate a name of such a type (such as "LOINC-number") or a code representing a type, such as "0", "1” and "2" for different types.
  • the analysis node(s) may be configured for operating according to the second analysis instruction data.
  • the analysis node(s) may be configured for executing the second analysis instructions of the second analysis instruction data. In other words, the analysis node(s) may be configured for performing operations based on the second analysis instructions.
  • the second analysis data instructions may comprise an indication relating to the analysis node(s).
  • the parser may be configured for processing the first analysis instruction data.
  • the parser may further be configured for generating machine-interpretable first analysis instruction data, as discussed above.
  • the first analysis instruction data and the second analysis instruction data may comprise processing instruction data.
  • the processing instruction data may be configured for indicating a processing of at least one or a plurality of sample(s).
  • the processing of the sample(s) can for example refer to a pre-processing step of the sample(s) for analysis, such as purification procedures before a PCR-analysis is performed.
  • the first analysis instruction data and the second analysis instruction data may comprise instruction sample type data.
  • the instruction sample type data may be configured for indicating a type of the sample(s).
  • the first analysis instruction data and the second analysis instruction data comprise instruction identification data.
  • the instruction identification data may be configured for identifying the sample(s) to which the instructions of the instruction data refer.
  • the instruction identification data may be configured for identifying the samples.
  • the instruction identification data may comprise at least one or a plurality of sample identifiers.
  • the instruction identification data may for example comprise an identification number, an index, or an identifier comprising an indication of a user from whom the sample originates.
  • the instruction identification data may for example also comprise an identification of a health care provider, an entity or a practitioner who took the sample.
  • the instruction identification data may relate to an index on a container or packaging of the sample, as discussed e.g. in WO98/05427 or EP 1 803 11.
  • the container or the packaging of the sample may comprise an indication of a corresponding sample identifier.
  • the compiler node may be configured for determining for each of the first analysis instructions a type of a corresponding second analysis instruction.
  • the second analysis instruction corresponding to the first analysis instruction may be configured for indicating a same analysis as the first analysis instruction.
  • the second analysis instructions may however of be a different type as the first analysis instruction.
  • Determining the type of the second analysis instruction may be based on data that the system or the compiler node has received. Determining the type of the second analysis instruction may also be based on an analysis node or a plurality of analysis nodes which are configured to operate according to the second analysis instruction.
  • the types of the second analysis instructions may be different for different first analysis instructions.
  • the determined type of the corresponding second analysis instruction may be different for different first analysis instructions. This may be optionally advantageous, as thus, instructions for analysis nodes which are configured for processing different types of analysis specifiers may be provided.
  • the compiler node may be configured for determining the type of the corresponding second analysis instruction.
  • the compiler node may be configured for determining the type of the corresponding second analysis instruction based on the analysis node data.
  • the compiler node may be configured for generating for some of the first analysis instructions a corresponding second analysis instruction of the respectively determined type. In some cases, the compiler node may be configured for keeping the first analysis instructions, for example when the respectively determined type is a same type as the type of the first analysis instruction.
  • the compiler node may be configured for searching associations of the some first analysis instructions in the data structure.
  • the compiler node may be configured for searching the associations of the first analysis instructions for which the type of the first and the corresponding second analysis instruction differ.
  • the compiler node may be configured for generating the second analysis instructions based on these associations found.
  • the compiler node may be configured for searching for associations of first analysis instructions in the data structure, for which the determined type is different from the actual type of the analysis instruction.
  • the compiler node may also be configured for searching only for associations of first analysis instructions in the data structure, for which the determined type is different from the actual type of the analysis instruction.
  • the compiler node may be configured for only searching for associations of analysis instruction, for which the type of the respective first and second analysis instruction differs.
  • the system may be configured for generating at least one or a plurality of association(s) of analysis specifiers.
  • the system may be configured for generating the at least one or the plurality of association(s) of analysis specifiers, associating the at least one or the plurality of analysis specifiers to specifiers of a respectively different type. This may be optionally advantageous, as it may enable generating the second analysis instructions based on the first analysis instructions.
  • the associations may for example be links between specifiers, such as in a relational database, a dictionary-type data structure or another suitable data structure, for example a data-structure for a symbol table of a compiler.
  • specifiers such as in a relational database, a dictionary-type data structure or another suitable data structure, for example a data-structure for a symbol table of a compiler.
  • an association might link the specifiers "Aluminum in Serum or Plasma in mol/ml", “Aluminium in Serum Oder Plasma in mol/ml", “14593-8” and "Aluminium in Serum Oder Plasma, Stoffmengenkonzentration”.
  • the system may be configured for generating the association(s) for at least one or a plurality of the first analysis instructions.
  • the system may be configured for generating the associations for at least one or a plurality of the first analysis instruction(s) for which associations to the determined type were not found in the data structure.
  • the system may be configured for adding the generated associations to the data structure. This may be optionally advantageous, as it may save processing time for generating an association a further time, when the associations or an association thereof is needed again for another part of the first instruction data.
  • the compiler node may be configured for generating the association(s).
  • the analysis node data may be configured for indicating for each first analysis instruction an analysis node. Said indicated analysis node may be configured for executing the second analysis instruction corresponding to the respective first analysis instruction.
  • the analysis node(s) may be configured for processing the sample(s) according to the processing instruction data.
  • the analysis node(s) may be configured for processing the sample(s) based on the instruction sample type data. For example, a type of pre-processing can be chosen for the sample(s) or some of the sample(s) based on the type of the sample(s). E.g., a sample preparation corresponding to a sample type can be performed.
  • the analysis node(s) may be configured for selecting the sample to be processed and/or analyzed according to an analysis instruction based on the instruction identification data and an identification of the sample(s), such as the sample identifier.
  • the control component of each of the analysis node(s) may be configured for controlling the plurality of analysis components of the respective analysis node(s) according to the second analysis instructions.
  • the control component of each of the analysis node(s) may be configured for controlling the respective plurality of analysis components so as to execute the second analysis instructions.
  • Each control component may be configured for controlling the analysis components based on the second analysis instructions corresponding to the respective analysis node.
  • a first control component controlling the analysis components of a first analysis node may control these analysis components according to a first group of the second analysis instructions, which group may correspond to the first analysis node.
  • a second control component controlling the analysis components of a second analysis node may control these analysis components according to a second group of the second analysis instructions, which group may correspond to said second analysis node.
  • the analysis node(s) may be configured for receiving the second analysis instruction data by means of the data-transmission component(s). As discussed above, single analysis nodes may each comprise a part of the second analysis instruction data, so that the analysis node(s) together receive the second analysis instruction data.
  • the analysis node(s) may be configured for generating result data.
  • the analysis node(s) may be configured for generating first result data.
  • the control component may be configured for generating the result data, preferably the first result data, based on operations performed by the analysis components. For example, the control component may generate the result data, preferably the first result data, based on analysis data generated by the analysis components.
  • the analysis node(s) may be configured for transmitting the result data, preferably the first result data, by means of the data-transmission component(s).
  • the analysis node(s) may be configured for transmitting the result data, preferably the first result data, to the compiler node.
  • the compiler node may be configured for modifying the result data, preferably the first result data, based on the data structure, so as to be according to a set of analysis specifiers.
  • the compiler node may be configured for generating second result data.
  • the compiler node may be configured for transmitting the result data, preferably the second result data, to the result node(s).
  • the instruction nodes may comprise the result nodes.
  • the instruction nodes may be data-processing systems of health care providers, which health care providers may then process the results of the analysis or the analyses subsequently.
  • the instruction nodes may be different from the result nodes. This may be optionally advantageous for data protection reasons, e.g. if the instruction node further performs other functions, such as administrating an insurance policy of a user.
  • the analysis node(s) may be configured for performing at least one bio-medical analysis.
  • the sample and/or the samples i.e. the sample(s)
  • the analysis instructions may comprise instructions relating to bio-medical analyses of the sample(s) originating from the human body.
  • the analysis node(s) may be configured for generating a at least one or a plurality of analyses of the sample(s).
  • the data-processing system comprises a transmission component and a data storage component.
  • the transmission component may be configured for data-transmission.
  • the transmission component may comprise external communication interfaces configured to facilitate electronic data exchange between the data-processing system for association analysis specifiers and devices or networks external to the data processing system 50.
  • the transmission component may comprise network interface card(s) that may be configured to connect the data processing system to a network, such as, to the Internet
  • the data-processing system may further comprise a pre-processing component.
  • the data-processing system may comprise a selection component.
  • the data-processing system may comprise an association component.
  • the data-processing system may be configured for processing the plurality of sets of the analysis specifiers.
  • Each set may comprise at least one or a plurality of analysis specifier(s) of a same type.
  • the plurality of sets of analysis specifiers may comprise at least the first set of analysis specifiers.
  • the types of the analysis specifiers may be mutually different between the sets.
  • the pre-processing component may be configured for pre-processing analysis specifiers at least of the first set of analysis specifiers.
  • the association component may be configured for generating data indicating associations of the analysis specifiers.
  • the data-processing system may be configured for receiving at least one of the sets of analysis specifiers.
  • the data-processing system may be configured for receiving a set of analysis specifiers from another component, or from the compiler node.
  • the set may comprise analysis specifiers for which the other component or the compiler node does not comprise associations or for which a corresponding association is not stored in the data structure.
  • the method may comprise receiving several of the plurality of sets of analysis specifiers.
  • the data-processing system may be configured for providing a set of pre- processed specifiers of a set of intermediary analysis specifiers.
  • Providing the set of the pre-processed specifiers of the set of intermediary analysis specifiers may comprise generating the pre-processed specifiers. Providing may additionally or alternatively also comprise loading the pre-processed specifiers from a storage medium or receiving the pre-processed specifiers from another system.
  • computing time may be saved. Further, computing time of the data-processing system may be saved.
  • the pre-processing component may further be configured for generating the set of pre-processed specifiers of the set of the intermediary analysis specifiers.
  • the pre-processing component may be configured for pre processing analysis specifiers at least of a second set of analysis specifiers.
  • the second set of the analysis specifiers may comprise specifiers of a second same type, which may be different from the type of the specifiers of the first set of analysis specifiers.
  • the pre-processing component may be configured for pre-processing specifiers at least by applying a removing criterion to each of the specifiers and removing at least a portion of the respective specifier if indicated by the removing criterion.
  • the pre-processing component may be configured for removing at least the portion of the respective specifier depending on a result of the removing criterion.
  • the removing criterion may be configured for indicating removal of at least one type of words.
  • the type of words may for example be a grammatical type of words, a defined group of words, words of a certain language or words with a certain function.
  • the removing criterion may be configured for indicating removal of stop words.
  • the type of words may be stop words.
  • the stop words may refer to words with low relevance in analysis of meaning of texts.
  • the stop words may be specific to a language of the specifier. For a list of stop words in English, see for example "SEO Stop Words: How Stop Words impact SEO", https://searchenginenation.com/stop-words/, retrieved at June 26, 2020.
  • the pre-processing component may be configured for pre-processing specifiers at least by converting at least one segment of each specifier to a pre-determined form.
  • the pre-processing component may be configured for converting the segments to a pre-determined grammatical form.
  • the pre-processing component may further be configured for converting the segments to at least one of a plural form and a singular form.
  • the pre-processing component may be configured for pre-processing specifiers at least by replacing text segments of the respective specifier by corresponding text segments.
  • a data structure such as a table or a relational database, which comprises corresponding text segments and optionally an indication which segments are to be kept and which are to be converted.
  • a data structure such as a table or a relational database, which comprises corresponding text segments and optionally an indication which segments are to be kept and which are to be converted.
  • the data structure may comprise an indication that A, B and C are corresponding, and optionally, that B as well as A are to be replaced by C.
  • the pre-processing component may be configured for pre-processing specifiers at least by replacing text segments by equivalent text segments.
  • the equivalent text segments may by synonyms of the text segments to be replaced. For example, replacing synonyms in the above-discussed way may yield a same result for two specifiers "Mean Cell Volume” and “Cells average size” referring to a same measure or analysis result.
  • the pre-processing component may be configured for pre-processing specifiers at least by generating a hash-value for each specifier.
  • Generating a hash-value for a specifier may be performed by means of a hash function, which may be cryptographic. However, the hash function does not need to be cryptographic.
  • a length of a specifier or an amount of characters of a specifier may also be a hash-function.
  • Another example can be a sum of parts of an element of the specifier, such as a sum of ASCII-values of characters of a reference element of the specifier, as discussed above.
  • Generating the hash-value for each of the specifiers may be optionally advantageous as it allows to easily detect terms that may be identical, since their hash- value may be identical. Further, optionally advantageously, comparing hash values of specifiers may be less computing-time intensive than (literally) comparing the entire specifiers.
  • some hash functions such as a sum of parts of a specifier, may generate a same result for two elements that are identical apart from an exchanged order. This can be a resource-efficient way to estimate specifiers or elements thereof that are identical apart from an interchange of parts, i.e. a changed order of the parts, compared to a comparison algorithm that exactly determines whether two specifiers or elements are identical apart from the interchange.
  • the pre-processing component may be configured for pre-processing specifiers at least by generating a plurality of different hash-values for each of the specifiers.
  • the pre-processing component may be configured for applying different hash algorithms to the specifiers or for hashing different portions of the specifiers.
  • the pre-processing component may be configured for generating a plurality of hash values for each of the specifiers.
  • the pre-processing component may be configured for pre-processing specifiers at least by representing the specifiers as bags of words. In other words, segments of the specifiers may be represented as multisets, known in the art.
  • the pre-processing component may be configured for at least one of (a) applying a removing criterion and removing corresponding portions of specifiers, (b) applying generating a hash value and (c) generating a plurality of different hash values.
  • the data-processing system may be configured for providing the set of pre- processed specifiers of the set of intermediary analysis specifiers by loading and/or receiving said set. [128] The data-processing system may be configured for providing subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers of the first set.
  • the data-processing system may be configured for providing a plurality of subsets of the intermediary analysis specifiers, wherein each subset of the intermediary analysis specifiers may be for one of the analysis specifiers of the first set.
  • Providing the subsets may be selecting the subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers from the first set.
  • the selection component is configured for selecting the subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers from the first set.
  • the data-processing system may be configured for estimating similarity values of the pre-processed analysis specifiers of the first set to at least some of the intermediary specifiers.
  • the selection component may be configured for estimating similarity values of the pre-processed analysis specifiers of the first set to at least some of the intermediary specifiers.
  • the data-processing system and/or the selection component may be configured for estimating said similarity values for pairs of each pre-processing analysis specifier and each intermediary specifier. However, the data-processing system and/or the selection component may also be configured to perform the estimation only for some of these pairs, for example only until an estimation yields a similarity above a certain threshold.
  • the data-processing system may be configured for selecting the subsets of the intermediary specifiers is based on the similarity values.
  • the selection component may be configured for selecting the subsets of the intermediary specifiers is based on the similarity values.
  • the data-processing system may be configured for selecting the subsets of the intermediary specifiers by a similarity criterion.
  • the selection component may be configured for selecting the subsets of the intermediary specifiers by a similarity criterion.
  • the similarity criterion may relate to the similarity values.
  • the similarity criterion may for example relate to a quantification of the estimated similarity, such as a maximum divergence of two hash results.
  • the similarity criterion may for example also relate to a number of n intermediary specifiers with a highest result of the similarity criterion for the corresponding specifier from the first set.
  • At least one of the data-processing system and the selection component may be configured for not selecting a subset of the intermediary specifiers for a pre-processed specifier of the first set, if the estimated similarity values for all of the intermediary specifiers for said pre-processed specifier are below a pre-defined threshold.
  • At least one of the data-processing system and the selection component may be configured to generate output and/or notification data.
  • selection of erroneous subsets may be reduced, e.g. in a case where the intermediary specifiers do not comprise a specifier corresponding to the specifier from the first set, or where the "true" corresponding specifiers from the first set and from the set of intermediary specifiers are not similar.
  • erroneous results of the data-processing system and/or the selection component may be reduced.
  • the association component may be configured for generating associations of the pre-processed analysis specifiers from the first set and corresponding specifiers selected from at least one of the intermediary analysis specifiers and analysis specifiers from the second set of analysis specifiers.
  • At least one of the data-processing system and the association component may be configured for adding at least some of the associations to the data structure for storing associations of the analysis specifiers of different types.
  • the data-storage component may be configured for storing the data structure for storing associations of analysis specifiers of different types.
  • the transmission component may be configured for transmitting the at least some of the associations to a data-storage system.
  • the data-storage system may be configured for storing the data structure for storing the associations of analysis specifiers of different types.
  • the association component may be configured for applying a comparison criterion to the pre-processed specifiers of the first set and specifiers from the intermediary specifiers.
  • the association component may be configured for generating for each of the pre- processed analysis specifiers of the first set an association to a respective analysis specifier of the intermediary specifiers for which the comparison criterion provided a result indicating a minimal difference between the analysis specifier and the intermediary specifier.
  • the minimal difference may be minimal relative to differences between the analysis specifier and other intermediary specifiers.
  • the comparison criterion may comprise a comparison by first applying a phonetic algorithm, such as Metaphone algorithm or Double Metaphone algorithm, to the analysis specifier from the first set and the intermediary specifier and character-wise comparison of a result.
  • the comparison can also be on a basis of parts of the specifiers, e.g. separately for each segment, such as each word.
  • the comparison criterion may further comprise calculating for each word of the first specifier, the max similarity ratio with relevant word of the second specifier. Such a ratio may for example be 0.91 for a comparison of "this" and "these".
  • the comparison criterion may further comprise processing results of comparing the parts of the specifiers. See the below formula as an example, wherein ratio , denotes the above-mentioned ratio of the comparison for each part, the index / indicates an ordinal number of said part, and rii and n ⁇ indicate amounts of parts of the specifiers to which the comparison criterion is applied. X may then indicate a result of the comparison criterion. ratio, nl+ n 2
  • s2 ['these', 'two', 'are', 'not', 'that', 'close']
  • X is then calculated as (0.27 (how/not), 1 (close/close), 0(is/-), 0.9(this/these), 0.3 (to/two), l(that/that) / 6.
  • the comparison criterion may thus yield 0,57.
  • the association component may be configured for applying the comparison criterion for the pre-processed specifiers of the first set to the respective pre-processed specifier from the first set and the intermediary specifiers from the respectively selected subset.
  • the association component may also be configured for applying the comparison criterion for the pre-processed specifiers of the first set only to the respective pre- processed specifier from the first set and the intermediary specifiers from the respectively selected subset.
  • the comparison criterion may be applied to the pre-processed specifiers of the first set in their pre-processed form. It may however also be applied to the pre-processed specifiers of the first set in their original form.
  • association component may be configured for applying the comparison criterion to each pair of a specifier of the first set and the intermediary specifiers of the respectively selected subset.
  • the pre-processing component may further be configured for pre-processing analysis specifiers of the second set of analysis specifiers.
  • the pre processing component may be configured for pre-processing analysis specifiers of the second set of analysis specifiers as discussed above with respect to the pre-processing of specifiers of the first set of analysis specifiers.
  • the selection component is may be configured for providing subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers of the second set.
  • the selection component may be configured for providing subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers of the second set as discussed above for the analysis specifiers of the first set.
  • the association component may further be configured for applying the comparison criterion to the pre-processed specifiers of the second set and specifiers from the intermediary specifiers and for generating associations of pre-processed analysis specifiers of the second set based thereon.
  • the association component may be configured for generating the associations for at least some of the pre-processed analysis specifiers of the second set.
  • the association component may further be configured for generating associations of at least some or all of the pre-processed specifiers of the first set and at least some or all of the pre-processed specifiers of the second set.
  • the association component may be configured for generating these associations based on the associations of the pre- processed specifiers of the first set to the intermediary analysis specifiers and the associations of the pre-processed specifiers of the second set to the intermediary analysis specifiers.
  • the association component may be configured for generating associations of pre- processed specifiers of the first set and pre-processed specifiers of the second set, which specifiers are associated to same intermediary analysis specifiers.
  • the association component may be configured for generating an association of A1 to Cl.
  • the data-processing system may be configured for sending the data structure to the compiler node.
  • the transmission component may be configured for sending the data structure to the compiler node.
  • At least one of the data-processing system and the transmission component may be configured for sending the generated associations to the compiler node.
  • the analysis specifiers may be at least one of analysis instructions and analysis indications, as discussed above.
  • the data-processing system may be configured for using the data structure for transforming analysis results from the first set of analysis specifiers to the second set of analysis specifiers.
  • At least some or all of the analysis specifier(s) may relate to a bio-medical analysis or bio-medical analyses.
  • At least some or all of the analysis specifier(s) may relate to an analysis or analyses of at least one or a plurality of sample(s) originating from the human body.
  • At least some or all of the analysis specifier(s) may relate to a bio-medical analysis or biomedical analyses of at least one or a plurality of sample(s) originating from the human body.
  • the data-processing system may be a data-processing system configured for associating the analysis specifiers.
  • the above-discussed system may comprise the data-processing system.
  • the above-discussed compiler node may comprise the data-processing system.
  • the method comprises an analysis instruction receiving step, an instruction compilation step and a transmission step.
  • the analysis instruction receiving step comprises receiving the first analysis instruction data from the at least one or the plurality of instruction node(s).
  • the instruction compilation step comprises generating the second analysis instruction data and accessing the data structure for storing analysis specifiers of different types.
  • the transmission step comprises transmitting the second analysis instruction data to the at least one or the plurality of analysis node(s).
  • the first analysis instruction data may comprise the analysis node data.
  • the analysis node data may comprise the indication of at least one of the analysis node(s).
  • the transmission step may comprise transmitting the second analysis instruction data to the analysis node(s) indicated by the analysis node data of the first analysis instruction data.
  • the first analysis instruction data may comprise the first analysis instructions.
  • a part of the first analysis instructions may be of the type A.
  • a part of the first analysis instructions may be of the type B.
  • the second analysis instruction data may comprise the second analysis instructions.
  • a part of the second analysis instructions may be of the type B.
  • a part of the second analysis instructions may be of the type C.
  • the analysis node data may comprise the indication of the at least one or the plurality of type(s) of the second analysis instructions.
  • the method may comprise an analysis step.
  • the analysis step may comprise operating the analysis node(s) according to the second analysis instruction data.
  • Operating the analysis node(s) according to the second analysis instruction data may comprise executing the second analysis instructions of the second analysis instruction data by the analysis node(s).
  • the second analysis data instructions may each comprise the indication relating to the analysis node(s).
  • the compiler node may receive the first analysis instruction data.
  • the parser may process the first analysis instruction data and generate machine-interpretable first analysis instruction data.
  • the first analysis instruction data and the second analysis instruction data may comprise the processing instruction data.
  • the processing instruction data may indicate the processing of at least one or a plurality of sample(s), such as a pre-processing or pre-treatment of the sample(s), as discussed above.
  • the first analysis instruction data and the second analysis instruction data may comprise the instruction sample type data.
  • the instruction sample type data may indicate the type of the sample(s).
  • the first analysis instruction data and the second analysis instruction data may comprise instruction identification data.
  • the instruction identification data may identify the sample(s).
  • the instruction identification data may relate to an index or identification of the sample(s) or their containers, as discussed above.
  • the compiler node may perform the instruction compilation step.
  • the instruction compilation step may comprise for each of the first analysis instructions, determining the type of the corresponding second analysis instruction.
  • Determining the type of the corresponding second analysis instruction may be based on the analysis node data.
  • the instruction compilation step may comprise generating for some of the first analysis instructions a corresponding second analysis instruction of the respectively determined type.
  • at least one of the first instructions may already be of a same type as the corresponding second analysis instruction and may thus not need generating of the corresponding analysis instruction.
  • the instruction compilation step may comprise for some of the first analysis instructions, searching associations of the some first analysis instructions in the data structure.
  • the instruction compilation step may comprise searching associations of some of the analysis specifiers corresponding to the first analysis structures in the data structure.
  • the instruction compilation step may comprise generating the second analysis instructions for the first analysis instructions for which associations to instructions of the respectively determined type where found based on these associations.
  • the instruction compilation step may comprise searching for associations of first analysis instructions, for which the determined type is different from the actual type of the analysis instruction.
  • the instruction compilation step may comprise searching only for associations of first analysis instructions, for which the determined type is different from the actual type of the analysis instruction.
  • the method may comprise an association generation step.
  • the association generation step may comprise generating at least one or a plurality of association(s) of analysis specifiers.
  • the association generation step may comprise generating the association(s) each associating an analysis specifier to an analysis specifier of another type.
  • the method may comprise performing the association generation step for at least one or a plurality of the first analysis instruction(s).
  • the method may comprise performing the association generation step for at least one or a plurality of the first analysis instruction(s) for which associations to the determined type were not found in the data structure.
  • the association generating step may comprise adding the generated associations to the data structure.
  • the compiler node may perform the association generation step.
  • the analysis node data may indicate for each first analysis instruction an analysis node.
  • the indicated analysis node may execute the second analysis instruction corresponding to the respective first analysis instruction.
  • the analysis step may comprise processing the sample(s) according to the processing instruction data by the analysis node(s).
  • the analysis step may comprise processing the sample(s) based on the instruction sample type data, as discussed above in the context of the system.
  • the analysis step may comprise selecting the sample to be processed and/or analyzed according to an analysis instruction based on the instruction identification data and the identification of the sample(s).
  • Operating the analysis node(s) according to the second analysis instruction data may comprise the at least one control component controlling the plurality of analysis components of the analysis node(s) according to the second analysis instructions.
  • Each analysis node may comprise one or more control nodes.
  • the at least one control component may be a plurality of control nodes.
  • the analysis node(s) may comprise the at least one or the plurality of data- transmission component(s).
  • the analysis node(s) may receive the second analysis instruction data by means of the data-transmission component(s).
  • the analysis step may comprise generating result data, preferably first result data, by the analysis node(s).
  • the at least one control component may generate result data, preferably first result data, based on operations performed by the respective analysis components.
  • the analysis step may comprise transmitting the result data, preferably the first result data, by means of the data-transmission component(s).
  • the analysis step may comprise transmitting the result data, preferably the first result data, to the compiler node.
  • the compiler node may modify the result data.
  • the compiler node may modify the first result data.
  • the compiler node may modify the at least one of the result data and the first result data based on the data structure, so as to be according to a set of analysis specifiers. The compiler node may thus generate the second result data.
  • the compiler node may transmit the result data, preferably the second result data, to at least one or plurality of the result nodes.
  • the instruction nodes may comprise the result nodes.
  • the analysis step may comprise generating at least one bio-medical analysis.
  • the sample(s) may comprise at least one or a plurality of sample(s) originating from the human body, such as samples of a bodily fluid and/or of a human tissue, optionally a discussed above.
  • the analysis instructions my comprise instructions relating to the bio-medical analyses of the sample(s) originating from the human body.
  • the analysis node(s) may perform at least one or a plurality of analyses of the sample(s).
  • the method for associating the analysis specifiers of the different types comprises processing the plurality of sets of analysis specifiers. Each set comprises at least one or a plurality of analysis specifier(s) of a same type, as discussed above. The types may be different or mutually different between the sets.
  • the method further comprises a pre processing step and an association data generation step.
  • the pre-processing step comprises pre-processing the analysis specifiers at least of the first set of analysis specifiers.
  • the association data generation step comprises generating the data indicating associations of the analysis specifiers.
  • the method for associating the specifiers may comprise receiving at least one of the sets of analysis specifiers, such a set of analysis specifiers for which the data structure does not comprise an association needed for generating a second analysis instruction by the above method.
  • the method for associating the specifiers may comprise receiving several of the sets of analysis specifiers.
  • the pre-processing step may further comprise providing the set of pre-processed specifiers of the set of intermediary analysis specifiers.
  • the pre-processing step may also comprise providing a set of pre-processed analysis specifiers at least of a second set of analysis specifiers.
  • the pre-processing step may further comprise pre-processing analysis specifiers at least of the second set of analysis specifiers.
  • Pre-processing specifiers may comprise applying the removing criterion to the specifiers and removing the portion of the specifiers if indicated by the removing criterion.
  • pre-processing a specifier may comprise removing at least the portion of the respective specifier depending on a result of the removing criterion.
  • the removing criterion may indicate removal of at least one type of words.
  • the removing criterion may indicate the removal of stop words.
  • Pre-processing the specifiers may comprise converting at least one segment of each specifier to the pre-determined form.
  • Pre-processing the specifiers may comprise converting the segments to the pre determined grammatical form, as discussed above.
  • Pre-processing the specifiers may comprise converting the segments to at least one of the plural form and the singular form.
  • Pre-processing the specifiers may comprise replacing text segments of the specifier by corresponding text segments.
  • Pre-processing specifiers may comprise replacing text segments by equivalent text segments.
  • the equivalent text segments may be synonyms of the text segments to be replaced.
  • Pre-processing the analysis specifiers may comprise generating the hash-values for the specifiers.
  • Pre-processing the specifiers may also comprise generating the plurality of different hash-values for the specifiers.
  • Pre-processing specifiers may also comprise representing the specifiers as bags of words, as discussed above.
  • the pre-processing component may perform the pre-processing step.
  • Providing the set of pre-processed specifiers of the set of intermediary analysis specifiers may comprise loading and/or receiving a result of pre-processing the intermediary specifiers.
  • Providing the set of pre-processed specifiers of the set of intermediary analysis specifiers may comprise pre-processing the set of the set of intermediary specifiers.
  • the method for associating the specifiers may further comprise a selection step.
  • the selection step may comprise for the pre-processed analysis specifiers of the first set, providing subsets of the intermediary analysis specifiers.
  • Providing the subsets may be selecting the subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers from the first set.
  • the selection component may perform the selection step.
  • Selecting the subsets may comprise for the pre-processed analysis specifiers of the first set, estimating similarity values to at least some of the intermediary specifiers. As discussed above, estimating the similarity values can be stopped for a pre-processed specifier e.g. when a similarity value above a certain threshold is found.
  • Selecting the subsets of the intermediary specifiers may be based on the similarity values.
  • the selection step may comprise selecting the subsets of the intermediary specifiers by a similarity criterion.
  • the similarity criterion may relate to the similarity values.
  • the similarity criterion may for example relate to similarity values above a predetermined threshold, or to a group of n intermediary specifiers with a highest similarity, where n may be a constant number.
  • the method for associating the specifiers may comprise not selecting a subset of the intermediary specifiers for a pre-processed specifier of the first set, if the estimated similarity values for all of the intermediary specifiers for said pre-processed specifier are below a pre-defined threshold.
  • the association data generation step may comprise generating associations of the pre-processed analysis specifiers from the first set and corresponding specifiers selected from at least one of the intermediary analysis specifiers and analysis specifiers from the second set of analysis specifiers.
  • the association data generation step may comprise generating associations of the specifiers from the first set to the intermediary specifiers.
  • the association data generation step may also comprise generating associations of the specifiers from the first set to the specifiers from the second set.
  • the method for associating the specifiers may further comprise adding at least some of the associations to a data structure for storing associations of analysis specifiers of different types.
  • Generating the associations of the pre-processed analysis specifiers of the first set and the corresponding specifiers comprises applying a comparison criterion to the pre-processed specifiers of the first set and specifiers from the intermediary specifiers.
  • the association data generation step may comprise generating for each of the pre-processed analysis specifiers of the first set an association to a respective analysis specifier of the intermediary specifiers for which the comparison criterion provided a result indicating a minimal difference between the analysis specifier and the intermediary specifier.
  • the comparison criterion may be applied to the respective specifier and the intermediary specifiers from the respectively selected subset. Particularly, for each of the pre-processed specifiers of the first set, the comparison criterion may be applied only to the respective specifier and the intermediary specifiers from the respectively selected subset.
  • the pre-processing step may further comprise providing pre-processed analysis specifiers of the second set of analysis specifiers.
  • the pre-processing step may also comprise pre-processing analysis specifiers of the second set of analysis specifiers.
  • the selection step may comprise for the pre-processed analysis specifiers of the second set, providing subsets of the intermediary analysis specifiers.
  • the association data generation step may further comprise applying the comparison criterion to the pre-processed specifiers of the second set and specifiers from the intermediary specifiers and generating associations of at least some or all of the pre- processed analysis specifiers of the second set based thereon.
  • the association data generation step may further comprise generating associations at least some of the pre-processed specifiers of the first set and at least some of the pre-processed specifiers of the second set based on the associations of the pre-processed specifiers of the first set to the intermediary analysis specifiers and the associations of the pre-processed specifiers of the second set to the intermediary analysis specifiers.
  • the association data generation step may comprise generating associations of pre-processed specifiers of the first set and pre-processed specifiers of the second set which specifiers are associated to same intermediary analysis specifiers.
  • the association data generation step may be performed by the association component.
  • the method for associating the specifiers may comprise storing the data structure.
  • the data-storage component may store the data structure.
  • the method for associating the specifiers may comprise sending the data structure to the compiler node.
  • the transmission component may send the data.
  • the data-processing system for associating analysis specifiers may perform the method.
  • the data-processing system for associating analysis specifiers may comprise the pre-processing component.
  • the data-processing system for associating analysis specifiers may comprise the selection component.
  • the data-processing system for associating analysis specifiers may comprise the association component.
  • the data-processing system for associating analysis specifiers may comprise the data-storage component.
  • the data-processing system for associating analysis specifiers may comprise the transmission component.
  • the analysis specifiers may be at least one of analysis instructions and analysis indications, as discussed above.
  • the method may comprise using the data structure for at least one of a pre processor, a compiler or an interpreter for analysis instructions.
  • the method may comprise using the data structure for transforming analysis results from the first set of analysis specifiers to the second set of analysis specifiers.
  • At least some or all of the analysis specifier(s) may relate to the bio-medical analysis or the bio-medical analyses.
  • At least some or all of the analysis specifier(s) may relate to the analysis or the analyses of the at least one or the plurality of sample(s) originating from the human body.
  • At least some or all of the analysis specifier(s) may relate to the bio-medical analysis or the biomedical analyses of the at least one or the plurality of sample(s) originating from the human body.
  • the association generation step of the method disclosed in the third embodiment may comprise performing the method for associating the analysis specifiers of the different types.
  • the compiler node used in the method disclosed in the third embodiment may comprise using the data-processing system for associating analysis specifiers.
  • a computer program product comprises instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method for associating analysis specifiers of different types.
  • a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out at least one of the analysis instruction receiving step, the instruction compilation step and the transmission step of the method disclosed in the third embodiment.
  • a system comprising a compiler node.
  • system further comprises at least one or a plurality of analysis node(s).
  • system further comprises at least one or a plurality of instruction node(s).
  • system further comprises at least one or a plurality of result node(s).
  • each of the analysis node(s) comprise at least one control component and a plurality of analysis components.
  • analysis node data comprise an indication of at least one or a plurality of type(s) of the second analysis instructions.
  • first analysis instruction data and the second analysis instruction data comprise processing instruction data
  • processing instruction data are configured for indicating a processing of at least one or a plurality of sample(s).
  • first analysis instruction data and the second analysis instruction data comprise instruction sample type data, and wherein the instruction sample type data are configured for indicating a type of the sample(s).
  • first analysis instruction data and the second analysis instruction data comprise instruction identification data, and wherein the instruction identification data are configured for identifying the sample(s).
  • system is configured for adding the generated association(s) to the data structure.
  • analysis node(s) are configured for selecting the sample to be processed and/or analysed according to an analysis instruction based on the instruction identification data and an identification of the sample(s).
  • control component of each of the analysis node(s) is configured for controlling the plurality of analysis components (44a, 44b) of the respective analysis node(s) (34, 34') according to the second analysis instructions.
  • control component is configured for generating the result data (20), preferably the first result data (20a), based on operations performed by the analysis components (44a, 44b).
  • sample(s) comprise at least one or a plurality of sample(s) originating from the human body, such as samples of a bodily fluid and/or of a human tissue.
  • analysis instructions comprise instructions relating to bio-medical analyses of the sample(s) originating from the human body.
  • a data-processing system which data processing system comprises a transmission component and a data-storage component. D2. The data-processing system according to the preceding embodiment, wherein the data-processing system further comprises a pre-processing component.
  • D5. The data-processing system according to any of the preceding data-processing system embodiments, wherein the data-processing system is configured for processing a plurality of sets of analysis specifiers, and wherein each set comprises at least one or a plurality of analysis specifier(s) of a same type, and wherein the plurality of sets of analysis specifiers comprises at least a first set of analysis specifiers.
  • D6 The data-processing system according to any of the preceding data-processing system embodiments with the features of D2, wherein the pre-processing component is configured for pre-processing analysis specifiers at least of the first set of analysis specifiers.
  • D8 The data-processing system according to any of the preceding data-processing system embodiments, wherein the data-processing system is configured for receiving at least one of the sets of analysis specifiers.
  • D9 The data-processing system according to any of the preceding data-processing system embodiments, wherein the method comprises receiving several of the plurality of sets of analysis specifiers.
  • DIO DIO.
  • the data-processing system according to any of the preceding data-processing system embodiments, wherein the data-processing system is configured for providing a set of pre-processed specifiers of a set of intermediary analysis specifiers.
  • Dll The data-processing system according to any of the preceding data-processing system embodiments with the features of D2, wherein the pre-processing component is further configured for generating the set of pre-processed specifiers of the set of intermediary analysis specifiers.
  • D12 The data-processing system according to any of the preceding data-processing system embodiments with the features of D2 and D5, wherein the pre-processing component is configured for pre-processing analysis specifiers at least of a second set of analysis specifiers.
  • D13 The data-processing system according to any of the preceding data-processing system embodiments and with the features of D2, wherein the pre-processing component is configured for pre-processing specifiers at least by applying a removing criterion to each of the specifiers and removing at least one portion of the respective specifier if indicated by the removing criterion.
  • D16 The data-processing system according to any of the preceding data-processing system embodiments and with the features of D2, wherein the pre-processing component is configured for pre-processing specifiers at least by converting at least one segment of each specifier to a pre-determined form.
  • D22 The data-processing system according to any of the preceding data-processing system embodiments and with the features of D2, wherein the pre-processing component is configured for pre-processing specifiers at least by generating a hash-value for the specifiers.
  • D23 The data-processing system according to the preceding embodiment, wherein the pre-processing component is configured for pre-processing specifiers at least by generating a plurality of different hash-values for each specifier D15a.
  • the pre-processing component is configured for pre-processing specifiers at least by representing the specifiers as bags of words.
  • D24 The data-processing system according to any of the preceding data-processing system embodiments, wherein the data-processing system is configured for providing the set of pre-processed specifiers of the set of intermediary analysis specifiers by loading and/or receiving said set.
  • D25 The data-processing system according to any of the preceding data-processing system embodiments, wherein the data-processing system is configured for providing subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers of the first set.
  • D26 The data-processing system according to the preceding embodiment, wherein providing the subsets is selecting the subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers from the first set.
  • D27 The data-processing system according to the preceding embodiment and with the features of D3, wherein the selection component is configured for selecting the subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers from the first set.
  • D28 The data-processing system according to any of the preceding data-processing system embodiments with the features of D26, wherein the data-processing system and/or the selection component is configured for estimating similarity values of the pre- processed analysis specifiers of the first set to at least some of the intermediary specifiers.
  • D29 The data-processing system according to the preceding embodiment with the features of at least one of at least one of D26 and D27, wherein the data-processing system and/or the selection component is configured for selecting the subsets of the intermediary specifiers is based on the similarity values.
  • D30 The data-processing system according to the preceding embodiment, wherein the data-processing system and/or the selection component is configured for selecting the subsets of the intermediary specifiers by a similarity criterion, wherein the similarity criterion relates to the similarity values.
  • D31 The data-processing system according to any of the preceding data-processing system embodiments and with the features of D28, wherein the data-processing system and/or the selection component is configured for not selecting a subset of the intermediary specifiers for a pre-processed specifier of the first set, if the estimated similarity values for all of the intermediary specifiers for said pre-processed specifier are below a pre-defined threshold.
  • D32 The data-processing system according to any of the preceding data-processing system embodiments with the features of D4, wherein the association component is configured for generating associations of the pre-processed analysis specifiers from the first set and corresponding specifiers selected from at least one of the intermediary analysis specifiers and analysis specifiers from the second set of analysis specifiers.
  • D33 The data-processing system according to the preceding embodiment, wherein the data-processing system and/or the association component is configured for adding at least some of the associations to a data structure for storing associations of analysis specifiers of different types.
  • D34 The data-processing system according to the preceding embodiment, wherein the data-storage component is configured for storing the data structure for storing associations of analysis specifiers of different types.
  • D35 The data-processing system according to any of the two preceding embodiments, wherein the transmission component is configured for transmitting the at least some of the associations to a data-storage system, which data-storage system is configured for storing the data structure for storing the associations of analysis specifiers of different types.
  • D36 The data-processing system according to any of the preceding data-processing system embodiments with the features of D32, wherein the association component is configured for applying a comparison criterion to the pre-processed specifiers of the first set and specifiers from the intermediary specifiers.
  • association component is configured for generating for each of the pre-processed analysis specifiers of the first set an association to a respective analysis specifier of the intermediary specifiers for which the comparison criterion provided a result indicating a minimal difference between the analysis specifier and the intermediary specifier.
  • D38 The data-processing system according to any of the preceding data-processing system embodiments with the features of D25 and D36, wherein the association component is configured for applying the comparison criterion for the pre-processed specifiers of the first set to the respective pre-processed specifier and the intermediary specifiers from the respectively selected subset.
  • D40 The data-processing system according to any of the preceding data-processing system embodiments with the features of D25 and D3, wherein the selection component is furthermore configured for providing subsets of the intermediary analysis specifiers for the pre-processed analysis specifiers of the second set.
  • association component is further configured for applying the comparison criterion to the pre-processed specifiers of the second set and specifiers from the intermediary specifiers and for generating associations of pre- processed analysis specifiers of the second set based thereon.
  • association component is further configured for generating associations of pre-processed specifiers of the first set and pre-processed specifiers of the second set based on the associations of the pre-processed specifiers of the first set to the intermediary analysis specifiers and the associations of the pre-processed specifiers of the second set to the intermediary analysis specifiers.
  • association component is configured for generating associations of pre- processed specifiers of the first set and pre-processed specifiers of the second set which specifiers are associated to same intermediary analysis specifiers.
  • D44 The data-processing system according to any of the preceding data-processing system embodiments with the features of D33, wherein the data-processing system and/or the transmission component is configured for sending the data structure to a compiler node.
  • D45 The data-processing system according to any of the preceding data-processing system embodiments with the features of D5, wherein the analysis specifiers are at least one of analysis instructions and analysis indications.
  • D46 The data-processing system according to any of the preceding data-processing system embodiments with the features of D33, wherein the data-processing system is configured for using the data structure for transforming analysis results from the first set of analysis specifiers to the second set of analysis specifiers.
  • D47 The data-processing system according to any of the preceding data-processing system embodiments with the features of D5, wherein at least some or all of the analysis specifier(s) relate to a bio-medical analysis/es.
  • D48 The data-processing system according to any of the preceding data-processing system embodiments with the features of D5, wherein at least some or all of the analysis specifier(s) relate to analysis/es of at least one or a plurality of sample(s) originating from the human body.
  • D49 The data-processing system according to any of the preceding data-processing system embodiments with the features of D5, wherein at least some or all of the analysis specifier(s) relate to a bio-medical analysis/es of at least one or a plurality of sample(s) originating from the human body.
  • D50 The data-processing system according to any of the preceding data-processing system embodiments, wherein the data-processing system is a data-processing system configured for associating analysis specifiers.
  • compiler node comprises the data-processing system according to any of the preceding data- processing system embodiments.
  • an analysis instruction receiving step comprising receiving first analysis instruction data from at least one or a plurality of instruction node(s),
  • an instruction compilation step comprising generating second analysis instruction data and accessing a data structure for storing analysis specifiers of different types
  • the first analysis instruction data comprise analysis node data, which analysis node data comprise an indication of at least one of the analysis node(s).
  • the transmission step comprises transmitting the second analysis instruction data to the analysis node(s) indicated by the analysis node data of the first analysis instruction data.
  • the first analysis instruction data comprise first analysis instructions, and wherein a part of the first analysis instructions is of a type A.
  • analysis node data comprise an indication of at least one or a plurality of type(s) of the second analysis instructions.
  • operating the analysis node(s) according to the second analysis instruction data comprises executing the second analysis instructions of the second analysis instruction data by the analysis node(s).
  • first analysis instruction data and the second analysis instruction data comprise processing instruction data
  • processing instruction data indicate a processing of at least one or a plurality of sample(s).
  • M24 The method according to any of the preceding embodiments, wherein the method comprises an association generation step, wherein the association generation step comprises generating at least one or a plurality of association(s) of analysis specifiers.
  • M25 The method according to any of the preceding method embodiments and with the features of M24 and at least one of M21 and M22, wherein the method comprises performing the association generation step for at least one or a plurality of the first analysis instruction(s).
  • association generating step comprises adding the generated association(s) to the data structure.
  • analysis step comprises selecting the sample to be processed and/or analysed according to an analysis instruction based on the instruction identification data and an identification of the sample(s).
  • operating the analysis node(s) according to the second analysis instruction data comprises at least one control component (42) controlling a plurality of analysis components (44a, 44b) of the analysis node(s) (34, 34') according to the second analysis instructions.
  • analysis node(s) comprise(s) at least one or a plurality of data-transmission component(s) (40) and wherein the analysis node(s) receive the second analysis instruction data by means of the data-transmission component(s).
  • control component generates the result data (20), preferably the first result data (20a) based on operations performed by the analysis components (44a, 44b).
  • the analysis step comprises transmitting the result data (20), preferably the first result data (20a), by means of the data-transmission component(s).
  • sample(s) comprise at least one or a plurality of sample(s) originating from the human body, such as samples of a bodily fluid and/or of a human tissue.
  • association method embodiments are discussed. These embodiments are abbreviated by the letter “A” followed by a number. Whenever reference is herein made to the “association method embodiments”, these embodiments are meant.
  • a pre-processing step comprising pre-processing analysis specifiers at least of a first set of analysis specifiers
  • A2 The method according to the preceding embodiment, wherein the method comprises receiving at least one of the sets of analysis specifiers.
  • A3 The method according to any of the two preceding embodiments, wherein the method comprises receiving several of the sets of analysis specifiers.
  • pre-processing step further comprises providing a set of pre-processed specifiers of a set of intermediary analysis specifiers.
  • pre-processing step further comprises providing a set of pre-processed analysis specifiers at least of a second set of analysis specifiers.
  • pre-processing specifiers comprises applying a removing criterion to the specifiers and removing a portion of the specifiers if indicated by the removing criterion.
  • pre-processing specifiers comprises converting at least one segment of each specifier to a pre-determined form.
  • pre-processing the specifiers comprises converting the segments to a pre-determined grammatical form.
  • pre-processing the specifiers comprises converting the segments to at least one of a plural form and a singular form.
  • pre-processing specifiers comprises replacing text segments of the specifier by corresponding text segments.
  • pre-processing specifiers comprises replacing text segments by equivalent text segments.
  • pre-processing specifiers comprises generating hash-values for the specifiers.
  • pre-processing specifiers comprises generating a plurality of different hash-values for each specifier.
  • pre-processing specifiers comprises representing the specifiers as bags of words.
  • the selection step comprises for the pre-processed analysis specifiers of the first set, providing subsets of the intermediary analysis specifiers.
  • providing the subsets is selecting the subsets of the intermediary analysis specifiers for the pre- processed analysis specifiers from the first set.
  • A24 The method according to any of the three preceding embodiments, wherein a selection component performs the selection step.
  • selecting the subsets comprises for the pre-processed analysis specifiers of the first set, estimating similarity values to at least some of the intermediary specifiers.
  • the selection step comprises selecting the subsets of the intermediary specifiers by a similarity criterion, which similarity criterion relates to the similarity values.
  • A28 The method according to any of the preceding association method embodiments with the features of A25, wherein the method comprises not selecting a subset of the intermediary specifiers for a pre-processed specifier of the first set, if the estimated similarity values for all of the intermediary specifiers for said pre-processed specifier are below a pre-defined threshold.
  • association data generation step comprises generating associations of the pre-processed analysis specifiers from the first set and corresponding specifiers selected from at least one of the intermediary analysis specifiers and analysis specifiers from the second set of analysis specifiers.
  • A30 The method according to the preceding embodiment, wherein the method further comprises adding at least some of the associations to a data structure for storing associations of analysis specifiers of different types.
  • association data generation step comprises generating for each of the pre-processed analysis specifiers of the first set an association to a respective analysis specifier of the intermediary specifiers for which the comparison criterion provided a result indicating a minimal difference between the analysis specifier and the intermediary specifier.
  • A33 The method according to any of the preceding association method embodiments with the features of A22 and A31, wherein for each of the pre-processed specifiers of the first set, the comparison criterion is applied to the respective specifier and the intermediary specifiers from the respectively selected subset.
  • pre-processing step further comprises pre-processing analysis specifiers of the second set of analysis specifiers.
  • A35 The method according to any of the preceding association method embodiments with the features of A22, wherein the selection step comprises for the pre-processed analysis specifiers of the second set, providing subsets of the intermediary analysis specifiers.
  • association data generation step further comprises applying the comparison criterion to the pre-processed specifiers of the second set and specifiers from the intermediary specifiers and generating associations of pre-processed analysis specifiers of the second set based thereon.
  • association data generation step further generating associations of pre-processed specifiers of the first set and pre-processed specifiers of the second set based on the associations of the pre- processed specifiers of the first set to the intermediary analysis specifiers and the associations of the pre-processed specifiers of the second set to the intermediary analysis specifiers.
  • association data generation step comprises generating associations of pre-processed specifiers of the first set and pre-processed specifiers of the second set which specifiers are associated to same intermediary analysis specifiers.
  • association data generation step is performed by an association component.
  • A40 The method according to any of the preceding association method embodiments with the features of A30, wherein the method comprises storing the data structure.
  • A41 The method according to the preceding embodiment, wherein a data-storage component stores the data structure.
  • A42 The method according to any of the preceding association method embodiments with the features of A30, wherein the method comprises sending the data structure to a compiler node.
  • A43 The method according to the preceding embodiment, wherein a transmission component sends the data.
  • A45 The method according to any of the preceding association method embodiments with the features of A18, wherein the data-processing system for associating analysis specifiers comprises the pre-processing component.
  • A46 The method according to any of the preceding association method embodiments with the features of A24, wherein the data-processing system for associating analysis specifiers comprises the selection component.
  • A48 The method according to any of the preceding association method embodiments with the features of A41, wherein the data-processing system for associating analysis specifiers comprises the data-storage component.
  • A49 The method according to any of the preceding association method embodiments with the features of A43, wherein the data-processing system for associating analysis specifiers comprises the transmission component.
  • analysis specifiers are at least one of analysis instructions and analysis indications.
  • A51 The method according to any of the preceding association method embodiments and with the features of A30, wherein the method comprises using the data structure for at least one of a pre-processor, a compiler or an interpreter for analysis instructions.
  • A52 The method according to any of the preceding association method embodiments and with the features of A30, wherein the method comprises using the data structure for transforming analysis results from the first set of analysis specifiers to the second set of analysis specifiers.
  • A53 The method according to any of the preceding association method embodiments, wherein at least some or all of the analysis specifier(s) relate to a bio-medical analysis/es.
  • A54 The method according to any of the preceding association method embodiments, wherein at least some or all of the analysis specifier(s) relate to analysis/es of at least one or a plurality of sample(s) originating from the human body.
  • A55 The method according to any of the preceding association method embodiments, wherein at least some or all of the analysis specifier(s) relate to a bio-medical analysis/es of at least one or a plurality of sample(s) originating from the human body.
  • association generation step comprises performing the analysis specifier association method according to any of the association method embodiments.
  • a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method for associating analysis specifiers of different types according to any of the association method embodiments.
  • a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out at least one of the analysis instruction receiving step, the instruction compilation step and the transmission step of the method according to any of the method embodiments.
  • Fig. 1 shows an embodiment of the system with details of an analysis node
  • FIG. 2 shows prior art
  • FIGs. 3 & 4 show further embodiments of the system
  • Fig. 5 shows an embodiment of result data
  • Fig. 6 shows an embodiment of analysis instruction data
  • Fig. 7 shows sets of analysis specifiers and intermediary analysis specifiers
  • Fig. 8 shows an embodiment of a data-processing system for associating analysis specifiers
  • Figure 1 shows a system comprising an instruction node 32, a result node 36, a compiler node 30 and an analysis node 34.
  • the analysis node 34 may for example comprise laboratory equipment. More particularly, the analysis node 34 may comprise a laboratory for bio-medical analyses and a control component 42.
  • the control component may comprise a LIS, i.e. a lab information system, storing and processing data relating to performed analyses, analysis results, samples and/or operation of the analysis equipment.
  • the control component 42 may comprise a data-processing system.
  • the data processing system of the control component 42 may comprise one or more processing units configured to carry out computer instructions of a program (i.e. machine readable and executable instructions).
  • the processing unit(s) can be singular or plural.
  • the data processing system of the control component 42 may comprise at least one of CPU, GPU, DSP, APU, ASIC, ASIP or FPGA.
  • the data processing system of the control component 42 may comprise memory components, such as, main memory (e.g. RAM), cache memory (e.g. SRAM) and/or secondary memory (e.g. HDD, SDD).
  • the data processing system may comprise volatile and/or non-volatile memory such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM.
  • the data processing system of the control component 42 may comprise internal communication interfaces (e.g. busses) configured to facilitate electronic data exchange between components of the data processing system, such as, the communication between the memory components and the processing components.
  • the control component 42 may comprise a data- transmission component 40, which may provide external communication interfaces configured to facilitate electronic data exchange between the control component 42 and devices or networks external to the control component 42, particularly the analysis node 34.
  • the data-transmission component 40 can comprise network interface card(s) that can be configured to connect the data processing system to a network, such as, to the Internet.
  • the data-transmission component 42 can be configured to transfer electronic data using a standardized communication protocol.
  • the data processing system of the control component 42 may be a centralized or distributed computing system.
  • the data processing system can comprise user interfaces, such as:
  • output user interface such as: o screens or monitors configured to display visual data (e.g. instructions for performing certain actions on a device, e.g. clear an error of an automated analysis device), o speakers configured to communicate audio data (e.g. a warning sound in case of technical problems or a required user interaction, such as a sample change),
  • visual data e.g. instructions for performing certain actions on a device, e.g. clear an error of an automated analysis device
  • o speakers configured to communicate audio data (e.g. a warning sound in case of technical problems or a required user interaction, such as a sample change)
  • input user interface such as: o a keyboard configured to allow the insertion of text and/or other keyboard commands (e.g. allowing the user to enter text data and/or other keyboard commands by having the user type on the keyboard) and/or o trackpad, mouse, touchscreen, joystick, e.g. to navigate through an interface of the laboratory information system.
  • keyboard commands e.g. allowing the user to enter text data and/or other keyboard commands by having the user type on the keyboard
  • trackpad mouse, touchscreen, joystick, e.g. to navigate through an interface of the laboratory information system.
  • the data processing system of the control component 42 can be a processing unit configured to carry out instructions of a program.
  • the data processing system of the control component 42 can be a system-on-chip comprising processing units, memory components and busses.
  • the data processing system can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer.
  • the data processing system can be a server, a server system, a portion of a cloud computing system or a system emulating a server, such as a server system with an appropriate software for running a virtual machine.
  • the data processing system of the control component 42 can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or a user interface (such as the upper-mentioned user interfaces).
  • the compiler node 30 may comprise one or more processing units configured to carry out computer instructions of a program (i.e. machine readable and executable instructions).
  • the processing unit(s) can be singular or plural.
  • the compiler node 30 may comprise at least one of CPU, GPU, DSP, APU, ASIC, ASIP or FPGA.
  • the compiler node 30 can comprise memory components, such as, main memory (e.g. RAM), cache memory (e.g. SRAM) and/or secondary memory (e.g. HDD, SDD).
  • the compiler node 30 may comprise volatile and/or non-volatile memory such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM.
  • the compiler node 30 may comprise internal communication interfaces (e.g.
  • the compiler node 30 can comprise external communication interfaces configured to facilitate electronic data exchange between the data processing system and devices or networks external to the data processing system.
  • the compiler node 30 can comprise network interface card(s) that can be configured to connect the data processing system to a network, such as, to the Internet.
  • the compiler node 30 can be configured to transfer electronic data using a standardized communication protocol.
  • the data processing system may be a centralized or distributed computing system.
  • the compiler node 30 can be a processing unit configured to carry out instructions of a program.
  • the compiler node 30 can be a system-on-chip comprising processing units, memory components and busses.
  • the compiler node 30 can be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer.
  • the compiler node 30 can be a server, a server system, a portion of a cloud computing system or a system emulating a server, such as a server system with an appropriate software for running a virtual machine.
  • the compiler node 30 can be a processing unit or a system-on-chip that can be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or user interface (such as the upper- mentioned user interfaces).
  • the instruction node 32 may be an entity generating instructions for analysis of a sample.
  • the instruction node 32 may be a facility where a sample of a user is generated, such as a health care provider.
  • the instruction node 32 may use a data- processing system as discussed above in the context of the analysis node 34, or another data-processing system.
  • the result node 36 may be an entity receiving results of analyses.
  • the result node 36 may be a facility where a user is medically treated, such as a health care provider.
  • the result node 36 may comprise a data-processing system as discussed above in the context of the analysis node 34, or another data-processing system.
  • the instruction node 32 and the result node 36 may be identical, e.g. if a health care provider takes a sample from a user, sends it for laboratory analysis and receives results.
  • the instruction node 32 and the result node 36 may not be identical, e.g. if two different health care providers are involved, such as a facility for laboratory medicine taking a sample a instructing analyses. However, in such cases, the results may be forwarded to the result node 36.
  • the instruction node 32 may generate analysis instruction data 10, particularly first analysis instruction data 10b.
  • the compiler node 30 may then compile the first analysis instruction data 10a so as to generate second analysis instruction data 10b.
  • the compiler node 30 may transmit the analysis instruction data 10, particularly the second analysis instruction data 10b, to the analysis node 34 or a plurality of analysis nodes (not shown).
  • the analysis node 34 may then perform at least one or a plurality of analyses based on the instruction data 10, particularly the second instruction data 10b.
  • the analysis node 34 may be configured for processing instruction data of a second type, such as the second instruction data 10b, but not instruction data of a first type, such as the first instruction data 10a.
  • the analysis node may further generate result data 20, particularly result data of a first type 20a.
  • the result data may relate to results of analyses performed by the analysis node 34.
  • the compiler node 30 may be configured to generate result data of a second type 20b and forward the result data to the result node 36.
  • a main difference between instruction data 10 as well as result data 20 of the first and the second type may be analysis specifiers specifying a type of an analysis that is to be performed (in case of the instruction data 10) or that was performed (in case of the result data 20).
  • An analysis specifier of the first type may specify a same analysis as an analysis specifier of the second type, which analysis specifier of the second type may however be different from the analysis specifier of the first type.
  • the compiler node 30 may transform the instruction data 10 and/or the result data 20 from one type to another.
  • Figure 2 shows prior art, where the analysis node receives and processes only instruction data of the first type 10a and wherein the analysis node 34 only generates result data of the first type 20a. Without the compiler node 30, it is thus necessary that the instruction node 32 and the result node 36 process instruction data 10 and/or result data 20 of a same type as the analysis node 34. [303] Thus, it becomes obvious that optionally advantageously, the compiler node 34 and the related method may thus may increase an interoperability of analysis nodes 34, instruction nodes 32 and result nodes 36.
  • Figure 3 shows another example of the system and the method, wherein a plurality of instruction nodes (32, 32', 32", 32"', 32"") sends instruction data to the compiler node 30.
  • some of the instruction data 10 sent to the compiler node 30 may be first instruction data 10a, others may be second instruction data 10b. Still others may be instruction data 10 of still another type.
  • the compiler node 30 may then generate modified instruction data and send parts of the modified instruction data to a first and a second analysis node 34, 34'.
  • a first analysis node 34 may process first instruction data 10a
  • a second analysis node 34' may process second instruction data 10b.
  • the compiler node 30 may transform the portions of the instruction data 10 that are to be processed by the first analysis node 34 to the first type so as to be first instruction data 10a, and the portions of the instruction data 10 that are to be processed by the second analysis node 34' to the second type so as to be second instruction data 10b.
  • the analysis nodes 34, 34' may generate corresponding result data 20.
  • the result data 20 may be transmitted to the result nodes 36, 36', 36", 36'", 36"".
  • the compiler node 30 may generate modified result data 20.
  • an interoperability of the analysis nodes 34, 34' and the result nodes 36, 36', 36", 36'", 36"" may be increased.
  • an interoperability of the instruction nodes 32, 32', 32", 32'", 32"" and the result nodes 36, 36', 36", 36"', 36"" may be increased.
  • Fig. 3 and Fig. 4 may also illustrate that the result data 20 may be modified by the compiler node 30.
  • the compiler node 30 may either send the modified result data 20 to the analysis nodes 34, 34’, which may transmit the result data 20 to the result nodes 36, 36’, 36", 36’", 36"".
  • an identity, an address or another feature allowing to identify the result nodes does not need to be available to the compiler node.
  • the compiler node 30 may forward the modified data to the result nodes, which may optionally advantageously avoid routing the result data again by the analysis nodes.
  • Fig. 5 shows an example of the result data 20, particularly first and second result data 20a, 20b.
  • the first result 20a data may be of a first type.
  • the second result data 20b may be of a second type.
  • the first and the second result data may each comprise result identification data 22, 22a, 22b.
  • the result identification data 22, 22a, 22b may identify the analysis result, e.g. by means of an indication of a sample to which the result relates, by means of an indication of a user to which the result relates or by another identification.
  • the result data 20 may comprise analysis type data 24, 24a, 24b, which may specify an analysis that was performed.
  • the result data 20 may also comprise sample processing data 28, 28a, 28b, which may indicate for example a pre-treatment or pre-processing of the sample.
  • the analysis type and/or the sample processing of an analysis may be indicated by an analysis specifier.
  • the analysis specifier may be proprietary.
  • the analysis specifier may be a standard specifier, for example a specifier according to the LOINC-standard.
  • Fig. 6 shows analysis instruction data 10, particularly first analysis instruction data 10a and second analysis instruction data 10b.
  • the analysis instruction data may comprise instruction identification data 12, 12a, 12b.
  • the instruction identification data may be data identifying a sample to which an instruction refers.
  • the analysis instruction data 10, 10a, 10b may comprise instruction sample type data 14, 14a, 14b relating to a type of a sample to be processed, e.g. a blood sample, a saliva sample, a stool sample, a sample of a particular tissue such as liver tissue, or the like.
  • the analysis instruction data 10, 10a, 10b may also comprise processing instruction data 16, 16a, 16b, which may indicate the pre-treatment or pre-processing of the sample. Furthermore, the analysis instruction data 10, 10a, 10b may comprise analysis instructions 18, 18a, 18b, which may specify an analysis to be performed.
  • At least one of the analysis instructions 18, 18a, 18b, the processing instruction data 16, 16a, 16b and the sample type data 14, 14a, 14b may be indicated by the analysis specifiers for each analysis to be performed.
  • FIG. 7 shows sets of different analysis specifiers.
  • a method for generating associations of the different analysis specifiers may associate specifiers from a first set of analysis specifiers 61 to intermediary analysis specifiers 62.
  • the intermediary analysis specifiers 62 may for example be according to the LOINC standard.
  • the method may further comprise associating specifiers from a second set of analysis specifiers 63 to the intermediary specifiers 62.
  • the method may comprise associating specifiers from further sets to the intermediary specifiers, such as specifiers from a third set of analysis specifiers 64.
  • the method may further comprise generating associations of analysis specifiers of different sets 61, 63, 64 by the intermediary specifiers to which they are associated.
  • the method may comprise associating analysis specifiers of different types which analysis specifiers are associated to same intermediary specifiers.
  • the associations may be stored in a data structure, such as a data base, a dictionary data type, or in a data structure used to implement symbol tables in compilers.
  • Fig. 8 shows an embodiment of a data-processing system for associating analysis specifiers 50.
  • the data-processing system for association analysis specifiers 50 may comprise one or more processing units configured to carry out computer instructions of a program (i.e. machine readable and executable instructions).
  • the processing unit(s) may be singular or plural.
  • the data-processing system may comprise at least one of CPU, GPU, DSP, APU, ASIC, ASIP or FPGA.
  • the data-processing system for association analysis specifiers 50 may comprise a data-storage component 58, comprising memory components, such as, main memory (e.g. RAM), cache memory (e.g. SRAM) and/or secondary memory (e.g. HDD, SDD).
  • main memory e.g. RAM
  • cache memory e.g. SRAM
  • secondary memory e.g. HDD, SDD
  • the data-processing system for association analysis specifiers 50 may comprise volatile and/or non-volatile memory such an SDRAM, DRAM, SRAM, Flash Memory, MRAM, F-RAM, or P-RAM.
  • the data-processing system for association analysis specifiers 50 may comprise internal communication interfaces (e.g. busses) configured to facilitate electronic data exchange between components of the data processing system, such as, the communication between the memory components and the processing components.
  • the data processing system 50 may comprise a transmission component 59.
  • the transmission component 59 may comprise external communication interfaces configured to facilitate electronic data exchange between the data-processing system for association analysis specifiers 50 and devices or networks external to the data processing system 50.
  • the transmission component 59 may comprise network interface card(s) that may be configured to connect the data processing system 50 to a network, such as, to the Internet.
  • the data-processing system for association analysis specifiers 50 can be configured to transfer electronic data using a standardized communication protocol.
  • the data processing system may be a centralized or distributed computing system.
  • the data-processing system for association analysis specifiers 50 may be a processing unit configured to carry out instructions of a program.
  • the data- processing system for association analysis specifiers 50 may be a system-on-chip comprising processing units, memory components and busses.
  • the data processing system 50 may be a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer.
  • the data processing system may be a server, a server system, a portion of a cloud computing system or a system emulating a server, such as a server system with an appropriate software for running a virtual machine.
  • the data processing system may be a processing unit or a system-on-chip that may be interfaced with a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or user interface (such as the upper-mentioned user interfaces).
  • the data-processing system for association analysis specifiers 50 may comprise a pre-processing component 52.
  • the pre-processing component 52 may be configured for pre-processing analysis specifiers and/or for performing a pre-processing step.
  • the data-processing system for association analysis specifiers 50 may further comprise a selection component 54.
  • the selection component 54 may be configured for selecting subsets of the intermediary analysis specifiers and/or for performing a selection step.
  • the data-processing system for association analysis specifiers 50 may comprise an association component 56.
  • the association component 56 may be configured for associating analysis specifiers of different types and/or for performing an association data generation step.
  • the data-storage component 58 may be configured for storing a result of the association data generation step, such as associations of the analysis specifiers of different types.
  • the data-processing system for association analysis specifiers 50 may comprise at least one storage device, such as the data storage component 58, wherein at least one of the pre-processing component 52, the selection component 54 and the association component 56 may be stored.
  • At least one of pre-processing component 52, the selection component 54 and the association component 56 may be implemented in software. Thus, at least one of these components may be a software component, or at least a portion of one or more software components.
  • the data-processing system for association analysis specifiers 50 may be configured for running said software component, and/or for running a software comprising this software component.
  • at least one of pre-processing component 52, the selection component 54 and the association component 56 may comprise one or more computer instructions (i.e. machine readable instructions) which may be executed by a computer (e.g. the data-processing system for association analysis specifiers 50).
  • At least one of the pre-processing component 52, the selection component 54 and the association component 56 may be stored on one or more different storage devices.
  • at least one of the components may be stored on a plurality of storage components comprising persistent memory, for example a plurality of storage devices in a RAID-system, or different types of memory, such as persistent memory (e.g. HDD, SDD, flash memory) and main memory (e.g. RAM).
  • persistent memory e.g. HDD, SDD, flash memory
  • main memory e.g. RAM
  • At least one of the pre-processing component 52, the selection component 54 and the association component 56 may also be implemented at least partially in hardware.
  • at least one of the components, or at least a portion of the at least one component may be implemented as a programmed and/or customized processing unit, hardware accelerator, or a system-on-chip that may be interfaced with the data-processing system for association analysis specifiers 50, a personal computer, a laptop, a pocket computer, a smartphone, a tablet computer and/or a server.
  • At least one of the pre-processing component 52, the selection component 54 and the association component 56 may also comprise elements implemented in hardware and elements implemented in software.
  • An example may be a use of a hardware- implemented encryption/decryption unit and a software implemented processing of the decrypted data.
  • At least one of the pre-processing component 52, the selection component 54 and the association component 56 may comprise elements specific to the data- processing system for association analysis specifiers 50, for example relating to an operating system, other components of the data-processing system for association analysis specifiers 50, or the analysis nodes and/or the compiler node, to which the data- processing system for association analysis specifiers 50 may be connected.
  • step (A) precedes step (B) this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A).
  • step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z).
  • step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Yl), ..., followed by step (Z).
  • 36, 36', 36", 36'", 36"" result node data-transmission component control component a, 44b analysis component data-processing system for associating analysis specifiers pre-processing component selection component association component data-storage component transmission component first set of analysis specifiers intermediary analysis specifiers second set of analysis specifiers third set of analysis specifiers

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  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

La présente invention concerne un système comprenant un nœud de compilateur. Le nœud de compilateur est configuré pour recevoir des premières données d'instruction d'analyse à partir d'un ou plusieurs nœuds d'instruction, pour générer des secondes données d'instruction d'analyse et pour accéder à une structure de données pour stocker des spécificateurs d'analyse de différents types. Les premières données d'instruction d'analyse comprennent des premières instructions d'analyse. Une partie des premières instructions d'analyse est d'un type A. Les secondes données d'instruction d'analyse comprennent des secondes instructions d'analyse. Une partie des secondes instructions d'analyse est d'un type B. Le nœud de compilateur est en outre configuré pour déterminer, pour chacune des premières instructions d'analyse, un type d'une seconde instruction d'analyse correspondante et pour rechercher des associations de premières instructions d'analyse dans la structure de données, pour lesquelles le type déterminé est différent du type réel de l'instruction d'analyse. Le nœud de compilateur est également configuré pour générer au moins une ou une pluralité d'association(s) de spécificateurs d'analyse pour au moins une ou une pluralité de la ou des premières instructions d'analyse, pour lesquelles des associations au type déterminé n'ont pas été trouvées dans la structure de données. De plus, le nœud de compilateur est configuré pour générer, pour certaines des premières instructions d'analyse, une seconde instruction d'analyse correspondante du type respectivement déterminé. La présente invention concerne également des procédés et des produits programmes d'ordinateur correspondants.
PCT/EP2021/052014 2020-01-30 2021-01-28 Compilateur pour données d'analyse WO2021152030A1 (fr)

Applications Claiming Priority (12)

Application Number Priority Date Filing Date Title
EP20154563 2020-01-30
EP20154563.9 2020-01-30
EP20154565 2020-01-30
EP20154565.4 2020-01-30
EP20172153.7 2020-04-29
EP20172153 2020-04-29
EP20182720.1 2020-06-26
EP20182720.1A EP3929929A1 (fr) 2020-06-26 2020-06-26 Système et procédé d'analyse d'échantillon
EP20182885.2 2020-06-29
EP20182885 2020-06-29
EP20197902 2020-09-23
EP20197902.8 2020-09-23

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WO2021152030A1 true WO2021152030A1 (fr) 2021-08-05

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PCT/EP2021/052005 WO2021152022A1 (fr) 2020-01-30 2021-01-28 Système et procédé de traitement de données de mesure
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