CN111785370A - Medical record data processing method and device, computer storage medium and electronic equipment - Google Patents
Medical record data processing method and device, computer storage medium and electronic equipment Download PDFInfo
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Abstract
The present disclosure relates to the technical field of data processing, and provides a medical record data processing method, a medical record data processing apparatus, a computer storage medium, and an electronic device, wherein the medical record data processing method includes: classifying medical record data containing the target disease to obtain an integrated data set; step A: determining a target data set from the integrated data set according to the type of the target disease; and B: and mining the sequence mode of the target data set to obtain a predicted disease sequence corresponding to the target disease. The medical record data processing method can quickly mine the development and evolution rule of a specific disease on the basis of medical record data, solves the technical problems of low accuracy and small coverage caused by predicting the evolution trend of the disease condition of a patient only by the personal experience of a clinician in the related technology, improves the comprehensiveness of disease condition prediction, and provides effective reference for medical scientific research and clinical treatment.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a medical record data processing method, a medical record data processing apparatus, a computer storage medium, and an electronic device.
Background
With the development of social economy, the life style and the living habits of people are changed, the disease spectrum is greatly changed, a plurality of novel diseases appear, the life and the health of people are greatly influenced, and partial diseases with extremely strong infectivity even take away the life of people without love. Therefore, how to effectively predict the evolution trend of the patient's disease condition becomes a focus of attention of the related technicians.
In conventional approaches, the patient's evolving trend is generally predicted based on the clinician's personal experience and his or her own accumulated medical knowledge. However, experience is a long-term cumulative process with some subjectivity, and its coverage is limited and the prediction results are not comprehensive enough.
In view of the above, there is a need in the art to develop a new medical record data processing method and device.
It is to be noted that the information disclosed in the background section above is only used to enhance understanding of the background of the present disclosure.
Disclosure of Invention
The present disclosure is directed to a medical record data processing method, a medical record data processing apparatus, a computer storage medium, and an electronic device, so as to avoid, at least to a certain extent, a defect that an evolution trend of a patient's condition cannot be comprehensively predicted in the related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of the present disclosure, there is provided a medical record data processing method, including: classifying medical record data containing the target disease to obtain an integrated data set; step A: determining a target data set from the integrated data set according to the type of the target disease; and B: and mining the sequence mode of the target data set to obtain a predicted disease sequence corresponding to the target disease.
In an exemplary embodiment of the disclosure, after obtaining the predicted disease sequence corresponding to the target disease, the method further comprises: judging whether a specified disease exists in the predicted disease sequence based on a medical knowledge base, wherein the specified disease is a disease which does not have a pathological association relation with the target disease; if so, removing the specified disease from the integrated dataset; and repeating the step A and the step B to obtain a new predicted disease sequence corresponding to the target disease.
In an exemplary embodiment of the present disclosure, the integrated dataset includes a plurality of medical event sequences corresponding to a plurality of patients; the determining a target data set from the integrated data set according to the type of the target disease comprises: respectively selecting a characteristic subsequence from each medical event sequence according to the type of the target disease; determining the target data set according to a plurality of the characteristic subsequences.
In exemplary embodiments of the present disclosure, the types of target diseases include chronic diseases and malignant diseases; selecting a characteristic subsequence from each medical event sequence according to the type of the target disease, wherein the characteristic subsequence comprises: when the type of the target disease is a chronic disease, selecting a sequence after the target disease is diagnosed from each medical event sequence as the characteristic subsequence; when the type of the target disease is malignant disease, selecting a sequence before the target disease is diagnosed from each medical event sequence as the characteristic subsequence.
In an exemplary embodiment of the present disclosure, the mining the sequence pattern of the target data set to obtain a predicted disease sequence corresponding to the target disease includes: performing sequence pattern mining on the target data set based on a sequence pattern mining algorithm to obtain a plurality of candidate predicted disease sequences corresponding to the target disease; determining the candidate predicted disease sequence with the frequency of occurrence larger than a frequency threshold value as the predicted disease sequence corresponding to the target disease.
In an exemplary embodiment of the disclosure, before the medical record data including the target disease is categorized to obtain the integrated data set, the method further includes: acquiring a plurality of medical events in original medical record data corresponding to each patient, and determining a timestamp corresponding to each medical event; sequencing the plurality of medical events according to the sequence of the timestamps from morning to evening to obtain a medical event sequence; determining the sequence of medical events as the medical record data.
In an exemplary embodiment of the disclosure, after obtaining the sequence of medical events, the method further comprises: determining an invalid medical event of the plurality of target medical events when the same plurality of target medical events exist in the sequence of medical events; the ineffective medical event is the target medical event occurring at the Nth time; removing the invalid medical events; n is a positive integer greater than 1.
According to a second aspect of the present disclosure, there is provided a medical record data processing apparatus including: the classification processing module is used for classifying medical record data containing the target disease to obtain an integrated data set; a determination module for determining a target data set from the integrated data set according to the type of the target disease; and the sequence pattern mining module is used for mining the sequence pattern of the target data set to obtain a predicted disease sequence corresponding to the target disease.
According to a third aspect of the present disclosure, there is provided a computer storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the medical record data processing method of the first aspect described above.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the medical record data processing method of the first aspect via execution of the executable instructions.
According to the technical solutions, the medical record data processing method, the medical record data processing apparatus, the computer storage medium and the electronic device in the exemplary embodiments of the present disclosure have at least the following advantages and positive effects:
in the technical solutions provided in some embodiments of the present disclosure, on one hand, medical history data including a target disease is classified to obtain an integrated data set, and the target data set is determined from the integrated data set according to the type of the target disease, so that redundant data that do not meet the disease development condition can be removed for different disease types, and the target data set that meet the disease development condition is retained, thereby ensuring the subsequent data processing speed and reliability. On the other hand, the target data set is subjected to sequence pattern mining to obtain a predicted disease sequence corresponding to the target disease, the development and evolution rules of the specific disease can be rapidly mined, the technical problems of low accuracy and small coverage caused by the fact that the evolution trend of the patient's condition is predicted only by the personal experience of a clinician in the related technology are solved, the prediction comprehensiveness is improved, a prevention reference is provided for part of early-stage patients, and an effective reference is provided for medical scientific research and clinical treatment.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 is a flow chart diagram illustrating a method for medical record data processing in an exemplary embodiment of the disclosure;
FIG. 2 is a sub-flow diagram illustrating a method for medical record data processing in an exemplary embodiment of the disclosure;
FIG. 3 is a sub-flow diagram illustrating a method for medical record data processing in an exemplary embodiment of the disclosure;
FIG. 4 is a general flow chart diagram illustrating a method for medical record data processing according to an exemplary embodiment of the disclosure;
FIG. 5 is a schematic diagram showing the structure of a medical record data processing device in an exemplary embodiment of the disclosure;
FIG. 6 shows a schematic diagram of a structure of a computer storage medium in an exemplary embodiment of the disclosure;
fig. 7 shows a schematic structural diagram of an electronic device in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/parts/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.; the terms "first" and "second", etc. are used merely as labels, and are not limiting on the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
In conventional approaches, the patient's evolving trend is generally predicted based on the clinician's personal experience and his or her own accumulated medical knowledge. However, experience is a long-term cumulative process and is somewhat subjective. Moreover, the coverage is limited, and the prediction result is not comprehensive enough.
In addition, for some difficult and complicated diseases with low incidence rate and complicated etiology, the evolution trend is still unknown, and the patients are difficult to know the trend of their disease conditions only according to the personal experience of the clinicians.
In the embodiment of the disclosure, a medical record data processing method is provided, which overcomes, at least to some extent, the drawback of the related art that the patient's evolution trend cannot be fully predicted.
Fig. 1 is a flowchart illustrating a medical record data processing method according to an exemplary embodiment of the disclosure, where an execution subject of the medical record data processing method may be a server that processes medical record data.
Referring to fig. 1, a medical record data processing method according to an embodiment of the present disclosure includes the steps of:
step S110, classifying medical record data containing target diseases to obtain an integrated data set;
step S120 (step A), determining a target data set from the integrated data set according to the type of the target disease;
and step S130 (step B), carrying out sequence pattern mining on the target data set to obtain a predicted disease sequence corresponding to the target disease.
In the technical solution provided in the embodiment shown in fig. 1, on one hand, medical history data including a target disease is classified to obtain an integrated data set, and the target data set is determined from the integrated data set according to the type of the target disease, so that redundant data not conforming to the disease development condition can be removed for different disease types, and the target data set conforming to the disease development condition is retained, thereby ensuring the subsequent data processing speed and reliability. On the other hand, the target data set is subjected to sequence pattern mining to obtain a predicted disease sequence corresponding to the target disease, the development and evolution rules of the specific disease can be rapidly mined, the technical problems of low accuracy and small coverage caused by the fact that the evolution trend of the patient's condition is predicted only by the personal experience of a clinician in the related technology are solved, the prediction comprehensiveness is improved, a prevention reference is provided for part of early-stage patients, and an effective reference is provided for medical scientific research and clinical treatment.
The following describes the specific implementation of each step in fig. 1 in detail:
in an exemplary embodiment of the present disclosure, medical record data for a plurality of patients may be acquired first. For example, referring to fig. 2, fig. 2 shows a sub-flow diagram of a medical data processing method in an exemplary embodiment of the present disclosure, and specifically shows a sub-flow diagram of processing original medical record data to obtain medical record data, including steps S201 to S203, and the following explains a specific implementation manner with reference to fig. 2.
In step S201, a plurality of medical events in the original medical record data corresponding to each patient are acquired, and a timestamp corresponding to each medical event is determined.
The original medical record data may be electronic medical record data of a patient(s) with a history of medical visits from a hospital database, where the electronic medical record is a digitized medical record stored, managed, transmitted, and reproduced by an electronic device, and is used to replace medical record information of a handwritten paper medical record, and mainly includes original records of the whole process of diagnosis and treatment of the hospital, for example, the original medical record data may include: diagnostic information (e.g., dizziness, insomnia, hypertension, gastroenteritis, hyperlipidemia, etc.), medication information (e.g., hydrochlorothiazide, indapamide, etc.), examination information (e.g., electrocardiogram, chest X-ray, urinary routine, etc.), surgical information (e.g., cataract surgery, cardiac stent surgery, cardiac bypass surgery, etc.), and cost information.
Wherein the medical event may be a confirmed diagnosis of a disease or an adverse reaction of the patient. For example, for patient a, its corresponding plurality of medical events may be: accurate diagnosis of hypertension, hyperlipidemia, and gastroenteritis. Further, for example, the timestamp corresponding to the medical event "dizziness and insomnia" may be determined to be 6 months in 2014, the timestamp corresponding to the medical event "diagnosed hypertension" may be determined to be 6 months in 2015, the timestamp corresponding to the medical event "diagnosed hyperlipidemia" may be 6 months in 2016, and the timestamp corresponding to the medical event "diagnosed gastroenteritis" may be 6 months in 2017.
For patient B, its corresponding plurality of medical events may be: dyspepsia, burning sensation in stomach area, late stage of gastric cancer, and emaciation. Further, for example, it may be determined that the medical event "dyspepsia" corresponds to a timestamp of 2014 6 months, the medical event "gastric burning sensation" corresponds to a timestamp of 2015 6 months, the medical event "gastric cancer advanced stage" corresponds to a timestamp of 2016 6 months, and the medical event "wasting" corresponds to a timestamp of 2017 6 months.
In step S202, a plurality of medical events are sorted according to the sequence of the timestamps from morning to evening, and a medical event sequence is obtained.
After determining the plurality of medical events in the original medical record data corresponding to each patient and determining the timestamp corresponding to each medical event, the plurality of medical events may be sorted according to the data of the timestamps from the beginning to the end to obtain a medical event sequence.
Illustratively, referring to the above explanation related to step S201, the medical event sequence corresponding to patient a may be: dizziness, insomnia, hypertension, hyperlipidemia, gastroenteritis; the sequence of medical events for patient B may be: dyspepsia-burning sensation in the stomach area-advanced gastric cancer-emaciation.
When a plurality of identical target medical events exist in the medical event sequence, an invalid medical event (an invalid medical event is a target medical event occurring the nth time (N is a positive integer greater than 1)) in the plurality of target medical events may be determined, and the invalid medical event may be eliminated. For example, when the obtained medical event sequence is "dizziness, insomnia, hypertension, hyperlipidemia, gastroenteritis, nausea, asthenia and gastroenteritis", the target medical event "gastroenteritis" appearing at the 2 nd time can be determined as an invalid medical event, and then the invalid medical event "gastroenteritis" appearing at the 2 nd time can be removed to process the medical event sequence into "dizziness, insomnia, hypertension, hyperlipidemia, gastroenteritis, nausea, debilitation". Therefore, medical events of important time nodes can be reserved, and interference of invalid data on a subsequent processing process is avoided.
In step S203, the medical event sequence is determined as medical record data.
Further, a plurality of medical event sequences corresponding to a plurality of patients may be specified as medical record data.
With continued reference to fig. 1, in step S110, the medical record data including the target disease is categorized to obtain an integrated data set.
After the medical record data is obtained, the medical record data including the target disease can be classified to obtain an integrated data set. The target diseases may be diseases with unclear disease development trend, diseases with high incidence, diseases with low cure rate, diseases with low incidence but high fatality rate, and the like, and can be set according to actual conditions, and belong to the protection scope of the present disclosure.
Illustratively, when the target disease is hypertension, a plurality of pieces of medical record data including hypertension can be screened out and integrated to obtain an integrated data set SG. When the target disease is late gastric cancer, a plurality of medical record data containing the late gastric cancer can be screened out and integrated to obtain another integrated data set SW。
In step S120 (step a), a target data set is determined from the integrated data set according to the type of the target disease.
After obtaining the integrated data set, a target data set may be determined from the integrated data set according to the type of the target disease. The type of target disease may include, among others, chronic diseases and malignant diseases. Chronic diseases are partial diseases which are long-term and are not easy to cure, once chronic diseases are caused, the chronic diseases are difficult to cure even can be accompanied in a lifetime, and the treatment can require lifetime medicine taking, for example: hypertension, diabetes, coronary heart disease, etc. Malignant diseases, i.e., diseases that once diagnosed, the patient's life is at risk at any time and can lead to the death of the patient in a short period of time, such as: malignant tumor, advanced lung cancer, advanced gastric cancer, etc. Therefore, the redundant data which do not accord with the disease development condition can be removed according to different disease types, and the target data set which accords with the disease development condition is reserved, so that the subsequent data processing speed and the reliability are ensured.
Further, it can be determined that the target disease "hypertension" is a chronic disease and the target disease "late gastric cancer" is a malignant disease.
When the type of the target disease is a chronic disease, the sequence after the target disease is diagnosed can be selected from the medical event sequences as a characteristic subsequence, so that the characteristic subsequence which better accords with the attention points of doctors and related patients with chronic diseases can be obtained, and the related patients with chronic diseases can conveniently know the evolution trend of the disease. Illustratively, with reference to the above explanation regarding step S202, a characteristic subsequence may be selected from the series of medical events corresponding to patient a: hypertension-hyperlipidemia-gastroenteritis. To integrate the data set SGIncluding a plurality of medical event sequences corresponding to a plurality of patients, a feature subsequence can be respectively selected from the medical event sequences of the patients, and the obtained feature subsequences are determined as a target data set. Exemplarily, reference may be made to table 1, where table 1 schematically illustrates a target data set M corresponding to a plurality of feature sub-sequencesG(including the target disease "hypertension").
TABLE 1
When the type of the target disease is malignant disease, a sequence before the target disease is diagnosed can be selected from the medical event sequences as a characteristic subsequence, so that a prevention reference can be provided for a relevant patient suffering from mild disease, and the phenomenon that the patient misses the optimal treatment time due to carelessness when suffering from mild disease to cause malignant disease is avoided. Illustratively, with reference to the above explanation regarding step S202, a characteristic subsequence may be selected from the series of medical events corresponding to patient B: dyspepsia-burning sensation in the stomach area-advanced gastric cancer. To integrate the data set SWIncluding a plurality of medical events corresponding to a plurality of patients, a feature subsequence can be respectively selected from the medical event sequences of the patients, and the obtained feature subsequences are determined as a target data set. Exemplarily, reference may be made to table 2, where table 2 schematically illustrates a target data set M corresponding to a plurality of feature sub-sequencesw(including the target disease "advanced gastric cancer").
TABLE 2
In step S130 (step B), sequence pattern mining is performed on the target data set to obtain a predicted disease sequence corresponding to the target disease.
After the target data set is obtained, sequence pattern mining can be performed on the target data set based on a sequence pattern mining algorithm (for example, a prefixspan algorithm, a GSP algorithm, a SPADE algorithm, a Disc-all algorithm and the like, which can be set by self according to actual conditions and belong to the protection range of the present disclosure), so as to obtain a plurality of candidate predicted disease sequences corresponding to the target disease. Wherein, the sequence pattern mining refers to mining frequently-occurring 'subsequences' from a large amount of sequence data, and the candidate predicted disease sequences can be a plurality of frequently-occurring disease sequences.
Then illustratively, for target dataset MGThe plurality of candidate predicted disease sequences obtained by mining may be "hypertension-hyperlipidemia-gastroenteritis (the frequency of occurrence is 90%), hypertension-conjunctivitis-gastroenteritis (the frequency of occurrence is 76%)". For a target data set MwThe plurality of candidate predicted disease sequences obtained by mining may be "dyspepsia-gastric burning sensation-late gastric cancer (occurrence frequency of 88%), dyspepsia-nausea vomiting-late gastric cancer (occurrence frequency of 72%)". Therefore, the development and evolution rules of specific diseases can be rapidly mined, the technical problems of low accuracy and small coverage caused by predicting the evolution trend of the patient's condition only by the personal experience of a clinician in the related technology are solved, the comprehensiveness of prediction is improved, and the method is a part ofEarly patients provide prevention reference and effective reference for medical scientific research and clinical treatment.
Furthermore, a frequency threshold (specific numerical value can be set according to actual conditions, and belongs to the protection scope of the present disclosure) may be set, and the candidate predicted disease sequence with the occurrence frequency greater than the frequency threshold is determined as the predicted disease sequence corresponding to the target disease. For example, when the set frequency threshold is 80%, it can be determined that the predicted disease sequence corresponding to the target disease "hypertension" is "hypertension-hyperlipidemia-gastroenteritis", and the predicted disease sequence corresponding to the target disease "late gastric cancer" is "dyspepsia-burning sensation in gastric region-late gastric cancer".
And when the set frequency threshold is 70%, the predicted disease sequences corresponding to the target disease "hypertension" can be determined as "hypertension-hyperlipidemia-gastroenteritis" and "hypertension-conjunctivitis-gastroenteritis". The target disease "late gastric cancer" corresponds to the predicted disease sequences "dyspepsia-late gastric cancer" and "dyspepsia-nausea and vomiting-late gastric cancer".
Therefore, when different frequency thresholds are set, different numbers of predicted disease sequences can be obtained, so that the disease state evolution trend of a patient can be comprehensively predicted, the patient can be helped to predict the disease evolution trend in advance, and the patient can do things which can be met by the dietary habits, the health habits, the life styles and the like, the autoimmunity can be enhanced, the diseases can be prevented, and the rapid deterioration of the diseases can be avoided.
After obtaining the predicted disease sequence corresponding to the target disease, referring to fig. 3, fig. 3 shows a sub-flowchart of the medical record data processing method in an exemplary embodiment of the present disclosure, and specifically shows a sub-flowchart of obtaining a new predicted disease sequence, which includes steps S301 to S303, and the following explains a specific implementation manner with reference to fig. 3.
In step S301, it is determined whether or not a specified disease, which is a disease having no pathological association with the target disease, exists in the predicted disease sequence based on the medical knowledge base.
Illustratively, a medical knowledge base (i.e., a database containing a large amount of medical knowledge, illustratively, a database containing causes, cases, diagnosis methods, treatment methods, and prevention methods of various diseases, treatment drugs, usage amounts, cautions, and medication descriptions of various diseases, clinical common disease knowledge, common drug knowledge, etc.) may be constructed in advance, and whether a specified disease exists in a predicted disease sequence is determined based on the medical knowledge base, where the specified disease may be a disease that does not have a pathological association relationship with the target disease. For example, when it is determined that the predicted disease sequence corresponding to the target disease "hypertension" is "hypertension-hyperlipidemia-gastroenteritis-cold", then, for example, it may be determined that "cold" is a specified disease that has no pathological association with the target disease based on the above medical knowledge base.
In step S302, if present, the specified disease is removed from the integrated dataset.
Further, from the above-mentioned integrated data set S, it is possible toGThe above-mentioned specific diseases "cold" are removed. Therefore, the specified diseases which do not have pathological association relation with the target disease can be removed, the interference of the specified diseases on the data processing result is avoided, and the reliability of a subsequently generated new predicted disease sequence is ensured.
In step S303, the above steps a and B are repeatedly performed to obtain a new predicted disease sequence corresponding to the target disease.
After the designated disease is removed from the integrated data set, steps a and B (i.e., steps S120 and S130) may be repeatedly performed to iteratively determine a new predicted disease sequence corresponding to the target disease. Therefore, the reliability of the generated new predicted disease sequence can be improved, and powerful evidence and feasibility guidance are provided for medical research of related disease development rules or causes.
By way of example, referring to fig. 4, fig. 4 shows an overall flowchart of a medical record data processing method in an exemplary embodiment of the disclosure, which includes steps S401 to S404, and a specific implementation is explained below with reference to fig. 4.
In step S401, data processing (i.e. ordering medical events in the original medical data to obtain a medical event sequence, and determining the medical event sequence as medical record data; classifying the medical record data to obtain an integrated data set; determining a target data set from the integrated data set);
in step S402, mining a sequence pattern to obtain a predicted disease sequence corresponding to a target disease;
in step S403, the priori knowledge is modified (whether a specified disease exists in the predicted disease sequence is determined based on the medical knowledge base, the specified disease is removed from the integrated data set), and a target data set is determined from the integrated data set and is subjected to sequence pattern mining;
in step S404, the evolution law of the target disease (new predicted disease sequence) is obtained.
Based on the technical scheme, on one hand, the method and the device can remove redundant data which do not accord with the disease development situation and reserve a target data set which accords with the disease development situation aiming at different disease types, so that the subsequent data processing speed and the reliability are ensured. On the other hand, the development and evolution rules of specific diseases can be rapidly mined, the technical problems of low accuracy and small coverage caused by predicting the evolution trend of the patient's condition only by the personal experience of a clinician in the related technology are solved, the comprehensiveness of prediction is improved, a prevention reference is provided for part of early patients, and an effective reference is provided for medical scientific research and clinical treatment.
The present disclosure also provides a medical record data processing apparatus, and fig. 5 shows a schematic structural diagram of the medical record data processing apparatus in an exemplary embodiment of the present disclosure; as shown in FIG. 5, the medical record data processing device 500 can include a categorization module 501, a determination module 502, and a sequence pattern mining module 503. Wherein:
and the classification processing module 501 is configured to classify medical record data including the target disease to obtain an integrated data set.
In an exemplary embodiment of the disclosure, the classification processing module is configured to acquire a plurality of medical events in original medical record data corresponding to each patient, and determine a timestamp corresponding to each medical event; sequencing a plurality of medical events according to the sequence of the timestamps from morning to evening to obtain a medical event sequence; the sequence of medical events is determined as medical record data.
In an exemplary embodiment of the disclosure, the classification processing module is further configured to determine an invalid medical event of the plurality of target medical events when the same plurality of target medical events exist in the sequence of medical events; the invalid medical event is a target medical event occurring at the Nth time; eliminating invalid medical events; n is a positive integer greater than 1.
A determination module 502 for determining a target data set from the integrated data set according to the type of the target disease.
In an exemplary embodiment of the disclosure, the determining module is configured to select a characteristic subsequence from each medical event sequence according to a type of the target disease; from the plurality of feature subsequences, a target data set is determined.
In exemplary embodiments of the present disclosure, the types of target diseases include chronic diseases and malignant diseases; the determining module is also used for selecting a sequence after the target disease is diagnosed from all the medical event sequences as a characteristic subsequence when the type of the target disease is chronic disease; when the type of the target disease is malignant disease, the sequence before the target disease is diagnosed is selected from the medical event sequences as a characteristic subsequence.
And the sequence pattern mining module 503 is configured to perform sequence pattern mining on the target data set to obtain a predicted disease sequence corresponding to the target disease.
In an exemplary embodiment of the disclosure, the sequence pattern mining module is configured to perform sequence pattern mining on a target data set based on a sequence pattern mining algorithm to obtain a plurality of candidate predicted disease sequences corresponding to a target disease; and determining the candidate predicted disease sequences with the occurrence frequency larger than the frequency threshold value as the predicted disease sequences corresponding to the target diseases.
In an exemplary embodiment of the disclosure, the sequence pattern mining module is further configured to determine whether a specified disease exists in the predicted disease sequence based on the medical knowledge base, the specified disease being a disease that does not have a pathological association with the target disease; if so, removing the specified disease from the integrated data set; and repeating the steps of determining the target data set and mining the sequence pattern to obtain a new predicted disease sequence corresponding to the target disease.
The specific details of each module in the medical record data processing apparatus have been described in detail in the corresponding medical record data processing method, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer storage medium capable of implementing the above method. On which a program product capable of implementing the above-described method of the present specification is stored. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
Referring to fig. 6, a program product 600 for implementing the above method according to an embodiment of the present disclosure is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 700 according to this embodiment of the disclosure is described below with reference to fig. 7. The electronic device 700 shown in fig. 7 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 7, electronic device 700 is embodied in the form of a general purpose computing device. The components of the electronic device 700 may include, but are not limited to: the at least one processing unit 710, the at least one memory unit 720, a bus 730 connecting different system components (including the memory unit 720 and the processing unit 710), and a display unit 740.
Wherein the storage unit stores program code that is executable by the processing unit 710 to cause the processing unit 710 to perform steps according to various exemplary embodiments of the present disclosure as described in the above section "exemplary methods" of this specification. For example, the processing unit 710 may perform the following as shown in fig. 1: step S110, classifying medical record data containing target diseases to obtain an integrated data set; step S120 (step A), determining a target data set from the integrated data set according to the type of the target disease; and step S130 (step B), carrying out sequence pattern mining on the target data set to obtain a predicted disease sequence corresponding to the target disease.
The storage unit 720 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)7201 and/or a cache memory unit 7202, and may further include a read only memory unit (ROM) 7203.
The storage unit 720 may also include a program/utility 7204 having a set (at least one) of program modules 7205, such program modules 7205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 700 may also communicate with one or more external devices 800 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 700, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 700 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 750. Also, the electronic device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 760. As shown, the network adapter 760 communicates with the other modules of the electronic device 700 via the bus 730. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A medical record data processing method is characterized by comprising the following steps:
classifying medical record data containing the target disease to obtain an integrated data set;
step A: determining a target data set from the integrated data set according to the type of the target disease;
and B: and mining the sequence mode of the target data set to obtain a predicted disease sequence corresponding to the target disease.
2. The method of claim 1, wherein after obtaining the predicted disease sequence corresponding to the disease of interest, the method further comprises:
judging whether a specified disease exists in the predicted disease sequence based on a medical knowledge base, wherein the specified disease is a disease which does not have a pathological association relation with the target disease;
if so, removing the specified disease from the integrated dataset;
and repeating the step A and the step B to obtain a new predicted disease sequence corresponding to the target disease.
3. The method of claim 1 or 2, wherein the integrated dataset comprises a plurality of medical event sequences corresponding to a plurality of patients;
the determining a target data set from the integrated data set according to the type of the target disease comprises:
respectively selecting a characteristic subsequence from each medical event sequence according to the type of the target disease;
determining the target data set according to a plurality of the characteristic subsequences.
4. The method of claim 3, wherein the types of target diseases include chronic diseases and malignant diseases;
selecting a characteristic subsequence from each medical event sequence according to the type of the target disease, wherein the characteristic subsequence comprises:
when the type of the target disease is a chronic disease, selecting a sequence after the target disease is diagnosed from each medical event sequence as the characteristic subsequence;
when the type of the target disease is malignant disease, selecting a sequence before the target disease is diagnosed from each medical event sequence as the characteristic subsequence.
5. The method of claim 1, wherein the mining the target data set in a sequence pattern to obtain a predicted disease sequence corresponding to the target disease comprises:
performing sequence pattern mining on the target data set based on a sequence pattern mining algorithm to obtain a plurality of candidate predicted disease sequences corresponding to the target disease;
determining the candidate predicted disease sequence with the frequency of occurrence larger than a frequency threshold value as the predicted disease sequence corresponding to the target disease.
6. The method of claim 1, wherein prior to categorizing the medical record data comprising the target disease into the integrated data set, the method further comprises:
acquiring a plurality of medical events in original medical record data corresponding to each patient, and determining a timestamp corresponding to each medical event;
sequencing the plurality of medical events according to the sequence of the timestamps from morning to evening to obtain a medical event sequence;
determining the sequence of medical events as the medical record data.
7. The method of claim 6, wherein after obtaining the sequence of medical events, the method further comprises:
determining an invalid medical event of the plurality of target medical events when the same plurality of target medical events exist in the sequence of medical events; the ineffective medical event is the target medical event occurring at the Nth time;
removing the invalid medical events;
n is a positive integer greater than 1.
8. A medical record data processing apparatus, comprising:
the classification processing module is used for classifying medical record data containing the target disease to obtain an integrated data set;
a determination module for determining a target data set from the integrated data set according to the type of the target disease;
and the sequence pattern mining module is used for mining the sequence pattern of the target data set to obtain a predicted disease sequence corresponding to the target disease.
9. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the medical record data processing method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute the medical record data processing method of any one of claims 1-7 via execution of the executable instructions.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113096756A (en) * | 2021-04-26 | 2021-07-09 | 安徽科大讯飞医疗信息技术有限公司 | Disease evolution classification method and device, electronic equipment and storage medium |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140229495A1 (en) * | 2011-01-19 | 2014-08-14 | Koninklijke Philips N.V. | Method for processing genomic data |
| US20160321426A1 (en) * | 2015-04-28 | 2016-11-03 | International Business Machines Corporation | Generating predictive models based on text analysis of medical study data |
| CN106232831A (en) * | 2013-11-06 | 2016-12-14 | 美国卫生和人力服务部 | Method lymphoma type being carried out Subtypes by expression pattern analysis |
| CN106874632A (en) * | 2017-04-06 | 2017-06-20 | 聂聪 | Slow disease health index intelligent monitoring system |
| CN107851464A (en) * | 2015-08-17 | 2018-03-27 | 西门子保健有限责任公司 | For carrying out the method and system of progression of disease modeling and therapy optimization for individual patient |
| CN109036579A (en) * | 2018-08-22 | 2018-12-18 | 泰康保险集团股份有限公司 | Information forecasting method, device, medium and electronic equipment based on block chain |
| CN109493979A (en) * | 2018-10-23 | 2019-03-19 | 平安科技（深圳）有限公司 | A kind of disease forecasting method and apparatus based on intelligent decision |
| CN109659033A (en) * | 2018-12-18 | 2019-04-19 | 浙江大学 | A kind of chronic disease change of illness state event prediction device based on Recognition with Recurrent Neural Network |
| WO2019132685A1 (en) * | 2017-12-29 | 2019-07-04 | Общество С Ограниченной Ответственностью "Интеллоджик" | Method and system for supporting medical decision making |
| CN110085323A (en) * | 2019-04-19 | 2019-08-02 | 北京工业大学 | A kind of diagnosis and treatment path discovery method based on electronic health record |
| CN110211690A (en) * | 2019-04-19 | 2019-09-06 | 平安科技（深圳）有限公司 | Disease risks prediction technique, device, computer equipment and computer storage medium |
| CN110277172A (en) * | 2019-06-27 | 2019-09-24 | 齐鲁工业大学 | A kind of clinical application behavior analysis system and its working method based on efficient negative sequence mining mode |
| CN110459324A (en) * | 2019-06-27 | 2019-11-15 | 平安科技（深圳）有限公司 | Disease forecasting method, apparatus and computer equipment based on shot and long term memory models |
| CN110781216A (en) * | 2019-11-05 | 2020-02-11 | 广东工业大学 | Traditional Chinese medicine symptom association rule mining method and device and storage medium |
-
2020
- 2020-07-01 CN CN202010632725.8A patent/CN111785370A/en active Pending
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140229495A1 (en) * | 2011-01-19 | 2014-08-14 | Koninklijke Philips N.V. | Method for processing genomic data |
| CN106232831A (en) * | 2013-11-06 | 2016-12-14 | 美国卫生和人力服务部 | Method lymphoma type being carried out Subtypes by expression pattern analysis |
| US20160321426A1 (en) * | 2015-04-28 | 2016-11-03 | International Business Machines Corporation | Generating predictive models based on text analysis of medical study data |
| CN107851464A (en) * | 2015-08-17 | 2018-03-27 | 西门子保健有限责任公司 | For carrying out the method and system of progression of disease modeling and therapy optimization for individual patient |
| US20180233233A1 (en) * | 2015-08-17 | 2018-08-16 | Siemens Healthcare Gmbh | Method and System for Disease Progression Modeling and Therapy Optimizing for Individual Patients |
| CN106874632A (en) * | 2017-04-06 | 2017-06-20 | 聂聪 | Slow disease health index intelligent monitoring system |
| WO2019132685A1 (en) * | 2017-12-29 | 2019-07-04 | Общество С Ограниченной Ответственностью "Интеллоджик" | Method and system for supporting medical decision making |
| CN109036579A (en) * | 2018-08-22 | 2018-12-18 | 泰康保险集团股份有限公司 | Information forecasting method, device, medium and electronic equipment based on block chain |
| CN109493979A (en) * | 2018-10-23 | 2019-03-19 | 平安科技（深圳）有限公司 | A kind of disease forecasting method and apparatus based on intelligent decision |
| CN109659033A (en) * | 2018-12-18 | 2019-04-19 | 浙江大学 | A kind of chronic disease change of illness state event prediction device based on Recognition with Recurrent Neural Network |
| CN110085323A (en) * | 2019-04-19 | 2019-08-02 | 北京工业大学 | A kind of diagnosis and treatment path discovery method based on electronic health record |
| CN110211690A (en) * | 2019-04-19 | 2019-09-06 | 平安科技（深圳）有限公司 | Disease risks prediction technique, device, computer equipment and computer storage medium |
| CN110277172A (en) * | 2019-06-27 | 2019-09-24 | 齐鲁工业大学 | A kind of clinical application behavior analysis system and its working method based on efficient negative sequence mining mode |
| CN110459324A (en) * | 2019-06-27 | 2019-11-15 | 平安科技（深圳）有限公司 | Disease forecasting method, apparatus and computer equipment based on shot and long term memory models |
| CN110781216A (en) * | 2019-11-05 | 2020-02-11 | 广东工业大学 | Traditional Chinese medicine symptom association rule mining method and device and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 吴本璞: "疾病时间序列研究中预测模型的应用", 浙江医科大学学报, vol. 18, no. 6, pages 277 - 281 * |
| 闫明月: "时间序列相似性与预测算法研究及其应用", 博士电子期刊, pages 1 - 124 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113096756A (en) * | 2021-04-26 | 2021-07-09 | 安徽科大讯飞医疗信息技术有限公司 | Disease evolution classification method and device, electronic equipment and storage medium |
| CN113096756B (en) * | 2021-04-26 | 2023-12-22 | 讯飞医疗科技股份有限公司 | Disease evolution classification method, device, electronic equipment and storage medium |
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