CN114496143A - Method and device for associating clinical data with literature, electronic equipment and storage medium - Google Patents
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Abstract
The disclosure provides a correlation method of clinical data and literature, comprising obtaining structured literature data, extracting disease treatment entity relationship pairs and control treatment entity relationship pairs of the same disease in the literature data; recalling the matched first set of clinical medical record data from a clinical medical record database using the disease treatment entity relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index; and performing exclusive OR operation on the first medical record data set and the second medical record data set to obtain clinical medical record data matched with literature data. The disclosure also provides a device for associating clinical data with literature, an electronic device and a storage medium.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and an apparatus for associating clinical data with documents, an electronic device, and a storage medium.
Background
Clinical data in hospital informatization systems typically exists in the form of long texts, such as test and examination reports, current and past medical histories, courses of treatment, discharge nodules, and the like. The clinical data contains a large amount of medical information, and the clinical data is structured, so that the clinical data analysis and the medical big data processing can be performed.
The medical entities obtained by the structured clinical data are scattered patient single-time visit information, the clinical data with the same characteristics are gathered and then are associated with medical documents, and data support can be provided for medical scientific research and clinical decision.
Disclosure of Invention
The disclosure provides a method and a device for associating clinical data with literature, an electronic device and a storage medium.
According to a first aspect of the present disclosure, there is provided a method of correlating clinical data with literature, comprising:
determining at least one piece of clinical medical record data related to the literature data from a clinical medical record database based on the disease, the intervention means and the comparison means corresponding to the literature data;
comparing the conclusion of the literature data with the intervention result in the clinical medical record data to determine a comparison result;
forming a conclusion for the disease based on the comparison result.
According to a second aspect of the present disclosure, there is provided an apparatus for associating clinical data with a document, comprising:
the acquisition unit is used for acquiring the structured literature data and extracting a disease treatment entity relationship pair and a contrast treatment entity relationship pair of the same disease in the literature data;
a recall unit to recall the matched first set of clinical medical record data from a clinical medical record database using the disease treatment entity-relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index; and carrying out XOR operation on the first medical record data set and the second medical record data set to obtain associated clinical medical record data matched with literature data.
A third aspect of the present disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of associating clinical data with literature described above.
A fourth aspect of the present disclosure provides a non-transitory computer-readable storage medium having stored thereon computer instructions for causing the computer to perform the method of associating clinical data with a document as described above.
A fifth aspect of the present disclosure provides a computer program product comprising computer programs/instructions which, when executed by a processor, implement the method of associating clinical data with a document as described above.
Acquiring structured literature data through a correlation method of clinical data and literature provided by the disclosure, and extracting a disease treatment entity relationship pair and a contrast treatment entity relationship pair of the same disease in the literature data; recalling the matched first set of clinical medical record data from a clinical medical record database using the disease treatment entity relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index; and performing exclusive OR operation on the first medical record data set and the second medical record data set to obtain associated clinical medical record data matched with literature data, so as to provide data support for medical scientific research and clinical decision.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 illustrates an alternative flow diagram of a method of correlating clinical data with a document provided by an embodiment of the present disclosure;
FIG. 2 illustrates an alternative flow diagram of a method of correlating clinical data with a document provided by an embodiment of the present disclosure;
FIG. 3 illustrates a schematic diagram of a method of correlating clinical data with a document provided by an embodiment of the present disclosure;
FIG. 4 is a schematic diagram illustrating an alternative configuration of an apparatus for associating clinical data with a document provided by an embodiment of the present disclosure;
FIG. 5 illustrates a schematic block diagram of an example electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the related art, the association between medical data and medical documents is mainly performed based on the association degree of keywords or labels. However, the keywords or labels of the literature cannot necessarily reflect the research content of the literature completely and accurately, and the experimental group and the control group cannot be clarified, and the keywords or labels have a certain difference from the clinical medical history experience in the text content, which also brings errors to the associated results.
Therefore, in view of the defects in the related art, the method for associating the literature and the medical data, the disclosed embodiments provide a method for associating the clinical data with the literature, which can solve at least some or all of the above technical problems.
Fig. 1 shows an alternative flow chart of a method for associating clinical data with a document provided by an embodiment of the present disclosure, which will be described according to various steps.
Step S101, acquiring structured literature data, and extracting a disease treatment entity relationship pair and a contrast treatment entity relationship pair of the same disease in the literature data.
In some embodiments, the disease treatment entity relationship pair comprises a disease, an intervention, and at least one treatment outcome; and/or, the proportion of each treatment outcome in the total treatment outcomes; the control treatment entity relationship pair comprises a disease, a control measure, and at least one treatment outcome; and/or the proportion of each treatment outcome in the total treatment outcomes.
In some embodiments, a device for associating clinical data with literature (hereinafter referred to as a device) obtains structured literature data, and extracts disease treatment entity relationship pairs and control treatment entity relationship pairs for the same disease in the literature data from the structured literature data.
In specific implementation, the device can directly acquire document data which is already structured, and can also perform structuring processing on the document data based on a method for acquiring an entity in the related art to acquire the structured document data. For example, the apparatus may process the document data based on an AC automaton to obtain structured document data.
Step S102, recalling a matched first clinical medical record data set from a clinical medical record database by using the disease treatment entity relation pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index; and carrying out XOR operation on the first medical record data set and the second medical record data set to obtain associated clinical medical record data matched with the literature data.
In some embodiments, the apparatus recalls, from the clinical medical records database, a first subset of clinical medical records that includes the disease based on the disease included in the pair of disease-treatment entity relationships or the pair of control-treatment entity relationships; recalling from the clinical medical record database a second subset of clinical medical records that includes the intervention instrument based on the intervention instrument that is included in the disease treatment entity-relationship pair; recall from the clinical medical record database a third subset of clinical medical records that includes the control means based on the control treatment entity-relationship pair included control means.
Further, the device and the first subset of clinical medical records and the second subset of clinical medical records to obtain the first set of clinical medical record data; and the first clinical medical record subset and the third clinical medical record subset are subjected to AND operation to obtain the second clinical medical record data set.
In some embodiments, the apparatus xors the first set of medical record data and the second set of medical record data to obtain an associated set of clinical medical record data that matches the literature data.
In specific implementation, the apparatus may perform an xor operation on the first medical record data set and the second medical record data set, determine medical record data included in both the first medical record data set and the second medical record data set, and delete the medical record data from the first medical record data set and the second medical record data set to obtain an associated clinical medical record data set matched with literature data. Optionally, the apparatus can delete the medical record data from the first medical record data set and the second medical record data set based on the ID of the medical record data.
Wherein the associated clinical medical record data matched with the literature data may include: the same disease as included in the literature data, and the clinical medical record data includes the same treatment as the intervention or control included in the literature data. For example, the disease in clinical history data is P1, and the treatment is I1; the disease of the literature data is P1, the intervention means is I1, and the control means is C1; the clinical medical record data and the literature data have the same disease, the literature data intervention means and the clinical medical record data have the same treatment means, and the clinical medical record data and the literature data are confirmed to be matched.
In some embodiments, the apparatus can also delete the same clinical medical record data in the first set of clinical medical record data and the second set of clinical medical record data. Extracting a first clinical treatment entity relationship pair in the first set of clinical medical record data after deleting the same clinical medical record data; and extracting a second clinical treatment entity relationship pair in the second set of clinical medical record data after deleting the same clinical medical record data.
In a specific implementation, the apparatus may extract a first clinical treatment entity relationship pair by taking an intersection of the associated clinical medical record data set and a first clinical medical record data set; and taking intersection of the associated clinical medical record data set and the second clinical medical record data set, and extracting a second clinical treatment entity relationship pair.
In some optional embodiments, after the apparatus obtains the clinical medical record data matching the literature data, the apparatus may further include:
step S103, grouping the first clinical treatment entity relationship pair and the second clinical treatment entity relationship pair according to a time window; the proportion of the different treatment outcomes in each group over the total treatment outcomes is determined.
In some embodiments, the apparatus may group the first pair of clinical treatment entity relationships and the second pair of clinical treatment entity relationships separately by a time window; identifying a plurality of sets of clinical treatment entity relationship pairs; and/or, merging at least two groups of clinical treatment entity relationship pairs such that the number of clinical treatment entity relationship pairs within each group after merging is greater than or equal to a first threshold. For example, the first pair of clinical treatment entity relationships and the second pair of clinical treatment entity relationships are grouped separately by age; such as one group every 5 years, one group every 10 years, etc. Thus, the change in the treatment outcome for the same disease with different treatment modalities as the medical level progresses can be determined.
In some embodiments, the device determines the proportion of the different treatment outcomes in each grouping that account for the total treatment outcomes.
In specific implementation, for at least one group of clinical treatment entity relationship pairs of the first clinical treatment entity relationship pair or the second clinical treatment entity relationship pair, the proportion of different treatment outcomes in each group of clinical treatment entity relationship pairs in all treatment outcomes corresponding to each group of clinical treatment entity relationship pairs is determined. For example, for a grouping corresponding to disease P3 and treatment P3, there may be three treatment outcomes in the pair of clinical treatment entity relationships, namely treatment outcome O1, treatment outcome O2, and treatment outcome O3; the number of treatment outcomes O1, treatment outcomes O2, and treatment outcomes O3, and the total number of treatment outcomes (i.e., the sum of the number of treatment outcomes O1, treatment outcomes O2, and treatment outcomes O3) in the cohort were determined separately, with the ratio of the number of treatment outcomes O1 to the total number of treatment outcomes being the ratio of the number of treatment outcomes O1 to the total number of treatment outcomes.
In some optional embodiments, after the apparatus determines the proportion of the total treatment outcomes of the different treatment outcomes in each group, the method may further include:
and step S104, determining the effectiveness of the intervention means and the comparison means corresponding to the treatment outcome based on the proportion of the different treatment outcomes in all the treatment outcomes in each group, and generating an auxiliary analysis suggestion by combining the literature data.
In some embodiments, the device confirms conclusions in the literature data for the disease and the intervention instrument, and conclusions in the literature data for the disease and the control instrument, based on the literature data, to determine the conclusions of the literature data.
In other embodiments, the device determines the effectiveness of the intervention means and the control means corresponding to the treatment outcome based on the proportion of the different treatment outcomes in each of the groups to the total treatment outcomes; optionally, the treatment outcome may include improvement, invariance and deterioration, and the effectiveness of the intervention means or the contrast means corresponding to the group is confirmed based on the proportion that the treatment outcome is improvement in the group.
In some optional embodiments, the device may further generate an auxiliary analysis suggestion by combining the conclusion of the literature data and the effectiveness of the intervention means and the control means corresponding to the outcome of the treatment.
The auxiliary analysis suggestion can be used for providing data support as medical scientific research and clinical decision, such as materials of new documents, or assisting the formation of new documents.
Therefore, by the method for associating the clinical data with the literature, the structured literature data is obtained, and the disease treatment entity relationship pair and the control treatment entity relationship pair of the same disease in the literature data are extracted; recalling the matched first set of clinical medical record data from a clinical medical record database using the disease treatment entity relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index; and performing exclusive OR operation on the first medical record data set and the second medical record data set to obtain clinical medical record data matched with literature data. Therefore, the medical data and the experience of structuring and normalization in the literature can be extracted, the accuracy of association of the medical data and the literature is improved, the conclusion in the literature can be verified through the medical data, the utilization rate of the medical data is improved, and data support is provided for medical scientific research and clinical decision.
Fig. 2 shows an alternative flow diagram of a method for associating clinical data with a document provided by an embodiment of the present disclosure, and fig. 3 shows a diagram of a method for associating clinical data with a document provided by an embodiment of the present disclosure, which will be described with reference to fig. 2 and 3.
Step S201, structured literature data is obtained, and disease treatment entity relation pairs and contrast treatment entity relation pairs of the same disease in the literature data are extracted.
In some embodiments, the literature data may be medical literature including literature of the type of control trial, typically by studying different treatment experiences and comparing, and identifying the disease (P-representation), intervention (I-representation), control (C-representation), and conclusion (O-representation) in the literature data using a medical entity identification method.
In some embodiments, the disease treatment entity relationship pair comprises a disease, an intervention, and at least one treatment outcome; and/or, the proportion of each treatment outcome in the total treatment outcomes; the control treatment entity relationship pair comprises a disease, a control measure, and at least one treatment outcome; and/or the proportion of each treatment outcome in the total treatment outcomes.
In some embodiments, a device for associating clinical data with literature (hereinafter referred to as a device) obtains structured literature data, and extracts disease treatment entity relationship pairs and control treatment entity relationship pairs for the same disease in the literature data from the structured literature data.
In specific implementation, the device can directly acquire document data which is already structured, and can also perform structuring processing on the document data based on a method for acquiring an entity in the related art to acquire the structured document data. For example, the apparatus may process the document data based on an AC automaton to obtain structured document data.
Step S202, acquiring diseases, treatment means and treatment outcome in the structured clinical medical record database.
In some embodiments, the medical data may be treatment experience obtained from clinical medical records, including which treatments (medications, surgery, radiation, etc.) were used after the disease occurred, and the final outcome of the treatment (improvement, invariance, or worsening) after the treatment was used. The correlation device of clinical data and literature extracts relevant treatment experiences from each clinical medical record data, then combines and summarizes according to diseases (P represents, such as diseases) and treatment means (I represents, such as medication and operation treatment), and calculates the corresponding numerical value (the proportion of each outcome in all outcomes) of various outcomes (O represents).
In some embodiments, the treatment or intervention may include drug therapy or surgical therapy, and the treatment outcome may include the final outcome after intervention in the disease by the treatment, such as improvement, invariance or worsening, or clinical findings of various symptoms, signs and the like after treatment, adverse reactions and the like.
In some alternative embodiments, the apparatus may directly obtain the already structured clinical medical record database, and may further perform a structuring process on the clinical medical record database based on a method for obtaining an entity in the related art to obtain the structured clinical medical record database. For example, the apparatus can process the clinical medical record database based on an AC automaton to obtain a structured clinical medical record database.
In step S203, associated clinical medical record data matching the literature data is obtained.
In some embodiments, the disease of the literature data is P2, the intervention modality is I2, and the control modality is C2. The device recalls the related clinical medical record data from the clinical medical record database based on the disease P2 and the intervention means I2 corresponding to the literature data, and the disease P2 and the control means C2 corresponding to the literature data.
In some embodiments, due to the enormous amount of data in the clinical medical record database, when determining the clinical medical record data associated with the literature data using clusters, a first set of clinical medical record data with disease P2 and treatment modality I2 can be screened from the clinical medical record database; and screening a second clinical medical record data set with the disease P2 and the treatment means C2 from the clinical medical record database, and confirming that the first clinical medical record data set and the second clinical medical record data set are data in the clinical medical record data.
In some embodiments, the apparatus xors the first set of medical record data and the second set of medical record data to obtain an associated set of clinical medical record data that matches the literature data.
In specific implementation, the apparatus may perform an xor operation on the first medical record data set and the second medical record data set, determine medical record data included in both the first medical record data set and the second medical record data set, and delete the medical record data from the first medical record data set and the second medical record data set to obtain an associated clinical medical record data set matched with literature data. Optionally, the apparatus may delete the medical record data from the first set of medical record data and the second set of medical record data based on the ID of the medical record data.
In particular, the apparatus may recall the corresponding experience (i.e., medical data including P2, I2 or P2, C2) from a clinical medical records database based on each entity of P2 and I2 (or P2 and C2) of the literature data, respectively, and finally take the intersection. Grouping is then carried out by taking the unique ID of the document data as a key (key), filtering operation is carried out on each group of data, the medical data recalled by the document data P2+ I2 and P2+ C2 are subjected to mutual exclusion processing (such as exclusive-or operation), and the medical data comprising P2, I2 and C2 are deleted.
For example, the apparatus obtains a first subset of clinical medical records that includes the P2 based on P2 of the literature data; based on the I2 of the literature data, obtaining a second subset of clinical medical records comprising the I2; based on C2 of the literature data, obtaining a third subset of clinical medical records that includes the C2; determining a first set of clinical medical record data based on an intersection of the first subset of clinical medical records and the second subset of clinical medical records; determining a second set of clinical medical record data based on an intersection of the first subset of clinical medical records and the third subset of clinical medical records; further, taking the unique ID of the literature data as a key word, and dividing the second clinical medical record data set and the first clinical medical record data set into a group; deleting the same first medical data in the first clinical medical record data set and the second clinical medical record data set; and determining the first clinical medical record data set and the second clinical medical record data set after the first medical data is deleted as the clinical medical record data.
For example, in the literature, in order to research the treatment method of the Mycoplasma pneumoniae inflammation, two treatment methods of the phlegm-heat clearing intravenous drip and the azithromycin are compared, and then P2 of the literature data is the Mycoplasma pneumoniae inflammation, I2 is the phlegm-heat clearing intravenous drip, and C2 is the azithromycin. Recalling a first subset of clinical medical records in the clinical medical records database that includes "Mycoplasma pneumoniae" based on P2; recalling a second subset of clinical medical records in the clinical medical records database comprising "phlegm-heat clearing intravenous drip" based on I2; recalling a third subset of clinical medical records in the database of clinical medical records that includes "azithromycin" based on C2; taking intersection of the first clinical medical record subset and the second clinical medical record subset to obtain a first clinical medical record data set which simultaneously comprises P2 and I2; intersecting the first subset of clinical medical records and the third subset of clinical medical records to obtain at least one piece of control medical data including both P2 and I2; dividing the second clinical medical record data set and the first clinical medical record data set into a group by taking the unique ID of the literature data as a key word; and deleting the same first medical data in the first clinical medical record data set and the second clinical medical record data set. Since the medical data may be recalled by both P2+ I2 and P2+ C2, i.e., the medical data adopts both treatment methods in the literature data, the mutual exclusion processing is required to exclude the medical data.
In some embodiments, the apparatus can also delete the same clinical medical record data in the first set of clinical medical record data and the second set of clinical medical record data. Extracting a first clinical treatment entity relationship pair in the first set of clinical medical record data after deleting the same clinical medical record data; and extracting a second clinical treatment entity relationship pair in the second set of clinical medical record data after deleting the same clinical medical record data.
In a specific implementation, the apparatus may extract a first clinical treatment entity relationship pair by taking an intersection of the associated clinical medical record data set and a first clinical medical record data set; and taking intersection of the associated clinical medical record data set and the second clinical medical record data set, and extracting a second clinical treatment entity relationship pair.
Step S204, grouping the first clinical treatment entity relationship pair and the second clinical treatment entity relationship pair according to a time window; the proportion of the different treatment outcomes in each group over the total treatment outcomes is determined.
In some embodiments, the apparatus may group the first and second pairs of clinical treatment entity relationships separately by a time window; confirming a plurality of groups of clinical medical record data sets; and/or, merging at least two groups of clinical treatment entity relationship pairs such that the number of clinical treatment entity relationship pairs within each group after merging is greater than or equal to a first threshold.
In some embodiments, the device determines the proportion of the different treatment outcomes in each grouping that account for the total treatment outcomes.
In specific implementation, for at least one group of clinical medical record data sets of the first clinical medical record data set, the proportion of different treatment outcomes in each group of clinical medical record data sets in all treatment outcomes corresponding to each group of clinical medical record data sets is determined. For example, for a group corresponding to disease P3 and treatment P3, there may be three treatment outcomes in the clinical history data set, namely treatment outcome O1, treatment outcome O2, and treatment outcome O3; the number of treatment outcomes O1, treatment outcomes O2, and treatment outcomes O3, and the total number of treatment outcomes (i.e., the sum of the number of treatment outcomes O1, treatment outcomes O2, and treatment outcomes O3) in the cohort were determined separately, with the ratio of the number of treatment outcomes O1 to the total number of treatment outcomes being the ratio of the number of treatment outcomes O1 to the total number of treatment outcomes.
In some embodiments, the apparatus generates at least one set of simulated experiments based on the first and second pairs of clinical treatment entity relationships; when any one of the steps S201 to S204 is implemented by using a cluster, the pairs of clinical treatment entity relationships may be grouped according to the year corresponding to the pair of clinical treatment entity relationships, and a plurality of groups of pairs of clinical treatment entity relationships are confirmed; and/or, merging at least two groups of clinical treatment entity relationship pairs such that the number of clinical treatment entity relationship pairs within each group after merging is greater than or equal to a first threshold; ensuring that the medical data of each group in the final grouping result is greater than the second threshold. And performing a simulation experiment on the grouped clinical treatment entity relationship pairs, and respectively performing summary calculation on the experiment results of the corresponding grouping of the first clinical treatment entity relationship pair and the corresponding grouping of the second clinical treatment entity relationship pair.
And S205, determining the effectiveness of the intervention means and the comparison means corresponding to the treatment outcome based on the proportion of the different treatment outcomes in all the treatment outcomes in each group, and generating an auxiliary analysis suggestion by combining the literature data.
In some embodiments, the device confirms conclusions in the literature data for the disease and the intervention instrument, and conclusions in the literature data for the disease and the control instrument, based on the literature data, to determine the conclusions of the literature data.
In other embodiments, the device determines the effectiveness of the intervention means and the control means corresponding to the treatment outcome based on the proportion of the different treatment outcomes in each of the groups to the total treatment outcomes; optionally, the treatment outcome may include improvement, invariance and deterioration, and the effectiveness of the intervention means or the contrast means corresponding to the group is confirmed based on the proportion that the treatment outcome is improvement in the group.
In some optional embodiments, the device may further combine the conclusion of the literature data and the effectiveness of the intervention and control corresponding to the outcome of the treatment to generate an auxiliary analysis recommendation. Wherein the auxiliary analysis suggestion can be used as a material of a new document or assist in the formation of a new document.
In some embodiments, the apparatus may be configured to generate at least one set of simulation experiments by the first clinical treatment entity relationship pair and the second clinical treatment entity relationship pair, to check whether the result of the medical data is consistent with the literature data conclusion, and to combine the comparison between the plurality of sets of medical data and/or literature to form different conclusions or new findings, so as to be used as a material of or to assist in writing a new literature.
In some embodiments, the apparatus may group the clinical medical record data in the clinical medical record database in advance based on step S201, and obtain the clinical medical record data subsets corresponding to different disease and treatment measures (including the intervention measure and the comparison measure corresponding to the literature data). After the literature data is subsequently acquired, the corresponding medical data set can be acquired from the medical data subsets respectively corresponding to the different diseases, intervention means and control means, and subsequent simulation experiments, result comparison analysis and the like are performed.
In other embodiments, the device may further acquire a corresponding medical data set based on a disease, an intervention means, and a control means corresponding to the literature data after acquiring the literature data; and then carrying out subsequent simulation experiments, result comparison analysis and the like.
Therefore, the correlation method of the clinical data and the literature provided by the embodiment of the disclosure can not only extract the medical data and the experience of structuring and normalization in the literature and improve the correlation accuracy of the medical data and the literature, but also verify the conclusion in the literature through the medical data and improve the utilization rate of the medical data and provide data support for medical scientific research and clinical decision.
Fig. 4 shows an alternative structure diagram of the device for associating clinical data with literature provided by the embodiment of the present disclosure, which will be described according to various parts.
In some embodiments, the apparatus 400 for associating clinical data with literature comprises: an acquisition unit 401 and a recall unit 402.
The obtaining unit 401 is configured to obtain structured literature data, and extract a disease treatment entity relationship pair and a control treatment entity relationship pair of the same disease in the literature data;
the recalling unit 402, configured to recall the matched first clinical medical record data set from the clinical medical record database using the disease treatment entity relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index; and carrying out XOR operation on the first medical record data set and the second medical record data set to obtain associated clinical medical record data matched with the literature data.
In some embodiments, the disease treatment entity relationship pair comprises a disease, an intervention, and at least one treatment outcome; and/or, the proportion of each treatment outcome in the total treatment outcomes; the control treatment entity relationship pair comprises a disease, a control measure, and at least one treatment outcome; and/or the proportion of each treatment outcome in the total treatment outcomes.
The recalling unit 402 is specifically configured to recall a first subset of clinical medical records including the disease from the clinical medical records database based on the disease included in the disease-treatment entity-relationship pair or the control-treatment entity-relationship pair; recalling from the clinical medical record database a second subset of clinical medical records that includes the intervention instrument based on the intervention instrument that is included in the disease treatment entity-relationship pair; recall from the clinical medical record database a third subset of clinical medical records that includes the control means based on the control treatment entity-relationship pair included control means.
The recalling unit 402 is specifically configured to perform an and operation on the first clinical medical record subset and the second clinical medical record subset to obtain the first clinical medical record data set; and the first clinical medical record subset and the third clinical medical record subset are subjected to AND operation to obtain the second clinical medical record data set.
The recall unit 402 is further configured to extract a first clinical treatment entity relationship pair by taking an intersection of the associated clinical medical record data set and the first clinical medical record data set; and taking intersection of the associated clinical medical record data set and the second clinical medical record data set, and extracting a second clinical treatment entity relationship pair.
In some embodiments, the apparatus 400 for associating clinical data with literature may further comprise: a grouping unit 403.
The grouping unit 403 is configured to group the first clinical treatment entity relationship pair and the first clinical treatment entity relationship pair according to a time window, respectively; the proportion of the different treatment outcomes in each group to the total treatment outcomes is determined.
In some embodiments, the apparatus 400 for associating clinical data with literature may further include: a validation unit 404.
The confirming unit is used for determining the effectiveness of the intervention means and the comparison means corresponding to the treatment outcome based on the proportion of the different treatment outcomes in all the treatment outcomes in each group, and generating the auxiliary analysis suggestion by combining the literature data.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information (clinical medical record data) of the related user are all in accordance with the regulations of related laws and regulations, and do not violate the good custom of the public order.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
Fig. 5 illustrates a schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 5, the apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The calculation unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable clinical data and article of manufacture, such that the program codes, when executed by the processor or controller, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (11)
1. A method of correlating clinical data with literature, the method comprising:
acquiring structured literature data, and extracting a disease treatment entity relationship pair and a contrast treatment entity relationship pair of the same disease in the literature data;
recalling the matched first set of clinical medical record data from a clinical medical record database using the disease treatment entity relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index; and carrying out XOR operation on the first medical record data set and the second medical record data set to obtain an associated clinical medical record data set matched with the literature data.
2. The method of claim 1,
the disease treatment entity relationship pair comprises a disease, an intervention, and at least one treatment outcome; and/or the proportion of each treatment outcome in the total treatment outcomes;
the control treatment entity relationship pair comprises a disease, a control measure, and at least one treatment outcome; and/or the proportion of each treatment outcome in the total treatment outcomes.
3. The method of claim 1 or 2, wherein the matching first set of clinical medical record data is recalled from a clinical medical record database using the disease treatment entity-relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index, comprising:
recalling a first subset of clinical medical records from the clinical medical records database that includes the disease based on the disease included in the pair of disease-treatment entity relationships or the pair of control-treatment entity relationships;
recalling from the clinical medical record database a second subset of clinical medical records that includes the intervention instrument based on the intervention instrument that is included in the disease treatment entity-relationship pair;
recall from the clinical medical record database a third subset of clinical medical records that includes the control means based on the control treatment entity-relationship pair included control means.
4. The method of claim 3, wherein the matching first set of clinical medical record data is recalled from a clinical medical record database using the disease treatment entity-relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index, comprising:
performing an and operation on the first clinical medical record subset and the second clinical medical record subset to obtain a first clinical medical record data set;
and the first clinical medical record subset and the third clinical medical record subset are subjected to AND operation to obtain the second clinical medical record data set.
5. The method of claim 1, wherein obtaining associated clinical medical record data that matches literature data comprises:
the associated clinical medical record data set and the first clinical medical record data set are intersected, and a first clinical treatment entity relationship pair is extracted;
and the intersection of the associated clinical medical record data set and the second clinical medical record data set is taken, and a second clinical treatment entity relationship pair is extracted.
6. The method of claim 5, wherein after the extracting the first and second clinical treatment entity relationship pairs, the method further comprises:
grouping the first pair of clinical treatment entity relationships and the first pair of clinical treatment entity relationships, respectively, by a time window;
the proportion of the different treatment outcomes in each group over the total treatment outcomes is determined.
7. The method of claim 6, wherein after determining the proportion of the total treatment outcome for the different treatment outcomes in each group, the method further comprises:
and determining the effectiveness of the intervention means and the comparison means corresponding to the treatment outcome based on the proportion of the different treatment outcomes in all the treatment outcomes in each group, and generating an auxiliary analysis suggestion by combining the literature data.
8. An apparatus for correlating clinical data with literature, the apparatus comprising:
the acquisition unit is used for acquiring the structured literature data and extracting a disease treatment entity relationship pair and a contrast treatment entity relationship pair of the same disease in the literature data;
a recall unit to recall the matched first set of clinical medical record data from a clinical medical record database using the disease treatment entity-relationship pair as a first index; recalling a second set of matched clinical medical record data from the clinical medical record database using the pair of control-treatment entity relationships as a second index; and carrying out XOR operation on the first medical record data set and the second medical record data set to obtain associated clinical medical record data matched with the literature data.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-7.
11. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-7.
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