Method and device for automatically generating clinical medical records based on spatiotemporal sequence model

Abstract

The present invention relates to the field of medical information technology, and in particular to a method and device for automatically generating clinical medical records based on a spatiotemporal sequence model, the method comprising: obtaining the diagnosis and treatment features in the diagnosis and treatment records of several diagnosis objects stored historically; constructing a corresponding standard spatiotemporal sequence model; determining the degree of matching between the single diagnosis and treatment features and the standard spatiotemporal sequence model; determining whether the individual spatiotemporal sequence model of the target object needs to be adjusted according to the matching degree; inputting the target time interval corresponding to the target time point into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment features, and generating the clinical medical record corresponding to the target object. The device comprises: a data acquisition module, a model construction module, a model matching module, a model adjustment module, and a record generation module. The present invention can automatically generate clinical diagnosis and treatment records when the target object has no historical diagnosis and treatment records, and can improve the accuracy of automatic generation of clinical medical records.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and in particular to a method and device for automatically generating clinical medical records based on a spatiotemporal sequence model. Background Art

[0002] The construction of smart hospitals is a key step in realizing the "Healthy China" strategy. The construction of smart hospitals is dominated by "digitalization, networking, and informatization", which has successfully broken the limitations of traditional medical services and greatly improved the efficiency and quality of medical services. To achieve the efficient operation of smart hospitals, a series of advanced information systems need to be established. The electronic medical record system is an information system that records the entire process of patient diagnosis and treatment. It can realize the digital storage, query and statistical analysis of medical record information. The hospital records the patient's medical information in an electronic way through the electronic medical record, including: home page, medical history record, examination and test results, doctor's orders, operation records, nursing records, etc., which contain both structured information and unstructured free text, as well as graphic image information. The clinical information system serves clinical activities such as disease diagnosis and treatment, medical research, etc., with patients as the core, and integrates patients' clinical diagnosis and treatment data with the help of various software systems. Its core function is to improve the quality and efficiency of medical process management. The clinical information system is not a single system, but a collection of a series of clinical information systems. Through the clinical information system, medical staff can more conveniently obtain patients' diagnosis and treatment information and related data, and improve the efficiency and accuracy of diagnosis and treatment. In the current series of information systems, although a large amount of patient data is stored in the database, doctors are still required to write medical records after each test result comes out, which cannot effectively improve the utilization efficiency of medical resources.

[0003] The Chinese patent authorization announcement number: CN114242196B discloses a method and device for automatically generating clinical medical records, including: obtaining electronic medical records and corresponding time information; selecting a word segmentation library to segment the electronic medical records according to the diagnosis information to obtain case keywords; using n consecutive sequence data in the spatiotemporal features as training samples and case keywords as combined labels to train the spatiotemporal sequence model to obtain a trained spatiotemporal sequence model; outputting the predicted case keywords of the predicted moment object according to the trained spatiotemporal sequence model and the time interval of the predicted moment to generate clinical medical text.

[0004] It can be seen that the following problems exist in the prior art: although the spatiotemporal sequence model can be trained based on the subject's electronic medical records and case keywords to automatically generate medical record text, the accuracy of the generated medical record text is difficult to guarantee, and when the subject has no historical medical records, the spatiotemporal sequence model cannot be constructed, and thus the clinical medical text cannot be automatically generated. Summary of the invention

[0005] To this end, the present invention provides a method and device for automatically generating clinical medical records based on a spatiotemporal sequence model, so as to overcome the problems in the prior art of low accuracy of automatically generated clinical medical records and inability to automatically generate clinical medical records when there are no historical medical records.

[0006] To achieve the above objectives, on the one hand, the present invention provides a method for automatically generating clinical medical records based on a spatiotemporal sequence model, comprising:

[0007] Step S1, obtaining the diagnosis and treatment characteristics of each diagnosis and treatment record of several diagnosis objects stored in history;

[0008] Step S2, determining a time series data set corresponding to the diagnosis and treatment features with a single type of diagnosis and treatment features, and constructing a standard spatiotemporal series model corresponding to the single type of diagnosis and treatment features;

[0009] Step S3, obtaining the diagnosis and treatment characteristics in each historical diagnosis and treatment record of the target object, and determining the matching degree between the single diagnosis and treatment characteristics and the standard spatiotemporal sequence model;

[0010] Step S4, determining whether the individual spatiotemporal sequence model of the target object needs to be adjusted according to the matching degree;

[0011] The adjusted individual spatiotemporal series model is determined according to the diagnosis and treatment influencing factors; the diagnosis and treatment influencing factors include the target subject's age, gender, weight, severity of related diseases, medication compliance, and body sensitivity;

[0012] Step S5, inputting the target time interval corresponding to the target time point into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment characteristics output by the individual target spatiotemporal sequence model, and generating the clinical medical record corresponding to the target object.

[0013] Furthermore, in step S2, a time series data set corresponding to the diagnosis and treatment features is determined based on the single-type diagnosis and treatment features, and a standard spatiotemporal series model corresponding to the single-type diagnosis and treatment features is constructed, including:

[0014] Step S21, determining a time series data set corresponding to the diagnosis and treatment feature with a single type of diagnosis and treatment feature; the time series data set includes a plurality of time series data; the time series data takes each historical diagnosis and treatment record corresponding to the single type of diagnosis and treatment feature of any diagnosis object as a node, the time interval of the historical diagnosis and treatment record is time information, and the single type of diagnosis and treatment feature is spatial information;

[0015] Step S22, training the initial spatiotemporal sequence model according to the time series data set to obtain a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment feature.

[0016] Furthermore, in step S22, the initial spatiotemporal sequence model is trained according to the time series data set to obtain a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment feature, including:

[0017] Step S221, according to the time series data set, obtain a number of training samples and a sample label corresponding to each training sample; the training sample is n consecutive sequence data in each time series data; the sample label is a single-class diagnosis and treatment feature corresponding to the next node of the n sequence data;

[0018] Step S223, training the initial spatiotemporal sequence model according to the training samples and the sample labels corresponding to each training sample, so as to obtain a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment feature.

[0019] Furthermore, in step S3, the diagnosis and treatment features in each historical diagnosis and treatment record of the target object are obtained, and the matching degree between the single diagnosis and treatment features and the standard spatiotemporal sequence model is determined according to the single diagnosis and treatment features, including:

[0020] Step S31, obtaining the diagnosis and treatment features in each historical diagnosis and treatment record of the target object, so as to obtain a first diagnosis and treatment feature value list corresponding to the diagnosis and treatment feature in each diagnosis and treatment record;

[0021] Step S32, determining a standard spatiotemporal sequence model corresponding to a single diagnosis and treatment feature according to the single diagnosis and treatment feature, and obtaining a second diagnosis and treatment feature value list corresponding to the diagnosis and treatment feature output by the standard spatiotemporal sequence model;

[0022] Step S33, determining the degree of matching between the single diagnosis and treatment characteristics and the standard spatiotemporal sequence model according to the first diagnosis and treatment feature value list and the second diagnosis and treatment feature value list.

[0023] Furthermore, in step S4, determining whether the individual spatiotemporal sequence model of the target object needs to be adjusted according to the matching degree includes:

[0024] Step S41: If the matching degree is less than a preset matching degree threshold, the individual spatiotemporal sequence model of the target object is adjusted.

[0025] Furthermore, in step S4, the adjusted individual spatiotemporal series model is determined according to the diagnosis and treatment influencing factors, including:

[0026] Step S42, determining the weight of each diagnosis and treatment influencing factor according to the target subject's historical diagnosis and treatment records;

[0027] Step S43, screening the diagnosis and treatment influencing factors corresponding to the weights less than the preset weight threshold;

[0028] Step S44, determining the adjusted individual spatiotemporal series model according to the diagnosis and treatment influencing factors corresponding to the weights that are greater than or equal to the preset weight threshold after screening.

[0029] Further, in step S44, the adjusted individual spatiotemporal series model is determined according to the screened diagnosis and treatment influencing factors, including:

[0030] Step S441, adjusting the diagnosis and treatment feature value corresponding to the diagnosis and treatment feature according to the screened diagnosis and treatment influencing factor;

[0031] Step S442, determining a corresponding standard spatiotemporal sequence model according to the adjusted diagnosis and treatment characteristic value;

[0032] Step S443, adjusting the standard space-time sequence model according to the single diagnosis and treatment characteristics to obtain an adjusted individual space-time sequence model.

[0033] Furthermore, in step S5, the target time interval corresponding to the target time point is input into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment features output by the individual target spatiotemporal sequence model, and generate the clinical medical record corresponding to the target object, including:

[0034] Step S51, obtaining a target detection report of the target object at a target time point;

[0035] Step S52, inputting the target time interval corresponding to the target time point into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment characteristics corresponding to the target time point;

[0036] Step S53, generating a clinical medical record corresponding to the target object according to the target detection report and the target diagnosis and treatment characteristics.

[0037] Furthermore, in step S53, a clinical medical record corresponding to the target object is generated according to the target detection report and the target diagnosis and treatment characteristics, including:

[0038] The test results corresponding to the target test report are compared with the target diagnosis and treatment characteristics. If they match, a clinical medical record corresponding to the target object is generated.

[0039] On the other hand, the present invention also provides a clinical medical record automatic generation device based on a spatiotemporal sequence model, comprising:

[0040] A data acquisition module, which is used to obtain the diagnosis and treatment characteristics of each diagnosis and treatment record of several diagnosis objects stored historically;

[0041] A model building module, which is connected to the data acquisition model and is used to determine the time series data set corresponding to the diagnosis and treatment characteristics based on the single-type diagnosis and treatment characteristics to build a standard spatiotemporal series model corresponding to the single-type diagnosis and treatment characteristics;

[0042] A model matching module is used to obtain the diagnosis and treatment characteristics of each historical diagnosis and treatment record of the target object, and determine the degree of matching between the single diagnosis and treatment characteristics and the corresponding standard spatiotemporal sequence model;

[0043] A model adjustment module, which is used to determine whether the individual spatiotemporal sequence model of the target object needs to be adjusted based on the matching degree, determine the diagnosis and treatment influence factors used for model adjustment based on the historical diagnosis and treatment records of the target object, and determine the adjusted individual spatiotemporal sequence model based on the determined diagnosis and treatment influence factors used to adjust the model;

[0044] The diagnosis and treatment influencing factors include the target subject's age, gender, weight, severity of related diseases, medication compliance, and body sensitivity;

[0045] A record generation module is used to determine the output target diagnosis and treatment characteristics based on the target time point and the individual target spatiotemporal sequence model, and generate a clinical medical record corresponding to the target object.

[0046] Compared with the prior art, the beneficial effect of the present invention lies in that, by acquiring the diagnosis and treatment characteristics in the diagnosis and treatment records of several diagnosis objects stored historically, the time series data set corresponding to the diagnosis and treatment characteristics is determined by a single type of diagnosis and treatment characteristics, and a standard spatiotemporal sequence model corresponding to a single type of diagnosis and treatment characteristics is constructed, thereby utilizing the diagnosis and treatment characteristics of different diagnosis objects stored historically to construct a standard spatiotemporal sequence model corresponding to each type of diagnosis and treatment characteristics, and when the diagnosis object has no historical diagnosis and treatment records, clinical medical records can be automatically generated according to the standard spatiotemporal sequence model. The diagnosis and treatment characteristics in the historical diagnosis and treatment records of the target object are acquired, and the degree of matching between the single diagnosis and treatment characteristics and the standard spatiotemporal sequence model is determined according to the degree of matching, and whether the individual spatiotemporal sequence model of the target object needs to be adjusted is determined according to the matching degree. If adjustment is required, the adjusted individual spatiotemporal sequence model is determined according to the diagnosis and treatment influencing factors. When the target object has a medical record, the degree of matching between the target object and the standard spatiotemporal sequence model is determined according to the single medical feature. If the matching degree is high, no adjustment is required, and the standard spatiotemporal sequence model can be directly used as the individual spatiotemporal sequence model corresponding to the target object to automatically generate the clinical medical record corresponding to the target object, which can improve the generation efficiency of the clinical medical record; if the matching degree is low, it is necessary to adjust according to the medical treatment influencing factor. The low matching degree indicates that the medical treatment characteristics of the target object do not conform to the general rules and are affected by other factors. Adjustment according to the medical treatment influencing factor can improve the generation accuracy of the clinical medical record. The target time interval corresponding to the target time point is input into the individual target spatiotemporal sequence model to obtain the target medical treatment characteristics output by the individual target spatiotemporal sequence model, and generate the clinical medical record corresponding to the target object. The present invention generates a standard spatiotemporal sequence model corresponding to a single type of medical treatment characteristics through the medical treatment records of different diagnostic objects stored in history, and can automatically generate clinical medical records when the target object has no medical treatment records. In addition, the target object can be matched with the standard spatiotemporal sequence model according to each historical medical treatment record of the target object, so as to construct an individual target spatiotemporal sequence model that is more suitable for the target object, and improve the accuracy of the automatic generation of clinical medical records.

[0047] Furthermore, by constructing a standard spatiotemporal series model corresponding to each single type of diagnosis and treatment feature based on the time series data set corresponding to each single type of diagnosis and treatment feature, the corresponding clinical medical records can be generated in a more targeted manner.

[0048] Furthermore, by constructing corresponding time series data sets based on the historical diagnosis and treatment records of different diagnosis objects with the same single-category diagnosis and treatment characteristics, and obtaining training samples and sample labels, the number of training samples can be expanded, thereby improving the generalization ability of the model.

[0049] Furthermore, by obtaining a list of first diagnosis and treatment feature values ​​corresponding to the diagnosis and treatment features in each diagnosis and treatment record and a list of second diagnosis and treatment feature values ​​corresponding to the diagnosis and treatment features output by the standard space-time sequence model, the degree of matching between a single diagnosis and treatment feature and the standard space-time sequence model can be quickly determined.

[0050] Furthermore, the degree of match between the single diagnosis and treatment characteristics and the standard space-time sequence model is low, indicating that the single diagnosis and treatment characteristics of the target object are not suitable for the corresponding standard space-time sequence model, and clinical medical records cannot be automatically generated based on this. Therefore, the individual space-time sequence model of the target object needs to be adjusted.

[0051] Furthermore, the degree of influence of the diagnosis and treatment influencing factors corresponding to the target object on the single diagnosis and treatment characteristics is not necessarily the same. It is necessary to determine the weight of each diagnosis and treatment influencing factor based on the historical diagnosis and treatment records of the target object, which can improve the accuracy of subsequent adjustments. In addition, the weights less than the preset weight threshold have a relatively small impact on the diagnosis and treatment characteristics. Screening them and determining the adjusted individual spatiotemporal series model based on the screened diagnosis and treatment influencing factors can improve the efficiency of model adjustment.

[0052] Furthermore, by adjusting the diagnosis and treatment feature values ​​corresponding to the diagnosis and treatment features according to the screened diagnosis and treatment influencing factors, the corresponding standard spatiotemporal sequence model is determined, and then the standard spatiotemporal sequence model is adjusted according to the single diagnosis and treatment features to obtain the adjusted individual spatiotemporal sequence model. Thus, the adjusted individual spatiotemporal sequence model is more closely matched with the diagnosis and treatment features of the target object, and the accuracy of the generated clinical medical records can be improved.

[0053] Furthermore, combined with the target detection report of the target object, it can assist medical workers in making judgments, making the generated clinical medical records more accurate and comprehensive.

[0054] Furthermore, the test results corresponding to the target detection report are compared with the target diagnosis and treatment characteristics, and a clinical medical record corresponding to the target object is generated when they match. The target detection report is used to assist in determining the accuracy of the target diagnosis and treatment characteristics, which can improve the accuracy of the generated clinical medical record.

[0055] Furthermore, the device of the present invention can construct a standard space-time sequence model corresponding to a single type of diagnosis and treatment characteristics by setting up a data acquisition module, a model construction module, a model matching module, a model adjustment module, and a record generation module. When the target object has no historical diagnosis and treatment records, the target diagnosis and treatment characteristics output by the standard space-time sequence model are used to generate clinical medical records. When the target object has historical diagnosis and treatment records, an individual space-time sequence model can be constructed based on the target object's historical diagnosis and treatment characteristics and the standard space-time sequence model to generate clinical medical records, thereby improving the accuracy of automatically generated clinical medical records. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of the steps of the method for automatically generating clinical medical records based on a spatiotemporal sequence model provided by the present invention;

[0057] Figure 2 A schematic diagram of the structure of a device for automatically generating clinical medical records based on a spatiotemporal sequence model provided by the present invention;

[0058] Figure 3 It is a step-by-step schematic diagram of step S4 of the present invention;

[0059] Figure 4 It is a step-by-step schematic diagram of step S44 of the present invention. DETAILED DESCRIPTION

[0060] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0062] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0063] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0064] See also Figure 1 As shown, it is a flow chart of a method for automatically generating clinical medical records based on a spatiotemporal sequence model provided by the present invention. A method for automatically generating clinical medical records based on a spatiotemporal sequence model provided by the present invention comprises:

[0065] Step S1, obtaining the diagnosis and treatment characteristics in the diagnosis and treatment records of several diagnosis objects stored historically.

[0066] Specifically, the information system of the medical institution stores the historical records of the entire medical process of all diagnosed objects, including all historical diagnosis and treatment records and the time information corresponding to each diagnosis and treatment record. The diagnosis result in each diagnosis and treatment record has corresponding characteristics. If the diagnosis results of the same disease are different, the diagnosis and treatment characteristics may be different. In the actual application process, the diagnosis and treatment characteristics corresponding to the diagnosis and treatment record can be set according to the preset diagnosis information rules.

[0067] Step S2, determining the time series data set corresponding to the diagnosis and treatment characteristics with the single-category diagnosis and treatment characteristics, and constructing a standard spatiotemporal series model corresponding to the single-category diagnosis and treatment characteristics.

[0068] Step S3, obtaining the diagnosis and treatment features in the target object's historical diagnosis and treatment records, and determining the degree of matching between the single diagnosis and treatment features and the standard spatiotemporal sequence model.

[0069] Step S4, determining whether the individual spatiotemporal sequence model of the target object needs to be adjusted according to the matching degree;

[0070] Among them, the adjusted individual spatiotemporal series model is determined according to the diagnosis and treatment influencing factors; the diagnosis and treatment influencing factors include the target object's age, gender, weight, severity of related diseases, medication compliance, and body sensitivity.

[0071] Step S5, inputting the target time interval corresponding to the target time point into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment characteristics output by the individual target spatiotemporal sequence model, and generating the clinical medical record corresponding to the target object.

[0072] The present invention obtains the diagnosis and treatment characteristics in the diagnosis and treatment records of several diagnosis objects stored in history, determines the time series data set corresponding to the diagnosis and treatment characteristics with a single type of diagnosis and treatment characteristics, and constructs a standard spatiotemporal sequence model corresponding to the single type of diagnosis and treatment characteristics, thereby using the diagnosis and treatment characteristics of different diagnosis objects stored in history to construct a standard spatiotemporal sequence model corresponding to each type of diagnosis and treatment characteristics, and can automatically generate clinical medical records according to the standard spatiotemporal sequence model when there is no historical diagnosis and treatment record for the diagnosis object. The diagnosis and treatment characteristics in the historical diagnosis and treatment records of the target object are obtained, and the degree of matching between the single diagnosis and treatment characteristics and the standard spatiotemporal sequence model is determined according to the degree of matching, and whether the individual spatiotemporal sequence model of the target object needs to be adjusted is determined according to the matching degree. If adjustment is required, the adjusted individual spatiotemporal sequence model is determined according to the diagnosis and treatment influencing factors. When the target object has a medical record, the degree of matching between the target object and the standard spatiotemporal sequence model is determined according to the single medical feature. If the matching degree is high, no adjustment is required, and the standard spatiotemporal sequence model can be directly used as the individual spatiotemporal sequence model corresponding to the target object to automatically generate the clinical medical record corresponding to the target object, which can improve the generation efficiency of the clinical medical record; if the matching degree is low, it is necessary to adjust according to the medical treatment influencing factor. The low matching degree indicates that the medical treatment characteristics of the target object do not conform to the general rules and are affected by other factors. Adjustment according to the medical treatment influencing factor can improve the generation accuracy of the clinical medical record. The target time interval corresponding to the target time point is input into the individual target spatiotemporal sequence model to obtain the target medical treatment characteristics output by the individual target spatiotemporal sequence model, and generate the clinical medical record corresponding to the target object. The present invention generates a standard spatiotemporal sequence model corresponding to a single type of medical treatment characteristics through the medical treatment records of different diagnostic objects stored in history, and can automatically generate clinical medical records when the target object has no medical treatment records. In addition, the target object can be matched with the standard spatiotemporal sequence model according to each historical medical treatment record of the target object, so as to construct an individual target spatiotemporal sequence model that is more suitable for the target object, and improve the accuracy of the automatic generation of clinical medical records.

[0073] Specifically, in step S2, a time series data set corresponding to the diagnosis and treatment features is determined based on the single-category diagnosis and treatment features, and a standard spatiotemporal series model corresponding to the single-category diagnosis and treatment features is constructed, including:

[0074] Step S21, determine the time series data set corresponding to the diagnosis and treatment characteristics with a single type of diagnosis and treatment characteristics; the time series data set includes a plurality of time series data; the time series data takes each historical diagnosis and treatment record corresponding to the single type of diagnosis and treatment characteristics of any diagnostic object as a node, the time interval of the historical diagnosis and treatment records is time information, and the single type of diagnosis and treatment characteristics is spatial information.

[0075] Step S22, training the initial spatiotemporal sequence model according to the time series data set to obtain a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment feature.

[0076] It should be noted that those skilled in the art should be aware that any spatiotemporal sequence model in the prior art, such as ConovLSTM, predRNN, etc., falls within the protection scope of the present invention and will not be described in detail here.

[0077] In implementation, the single-category diagnosis and treatment feature includes at least one disease progression stage feature corresponding to the disease type and at least one data feature. For example, the single-category diagnosis and treatment feature is determined to be hypertension, and its corresponding single-category diagnosis and treatment feature includes the hypertension grade corresponding to the current node (disease progression stage feature) and the blood pressure value corresponding to the current node (systolic blood pressure data and diastolic blood pressure data). For another example, the single-category diagnosis and treatment feature is gastric ulcer, and its corresponding single-category diagnosis and treatment feature includes the gastric ulcer grade corresponding to the current node (disease progression stage feature) and the ulcer surface data (data feature) corresponding to the current node.

[0078] In the present invention, a standard spatiotemporal sequence model corresponding to each single type of diagnosis and treatment feature is constructed according to the time series data set corresponding to each single type of diagnosis and treatment feature, so that the corresponding clinical medical records can be generated more specifically.

[0079] Specifically, each historical diagnosis and treatment record of each diagnosed object corresponding to the single type of diagnosis and treatment feature in the time series data set is converted into a node sequence. There is a certain interval between the nodes, and each node is described by the diagnosis and treatment feature, so each time series data obtained has a three-dimensional feature.

[0080] Specifically, in step S22, the initial spatiotemporal sequence model is trained according to the time series data set to obtain a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment feature, including:

[0081] Step S221, according to the time series data set, obtain a number of training samples and a sample label corresponding to each training sample; the training sample is n consecutive sequence data in each time series data; the sample label is a single-category diagnosis and treatment feature corresponding to the next node of the n sequence data.

[0082] Step S223, training the initial spatiotemporal sequence model according to the training samples and the sample labels corresponding to each training sample, so as to obtain a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment feature.

[0083] It is understandable that since the training samples obtained come from different diagnostic objects, their diagnosis and treatment records may include multiple types of diagnosis and treatment characteristics. As a method for establishing a standard spatiotemporal sequence model, it is preferred that the diagnosis and treatment characteristics of the diagnostic objects with only a single type of diagnosis and treatment characteristics are first selected as samples for data training. When the sample size of the diagnostic objects with only a single type of diagnosis and treatment characteristics is less than the basic sample size for model training or the diagnosis and treatment characteristics of the samples of the diagnostic objects with only a single type of diagnosis and treatment characteristics cannot cover all stages of disease progression, the diagnosis objects with large differences in the two types of diagnosis and treatment characteristics, or the diagnosis and treatment characteristics of the diagnostic objects with one of the two types of diagnosis and treatment characteristics unchanged, are selected as sample data for training the standard spatiotemporal sequence model.

[0084] In the present invention, a corresponding time series data set is constructed according to the historical diagnosis and treatment records of different diagnosis objects with the same single-category diagnosis and treatment characteristics, and training samples and sample labels are obtained, which can expand the number of training samples and thus improve the generalization ability of the model.

[0085] Specifically, in step S3, the diagnosis and treatment features in each historical diagnosis and treatment record of the target object are obtained, and the matching degree between the single diagnosis and treatment features and the standard spatiotemporal sequence model is determined according to the single diagnosis and treatment features, including:

[0086] Step S31, obtaining the diagnosis and treatment features in each historical diagnosis and treatment record of the target object, so as to obtain a first diagnosis and treatment feature value list corresponding to the diagnosis and treatment features in each diagnosis and treatment record.

[0087] Step S32, determining a standard space-time sequence model corresponding to a single diagnosis and treatment feature according to the single diagnosis and treatment feature, and obtaining a second diagnosis and treatment feature value list corresponding to the diagnosis and treatment feature output by the standard space-time sequence model.

[0088] Step S33, determining the degree of matching between the single diagnosis and treatment characteristics and the standard spatiotemporal sequence model according to the first diagnosis and treatment feature value list and the second diagnosis and treatment feature value list.

[0089] In implementation, each disease progression stage feature and data feature corresponding to a single type of diagnosis and treatment feature can form a diagnosis and treatment feature vector corresponding to a single node, and the diagnosis and treatment feature value is determined according to the spatial position of the formed diagnosis and treatment feature vector.

[0090] In a specific embodiment, determining the matching degree MP between the single diagnosis and treatment characteristics and the standard spatiotemporal sequence model includes:

[0091] The first diagnosis and treatment characteristic value list YB=(YB1,YB2,…,YB j , …, YB m ), the second diagnosis and treatment feature value list EB = (EB1, EB2, ..., EB j , …, EB m ), among which, YBj is the jth diagnosis and treatment feature value corresponding to the diagnosis and treatment feature in the historical diagnosis and treatment record of the target object, EB j is the j-th diagnosis and treatment feature value of the diagnosis and treatment feature output by the standard space-time series model, j = 1, 2, ..., m; m is the number of diagnosis and treatment feature values.

[0092] Then the matching degree MP = ∑ m j＝1 YB j ×EB j / (sqrt(∑ m j＝1 (YB j ) 2 )×sqrt(∑ m j＝1 (EB j ) 2 )), sqrt() is the preset square root determination function.

[0093] Furthermore, by obtaining a list of first diagnosis and treatment feature values ​​corresponding to the diagnosis and treatment features in each diagnosis and treatment record and a list of second diagnosis and treatment feature values ​​corresponding to the diagnosis and treatment features output by the standard space-time sequence model, the degree of matching between a single diagnosis and treatment feature and the standard space-time sequence model can be quickly determined.

[0094] See also Figure 3 Specifically, in step S4, determining whether the individual spatiotemporal sequence model of the target object needs to be adjusted according to the matching degree includes:

[0095] Step S41: If the matching degree is less than a preset matching degree threshold, the individual spatiotemporal sequence model of the target object is adjusted.

[0096] Specifically, in the actual application process, the actual implementer can set the preset matching degree threshold according to the actual situation. The larger the value of the preset matching degree threshold, the higher the requirement for the adaptability between the standard spatiotemporal sequence model and the target object. Preferably, the value range of the preset matching degree threshold can be set to between 0.7 and 0.9.

[0097] The matching degree between the single diagnosis and treatment characteristics and the standard space-time sequence model in the present invention is small, indicating that the single diagnosis and treatment characteristics of the target object are not suitable for the corresponding standard space-time sequence model, and clinical medical records cannot be automatically generated based on this. Therefore, it is necessary to adjust the individual space-time sequence model of the target object.

[0098] Please continue reading Figure 3 As shown, specifically, in step S4, the adjusted individual spatiotemporal series model is determined according to the diagnosis and treatment influencing factors, including:

[0099] Step S42, determining the weight of each diagnosis and treatment influencing factor according to the historical diagnosis and treatment records of the target object.

[0100] Step S43, screening the diagnosis and treatment influencing factors corresponding to the weights less than the preset weight threshold.

[0101] Step S44, determining the adjusted individual spatiotemporal series model according to the diagnosis and treatment influencing factors corresponding to the weights that are greater than or equal to the preset weight threshold after screening.

[0102] Specifically, the diagnosis and treatment influencing factors for each diagnosis object may be different. In actual application, they need to be determined based on the diagnosis and treatment information of the diagnosis object.

[0103] In a specific embodiment, a comparison table of the weights of the diagnosis and treatment influencing factors for each type of diagnosis and treatment feature may be constructed. For example, the weight value range of the diagnosis and treatment influencing factors for hypertension may be set to:

[0104] The weight corresponding to age is 0.2-0.5 (under 25 years old: 0.2-0.25, 26-36 years old: 0.25-0.35, 37-50 years old: 0.3-0.4, 51-65 years old: 0.4-0.45, over 66 years old: 0.45-0.5);

[0105] The weight corresponding to gender is 0.05-0.1 (female: 0.05-0.08, male: 0.06-0.1);

[0106] The weight corresponding to body weight is 0.2-0.4 (including, below 50kg: 0.2-0.25, 50kg-70kg: 0.22-0.32, 70kg-90kg: 0.3-0.8, above 90kg: 0.35-0.4);

[0107] The weight corresponding to the severity of the associated disease is 0.4-0.5 (wherein, the severity of the associated disease is mild: 0.4-0.43, the severity of the associated disease is moderate: 0.42-0.46, and the severity of the associated disease is severe: 0.45-0.5);

[0108] The weight corresponding to medication compliance is 0.15-0.35 (among which, excellent medication compliance: 0.15-0.2, good medication compliance: 0.2-0.3, and average medication compliance: 0.3-0.35);

[0109] The weight corresponding to the body sensitivity is 0.15-0.25 (good body sensitivity: 0.15-0.18, average body sensitivity: 0.18-0.22, poor body sensitivity: 0.22-0.25);

[0110] In addition, the weight value corresponding to the genetic factor can be set to 0.25-0.35, and the weight value corresponding to the psychological factor can be set to 0.1-0.2.

[0111] Specifically, in the actual application process, the actual implementers can set the preset weight threshold based on the expert system according to the actual situation of various diagnosis and treatment characteristics. Generally, the value range of the preset weight threshold can be set to 0.2-0.35.

[0112] In the present invention, the degree of influence of the diagnosis and treatment influencing factors corresponding to the target object on the single diagnosis and treatment characteristics is not necessarily the same. It is necessary to determine the weight of each diagnosis and treatment influencing factor based on the historical diagnosis and treatment records of the target object, which can improve the accuracy of subsequent adjustments. In addition, the weights less than the preset weight threshold have a relatively small impact on the diagnosis and treatment characteristics, and they are screened and eliminated, which reduces the complexity of model construction. The adjusted individual spatiotemporal series model is determined based on the screened diagnosis and treatment influencing factors, which can improve the efficiency of model adjustment.

[0113] See also Figure 4 As shown, specifically, in step S44, the adjusted individual spatiotemporal series model is determined according to the screened diagnosis and treatment influencing factors, including:

[0114] Step S441, adjusting the diagnosis and treatment feature value corresponding to the diagnosis and treatment feature according to the screened diagnosis and treatment influencing factor.

[0115] In a specific embodiment, the weights of the screening diagnosis and treatment influencing factors are ZL1, ZL2, ..., ZL i , …, ZL n , i=1,2,…,n,ZL i is the i-th diagnosis and treatment influencing factor after screening, n is the number of diagnosis and treatment influencing factors after screening, and the diagnosis and treatment characteristic values ​​corresponding to the diagnosis and treatment characteristics before adjustment are T1, T2, …, T k ,…,T q , k = 1, 2, ..., q, q is the number of diagnosis and treatment characteristic values ​​corresponding to the diagnosis and treatment characteristics, then the adjusted diagnosis and treatment characteristic values ​​are TL1, TL2, ..., TL k ,…,TL q , where TL k =T k ×(1-ZL1×ZL2×…×ZL i ×…×ZL n ).

[0116] Step S442, determining the corresponding standard space-time series model according to the adjusted diagnosis and treatment characteristic value.

[0117] Specifically, the corresponding diagnostic and treatment characteristics are redetermined according to the adjusted diagnostic and treatment characteristic values ​​(in actual applications, a diagnostic and treatment characteristic value comparison table of various diagnostic and treatment characteristics is constructed, and the diagnostic and treatment characteristic value of each diagnostic and treatment characteristic has its own value range, which can be determined based on expert knowledge), so as to redetermine the corresponding standard space-time series model.

[0118] Step S443, adjusting the standard space-time sequence model according to the single diagnosis and treatment characteristics to obtain an adjusted individual space-time sequence model.

[0119] Furthermore, by adjusting the diagnosis and treatment feature values ​​corresponding to the diagnosis and treatment features according to the screened diagnosis and treatment influencing factors, the corresponding standard spatiotemporal sequence model is determined, and then the standard spatiotemporal sequence model is adjusted according to the single diagnosis and treatment features to obtain the adjusted individual spatiotemporal sequence model. Thus, the adjusted individual spatiotemporal sequence model is more closely matched with the diagnosis and treatment features of the target object, and the accuracy of the generated clinical medical records can be improved.

[0120] Specifically, in step S5, the target time interval corresponding to the target time point is input into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment features output by the individual target spatiotemporal sequence model, and generate the clinical medical record corresponding to the target object, including:

[0121] Step S51, obtaining a target detection report of the target object at a target time point.

[0122] Step S52, inputting the target time interval corresponding to the target time point into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment characteristics corresponding to the target time point.

[0123] Step S53, generating a clinical medical record corresponding to the target object according to the target detection report and the target diagnosis and treatment characteristics.

[0124] Furthermore, combined with the target detection report of the target object, it can assist medical workers in making judgments, making the generated clinical medical records more accurate and comprehensive.

[0125] Specifically, in step S53, a clinical medical record corresponding to the target object is generated according to the target detection report and the target diagnosis and treatment characteristics, including:

[0126] The test results corresponding to the target test report are compared with the target diagnosis and treatment characteristics. If they match, a clinical medical record corresponding to the target object is generated.

[0127] In the present invention, if the degree of matching between the diagnosis and treatment feature value corresponding to the target diagnosis and treatment feature and the diagnosis and treatment feature value corresponding to the target detection report exceeds a preset matching degree threshold, it is determined to be consistent.

[0128] Furthermore, the test results corresponding to the target detection report are compared with the target diagnosis and treatment characteristics, and a clinical medical record corresponding to the target object is generated when they match. The target detection report is used to assist in determining the accuracy of the target diagnosis and treatment characteristics, which can improve the accuracy of the generated clinical medical record.

[0129] See also Figure 2 The present invention also provides a clinical medical record automatic generation device based on a spatiotemporal sequence model, comprising:

[0130] The data acquisition module is used to obtain the diagnosis and treatment characteristics in the historically stored diagnosis and treatment records of several diagnosis objects.

[0131] A model building module is connected to the data acquisition model and is used to determine the time series data set corresponding to the diagnosis and treatment characteristics based on the single-category diagnosis and treatment characteristics to build a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment characteristics.

[0132] The model matching module is used to obtain the diagnosis and treatment characteristics in the historical diagnosis and treatment records of the target object, and determine the degree of matching between the single diagnosis and treatment characteristics and the corresponding standard spatiotemporal sequence model.

[0133] A model adjustment module is used to determine whether the individual spatiotemporal sequence model of the target object needs to be adjusted based on the degree of matching, determine the diagnosis and treatment influencing factors used for model adjustment based on the historical diagnosis and treatment records of the target object, and determine the adjusted individual spatiotemporal sequence model based on the determined diagnosis and treatment influencing factors used to adjust the model.

[0134] Among them, the diagnosis and treatment influencing factors include the target object's age, gender, weight, severity of related diseases, medication compliance, and body sensitivity.

[0135] A record generation module is used to determine the output target diagnosis and treatment characteristics based on the target time point and the individual target spatiotemporal sequence model, and generate a clinical medical record corresponding to the target object.

[0136] The device of the present invention can construct a standard space-time sequence model corresponding to a single type of diagnosis and treatment characteristics by setting a data acquisition module, a model construction module, a model matching module, a model adjustment module, and a record generation module. When the target object has no historical diagnosis and treatment records, the target diagnosis and treatment characteristics output by the standard space-time sequence model are used to generate clinical medical records. When the target object has historical diagnosis and treatment records, an individual space-time sequence model can be constructed based on the target object's historical diagnosis and treatment characteristics and the standard space-time sequence model to generate clinical medical records, thereby improving the accuracy of automatically generated clinical medical records.

[0137] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for automatically generating clinical medical records based on a spatiotemporal sequence model, characterized in that: include: Step S1, obtaining the diagnosis and treatment characteristics of each diagnosis and treatment record of several diagnosis objects stored in history; Step S2, determining a time series data set corresponding to the diagnosis and treatment features with a single type of diagnosis and treatment features, and constructing a standard spatiotemporal series model corresponding to the single type of diagnosis and treatment features; The step S2 includes: Step S21, determining a time series data set corresponding to the diagnosis and treatment feature with a single type of diagnosis and treatment feature; the time series data set includes a plurality of time series data; the time series data takes each historical diagnosis and treatment record corresponding to the single type of diagnosis and treatment feature of any diagnosis object as a node, the time interval of the historical diagnosis and treatment record is time information, and the single type of diagnosis and treatment feature is spatial information; Step S22, training the initial spatiotemporal sequence model according to the time series data set to obtain a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment feature; The step S22 comprises: Step S221, according to the time series data set, obtain a number of training samples and a sample label corresponding to each training sample; the training sample is n consecutive sequence data in each time series data; the sample label is a single-class diagnosis and treatment feature corresponding to the next node of the n sequence data; Step S223, training the initial spatiotemporal sequence model according to the training samples and the sample labels corresponding to each training sample, so as to obtain a standard spatiotemporal sequence model corresponding to the single-category diagnosis and treatment feature; Step S3, obtaining the diagnosis and treatment characteristics in each historical diagnosis and treatment record of the target object, and determining the matching degree between the single diagnosis and treatment characteristics and the standard spatiotemporal sequence model; Step S4, determining whether the individual spatiotemporal sequence model of the target object needs to be adjusted according to the matching degree; The adjusted individual spatiotemporal series model is determined according to the diagnosis and treatment influencing factors; the diagnosis and treatment influencing factors include the target subject's age, gender, weight, severity of related diseases, medication compliance, and body sensitivity; In step S4, the adjusted individual spatiotemporal series model is determined according to the diagnosis and treatment influencing factors, including: Step S42, determining the weight of each diagnosis and treatment influencing factor according to the target subject's historical diagnosis and treatment records; Step S43, screening the diagnosis and treatment influencing factors corresponding to the weights less than the preset weight threshold; Step S44, determining an adjusted individual spatiotemporal series model according to the diagnosis and treatment influencing factors corresponding to the weights greater than or equal to the preset weight threshold after screening; The step S44 comprises: Step S441, adjusting the diagnosis and treatment feature value corresponding to the diagnosis and treatment feature according to the screened diagnosis and treatment influencing factor; Step S442, determining a corresponding standard spatiotemporal sequence model according to the adjusted diagnosis and treatment characteristic value; Step S443, adjusting the standard spatiotemporal sequence model according to the single diagnosis and treatment characteristics to obtain an adjusted individual spatiotemporal sequence model; Step S5, inputting the target time interval corresponding to the target time point into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment characteristics output by the individual target spatiotemporal sequence model, and generating the clinical medical record corresponding to the target object.

2. The method for automatically generating clinical medical records based on a spatiotemporal sequence model according to claim 1, characterized in that: The step S3 comprises: Step S31, obtaining the diagnosis and treatment features in each historical diagnosis and treatment record of the target object, so as to obtain a first diagnosis and treatment feature value list corresponding to the diagnosis and treatment feature in each diagnosis and treatment record; Step S32, determining a standard spatiotemporal sequence model corresponding to a single diagnosis and treatment feature according to the single diagnosis and treatment feature, and obtaining a second diagnosis and treatment feature value list corresponding to the diagnosis and treatment feature output by the standard spatiotemporal sequence model; Step S33, determining the degree of matching between the single diagnosis and treatment characteristics and the standard spatiotemporal sequence model according to the first diagnosis and treatment feature value list and the second diagnosis and treatment feature value list.

3. The method for automatically generating clinical medical records based on a spatiotemporal sequence model according to claim 1, characterized in that: In step S4, determining whether the individual spatiotemporal sequence model of the target object needs to be adjusted according to the matching degree includes: Step S41: If the matching degree is less than a preset matching degree threshold, the individual spatiotemporal sequence model of the target object is adjusted.

4. The method for automatically generating clinical medical records based on a spatiotemporal sequence model according to claim 3, characterized in that: The step S5 comprises: Step S51, obtaining a target detection report of the target object at a target time point; Step S52, inputting the target time interval corresponding to the target time point into the individual target spatiotemporal sequence model to obtain the target diagnosis and treatment characteristics corresponding to the target time point; Step S53, generating a clinical medical record corresponding to the target object according to the target detection report and the target diagnosis and treatment characteristics.

5. The method for automatically generating clinical medical records based on a spatiotemporal sequence model according to claim 4, characterized in that: The step S53 comprises: The test results corresponding to the target test report are compared with the target diagnosis and treatment characteristics. If they match, a clinical medical record corresponding to the target object is generated.

6. A clinical medical record automatic generation device based on a spatiotemporal sequence model applied to the method described in any one of claims 1 to 5, characterized in that: The device comprises: A data acquisition module, which is used to obtain the diagnosis and treatment characteristics of each diagnosis and treatment record of several diagnosis objects stored historically; A model building module, which is connected to the data acquisition model and is used to determine the time series data set corresponding to the diagnosis and treatment characteristics based on the single-type diagnosis and treatment characteristics to build a standard spatiotemporal series model corresponding to the single-type diagnosis and treatment characteristics; A model matching module is used to obtain the diagnosis and treatment characteristics of each historical diagnosis and treatment record of the target object, and determine the degree of matching between the single diagnosis and treatment characteristics and the corresponding standard spatiotemporal sequence model; A model adjustment module, which is used to determine whether the individual spatiotemporal sequence model of the target object needs to be adjusted based on the matching degree, determine the diagnosis and treatment influence factors used for model adjustment based on the historical diagnosis and treatment records of the target object, and determine the adjusted individual spatiotemporal sequence model based on the determined diagnosis and treatment influence factors used to adjust the model; The diagnosis and treatment influencing factors include the target subject's age, gender, weight, severity of related diseases, medication compliance, and body sensitivity; A record generation module is used to determine the output target diagnosis and treatment characteristics based on the target time point and the individual target spatiotemporal sequence model, and generate a clinical medical record corresponding to the target object.

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