Electronic information quality management and control system for intelligent clinical nursing

Abstract

The invention relates to the technical field of medical information processing, in particular to an electronic information quality management and control system for intelligent clinical nursing, and the system comprises a data collection module which is used for obtaining a plurality of nursing data of each patient. The data analysis module is used for obtaining a first category of the reference data; obtaining a generalization interval of the target data of each patient and a second category of the target data according to the first category of the reference data, and obtaining the generalization interval of the target data of each patient and the second category of the target data again according to the second category of the reference data; and abnormal data in the second category. And the quality control module is used for judging the quality of the target data of the new patient according to the abnormal data in the second category and the generalization interval of the abnormal data. According to the method, the quality of the target data of the newly-added patient is accurately judged in time, meanwhile, the influence of noise caused by individual differences of patients is further avoided, and finally the electronic information quality of clinical nursing is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information processing, and in particular to an electronic information quality control system for intelligent clinical nursing. Background Art

[0002] In the modern medical system, with the popularization of electronic medical records (EMR) and electronic health record (EHR) systems, the lack of information quality has gradually emerged. In clinical care, complete and accurate patient information is essential for intelligent clinical care (for example, it is essential for formulating the right care plan). However, many current medical information systems face the following problems in data recording and management: key information such as patient history, diagnosis, and treatment records are often incomplete; the integration of multi-source data is improper, and patient information recorded by different medical personnel may be conflicting or incorrect, and the clinical care or treatment process is unreasonable. These problems directly affect the quality of electronic information data for clinical care, and low-quality electronic information data is difficult to discover in time, resulting in unreliable and low-quality recorded electronic information data for clinical care. In order to give full play to the role of electronic information data for clinical care in intelligent applications, it is necessary to conduct quality control on the electronic information data for clinical care directly recorded to improve the quality of electronic information data for clinical care. Summary of the invention

[0003] In order to solve the above-mentioned problems of unreliable and low-quality electronic information data of clinical nursing, the present invention provides an electronic information quality control system for intelligent clinical nursing.

[0004] The electronic information quality control system for intelligent clinical nursing of the present invention adopts the following technical solutions: An embodiment of the present invention provides an electronic information quality control system for intelligent clinical nursing, the system comprising the following modules: The data collection module is used to obtain several types of nursing data for each patient, including: disease details, medication data, and nursing monitoring results; The data analysis module is used to record any kind of nursing data as target data; the nursing data other than the target data are recorded as reference data and sample data in sequence. When the target data is disease details, the reference data and sample data are medication data and nursing monitoring results, respectively; when the target data is medication data, the reference data and sample data are disease details and nursing monitoring results, respectively; when the target data is nursing monitoring results, the reference data and sample data are disease details and medication data, respectively; the reference data of all patients are clustered to obtain the first category of the reference data; Obtaining the generalization interval of target data of each patient and the second category of target data according to the first category of reference data, including: obtaining a number of most similar data of the target data of each patient in the first category of reference data, and obtaining the generalization interval of the target data of each patient according to the difference between the sample data corresponding to the number of most similar data; clustering the target data of all patients by using the similarity between the target data, the generalization interval and whether the target data is within the generalization interval, to obtain a number of second categories of target data; For all nursing data of each patient, a generalization interval of all nursing data of each patient and a second category of each nursing data are obtained; the generalization interval of all nursing data includes the second category of the reference data; According to the second category of the reference data, the generalization interval of the target data of each patient and the second category of the target data are obtained again; in the second category to which the target data of each patient belongs, the offset error of the target data of each patient is obtained; and the abnormal data in the second category is obtained according to the offset errors of the target data of all patients; The quality control module determines the quality of the target data of the newly added patients according to the abnormal data in the second category and whether the target data of the newly added patients falls into the generalization interval of the abnormal data.

[0005] Preferably, clustering the reference data of all patients to obtain the first category of reference data comprises the following specific steps: The similarity of the reference data of any two patients is obtained, denoted as x, and exp(-x) is denoted as the metric distance of the reference data of any two patients. The reference data of all patients are clustered using the K-Means clustering algorithm according to the metric distance to obtain several first categories, and exp() represents an exponential function with a natural constant as the base.

[0006] Preferably, the step of obtaining a plurality of most similar data of the target data of each patient and obtaining a generalization interval of the target data of each patient according to the difference between the sample data corresponding to the plurality of most similar data comprises the following specific steps: Each first category of the reference data includes target data corresponding to the reference data of each patient; For all target data in any first category of the reference data, the target data of any patient is recorded as A. For each target data other than the target data A in the first category, the similarity between the target data A and each other target data is obtained, and the target data with the greatest similarity are taken as the most similar data of the target data A; For all sample data corresponding to the most similar data of target data A, the difference between all sample data is recorded as a; The mean value of the similarity between the target data A and all the most similar data is recorded as wa; in the first category to which the target data A belongs, the mean value of the similarity between the target data A and all other target data is obtained as the coordinate value of the target data A, recorded as h; The generalization interval of the target data A is recorded as [(1-a / wa)×h, (1+a / wa)×h].

[0007] Preferably, the specific steps of obtaining the differences between all sample data are as follows: In all the sample data, the similarity between any two sample data is obtained, the mean of the similarities between all the sample data is recorded as b, exp(-b) is recorded as the difference between all the sample data, and exp() represents an exponential function with a natural constant as the base.

[0008] Preferably, the method of clustering the target data of all patients by using the similarity between the target data, the generalization interval, and whether the target data is within the generalization interval to obtain a plurality of second categories of the target data comprises the following specific steps: The similarity of the target data of any two patients is recorded as y1; the intersection and union ratio of the generalization intervals of the target data of any two patients is recorded as y2; Let Y = (1 + y2 × y3) × y1 be the corrected similarity of the target data of any two patients; Among them, for any two patients' target data Q1 and Q2, when the coordinate value of Q1 falls within the generalization interval of Q2, and the coordinate value of Q2 falls within the generalization interval of Q1, the parameter y3=1, otherwise the parameter y3=0; exp(-Y) is used as the metric distance between the target data of any two patients, and then the metric distance is used to perform K-Means clustering on the target data of all patients to obtain several second categories of the target data; exp() represents an exponential function with a natural constant as the base.

[0009] Preferably, the step of obtaining the generalization interval of the target data of each patient and the second category of the target data again according to the second category of the reference data; obtaining the offset error of the target data of each patient in the second category to which the target data of each patient belongs; and obtaining the abnormal data in the second category according to the offset error of the target data of all patients includes the following specific steps: H1: The second category of the reference data is used as the first category of the reference data; then the generalization interval of the target data of each patient and the second category of the target data are obtained according to the first category of the reference data, and for all the nursing data of each patient, the generalization interval of all the nursing data of each patient and the second category of each nursing data are obtained; the generalization interval of all the nursing data includes the second category of the reference data; H2: Repeat the steps in H1; After each execution of the steps in H1, the offset error of the generalization interval of each target data in the second category to which the target data of each patient belongs is obtained; the set consisting of the offset errors of the generalization intervals of all target data in all the second categories is recorded as the error distribution after each execution of H1; when the error distribution no longer changes, the steps in H1 are stopped; after stopping the steps in H1, the abnormal data in the second category is obtained according to the error distribution.

[0010] Preferably, the step of obtaining the offset error of the generalization interval of each target data includes the following specific steps: For the target data in the second category, any generalization interval among the generalization intervals of all the target data is recorded as B, and the other generalization intervals other than B are recorded as reference intervals. The difference B1 between the left endpoint of B and the left endpoint of each reference interval is obtained, and the difference B2 between the right endpoint of B and the right endpoint of each reference interval is obtained. [B1, B2] is recorded as the error feature between each reference interval and B; the error features of all reference intervals and B are subjected to PCA dimensionality reduction, and the mean of all dimensionality reduction results is recorded as the offset error of the generalization interval B.

[0011] Preferably, the step of obtaining abnormal data in the second category according to the error distribution includes the following specific steps: The final error distribution includes the offset error of the generalization interval of each target data; the LOF anomaly detection algorithm is used to obtain the abnormal offset error in the final error distribution, and the target data corresponding to the offset error is recorded as abnormal data.

[0012] Preferably, judging the quality of the target data of the newly added patient according to whether the abnormal data in the second category and the target data of the newly added patient fall into the generalization interval of the abnormal data includes the following specific steps: The target data of the newly added patient is recorded as data F, and the second category to which the data F belongs is obtained from all the second categories of the target data; For any abnormal data in the second category to which data F belongs, the similarity between data F and the abnormal data is recorded as the abnormal value of data F and the abnormal data. When the abnormal value falls into the generalization interval of the abnormal data, the abnormal data is recorded as the target abnormal data; The target data other than all abnormal data in the second category to which data F belongs is recorded as normal data, and the similarity between data F and normal data is recorded as the normal value of data F and normal data. When the normal value falls into the generalization interval of normal data, the normal data is recorded as the target normal data; The quality of the target data of the newly added patients is judged based on the generalization interval between the target abnormal data and the target normal data.

[0013] Preferably, judging the quality of the target data of the newly added patient according to the generalization interval of the target abnormal data and the target normal data comprises the following specific steps: Obtain the intersection and union ratio of the generalization intervals of any pair of target abnormal data and target normal data, and record it as a normal indicator of data F. For all target abnormal data and target normal data, obtain all normal indicators of data F. When the maximum value of the normal indicator is less than the first preset threshold th1, the target data of the newly added patient is recorded as low-quality data, otherwise it is not recorded as low-quality data.

[0014] The beneficial effects of the technical solution of the present invention are: In the first category of reference data, several most similar data of the target data of each patient are obtained, and the generalization interval of the target data of each patient is obtained according to the difference between the sample data corresponding to the several most similar data; the generalization interval is used to describe the generalization ability of the target data under the same or similar reference data (that is, in the same first category); the generalization ability indicates: the strength of the target data being affected by noise interference (for example, the strength of the influence of individual differences such as patient weight, age, and physiological condition).

[0015] Furthermore, the target data of all patients are clustered using the similarity between the target data, the generalization interval, and whether the target data is within the generalization interval, to obtain several second categories of the target data; the target data in the same second category not only have the same or similar characteristics, but any target data is as far as possible within the generalization interval of another target data, ensuring that all target data in each second category have the same or similar data quality, and avoiding to a certain extent the problem of unreliable second categories when the target data is affected by noise interference.

[0016] Furthermore, the generalization interval of the target data of each patient and the second category of the target data are obtained again according to the second category of the reference data; the offset error of the target data of each patient is obtained in the second category to which the target data of each patient belongs; and the abnormal data in the second category is obtained according to the offset error of the target data of all patients. This process realizes the update of the generalization interval and the second category by obtaining the generalization interval of the target data of each patient and the second category of the target data again, and further ensures the accuracy of obtaining the abnormal data.

[0017] Finally, for the target data of the newly added patients, the quality of the target data of the newly added patients is judged based on the abnormal data in the second category and whether the target data of the newly added patients falls into the generalization interval of the abnormal data; ensuring that the data quality of the target data of the newly added patients can be obtained in a timely and accurate manner.

[0018] In summary, the present invention uses the generalized interval and second category of the recorded nursing data to timely and accurately judge the quality of the target data of new patients, while further avoiding the influence of noise caused by individual differences among patients, screening out low-quality nursing data, and improving the quality of nursing data as a whole. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A framework diagram of an electronic information quality control system for intelligent clinical nursing provided by one embodiment of the present invention; Figure 2 A flowchart of the steps of a data analysis module in an electronic information quality control system for intelligent clinical nursing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the electronic information quality control system for intelligent clinical nursing proposed by the present invention, its specific implementation method, structure, characteristics and effects are described in detail as follows in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0022] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0023] The specific scheme of the electronic information quality control system for intelligent clinical nursing provided by the present invention is described in detail below with reference to the accompanying drawings.

[0024] See also Figure 1 , which shows a framework diagram of an electronic information quality control system for intelligent clinical nursing provided by an embodiment of the present invention, the system includes the following modules: The data collection module is used to record relevant data of clinical patients, including the patient's identity information, such as name, gender, age, weight, etc., as well as nursing data, such as medical staff information and attending physician information, disease details, medication data, nursing monitoring results, etc.

[0025] Since disease details, medication data and nursing monitoring results have the greatest impact on the quality of electronic information of clinical nursing, this embodiment only analyzes three types of nursing data, namely disease details, medication data and nursing monitoring results.

[0026] Among them, the details of the illness refer to the specific illness summarized by the doctor after the doctor's consultation and medical equipment (such as CT) diagnosis. Medication data refers to the drugs used by the patient during clinical care, including drug name, dosage, medication time, etc. Nursing monitoring results refer to the test results and test time of the patient's physical signs or physiological status during the patient's clinical care, such as whether the heart rate, blood pressure, body temperature and respiratory rate are normal, and whether the blood, urine and other test results are normal. All nursing data for each patient is recorded in the MySQL database.

[0027] The data quality of all nursing data of each patient (that is, the quality of electronic information mentioned above) mainly refers to: whether the patient's nursing data records are complete; whether the integration of multi-source data is appropriate, such as the diagnosis results from the attending physician, the medication data from the nurse, and the diagnostic monitoring data from the medical equipment; whether there are conflicts or errors in the patient information recorded by different medical personnel, whether the clinical care or treatment process is reasonable, such as whether there is excessive medication or drug substitution for the patient, and whether there are inappropriate treatment methods or nursing methods, etc. All of these will affect the data quality of nursing data.

[0028] Data analysis module, used to process nursing data.

[0029] It should be noted that for different patients, the doctors or nurses who register or enter nursing data are different, and the nursing data may not have a unified format. In this embodiment, in order to facilitate the analysis and processing of nursing data; when registering or entering nursing data, the details of the illness are described using text format data, and professional terms are used to describe the type of illness, and words such as "mild", "severe", and "normal" are used to describe the severity of the illness. Since using text format data to describe the illness is a conventional and commonly used technical means, this embodiment will not be described in detail. This embodiment uses one-hot encoding technology to encode the details of the illness into vectors. One-hot encoding technology is a well-known technology and will not be described in detail in this embodiment. Medication data and nursing monitoring results are uniformly recorded using data wide tables; wherein data wide tables are data formats that may be required to be input in the fields of artificial intelligence and machine learning, and belong to well-known technologies, and will not be described in detail in this embodiment.

[0030] In other embodiments, considering that nursing data is important electronic information in clinical nursing, one of its important functions is to serve as a data set for training machine learning models or neural networks. Therefore, the nursing data can be converted into vectors for application in machine learning models or neural networks. For example, the disease details, medication data, and nursing monitoring results are converted into vectors respectively. Methods for converting nursing data into vectors include word2Vec technology, LSTM neural network, etc. These technologies are well known and are not specifically limited in this embodiment.

[0031] In still other embodiments, templated forms may be used to record and store nursing data.

[0032] The quality control module is used to monitor the quality of nursing data entered into the database. If the nursing data is missing, wrong, or contradictory, it means that there are quality problems in the nursing data (that is, low-quality nursing data).

[0033] Specifically, Figure 2 As shown, the steps included in the data analysis module are as follows: Step S1, record any kind of nursing data as target data; record nursing data other than target data as reference data and sample data in turn; cluster the reference data of all patients to obtain the first category of reference data.

[0034] Among them, the nursing data other than the target data are recorded as reference data and sample data in turn, specifically including: when the target data is disease details, the reference data and sample data are medication data and nursing monitoring results, respectively; when the target data is medication data, the reference data and sample data are disease details and nursing monitoring results, respectively; when the target data is nursing monitoring results, the reference data and sample data are disease details and medication data, respectively.

[0035] First, the similarity of the reference data of any two patients is obtained, recorded as x, and exp(-x) is recorded as the metric distance of the reference data of any two patients. The reference data of all patients are clustered using the K-Means clustering algorithm according to the metric distance to obtain K first categories. It is a well-known technology to use the K-Means clustering algorithm to perform clustering after a given metric distance, and this embodiment will not be described in detail.

[0036] All patients mentioned herein refer to all patients whose clinical care has been completed and whose care data has been entered into the database.

[0037] Considering that the nursing data of different departments (such as respiratory department and orthopedic department) are quite different, all patients in these departments cannot be analyzed together or the same number of data cannot be used to record nursing data. Therefore, in some embodiments, all the patients mentioned above are limited to all patients in each department.

[0038] As an example, the similarity of reference data of any two patients is obtained by: When the reference data are disease details, each reference data is represented as a vector (obtained by one-hot encoding technology), and the cosine similarity of the reference data of any two patients is taken as the similarity of the reference data of the two patients.

[0039] When the reference data is medication data or nursing monitoring results, each reference data is represented as a wide data table. For the wide data tables corresponding to the reference data of any two patients, the difference between the table contents at the same position in the two wide data tables is calculated, and the absolute value of the difference between all table contents is summed up as Dx. 1 / (Dx+1) is taken as the similarity of the reference data of the two patients. The larger the Dx, the greater the difference in the contents of the two wide data tables, and the smaller the similarity.

[0040] It should be noted that some tables in a data wide table may not record numerical values ​​but text. When the texts recorded in the tables at the same position in two data wide tables are the same, the difference is recorded as 0, and when they are different, the difference is recorded as 1.

[0041] As another example, obtaining the similarity of reference data of any two patients includes: When the reference data is represented as a vector (eg, converted into a vector using word2Vec technology), the cosine similarity of the reference data of any two patients is used as the similarity.

[0042] As an example, the method for setting K is: setting K to a preset parameter, for example, K is equal to one twentieth of the number of all patients.

[0043] As another example, the method for setting K is: first clustering the reference data of all patients using the mean shift clustering algorithm, and using the obtained number of categories as the value of K. The mean shift clustering algorithm is a well-known technology, and the clustering algorithm can automatically obtain the number of categories, and use this number of categories as the number of categories output by the K-Means clustering algorithm (that is, the K value).

[0044] It should be noted that when the target data is disease details, the reference data and sample data are medication data and nursing monitoring results respectively; when the target data is medication data, the reference data and sample data are disease details and nursing monitoring results respectively; when the target data is nursing monitoring results, the reference data and sample data are disease details and medication data respectively.

[0045] Step S2: Obtain the generalization interval of the target data of each patient and the second category of the target data according to the first category of the reference data.

[0046] S201 . Obtain a generalization interval of target data for each patient according to a first category of reference data.

[0047] The first category contains several reference data, which have similar characteristics. For example, when the reference data is medication data, the medication data in each first category describes the same or similar medication situations, while the medication data in different first categories have obvious differences and distinctions; for another example, when the reference data is disease details, the disease details in each first category represent the same or similar disease situations, while the disease situations in different first categories have obvious differences and distinctions.

[0048] Each reference data in each first category of reference data corresponds to a patient, and the patient also corresponds to target data and sample data, so each first category includes both reference data of several patients and target data and sample data of several patients.

[0049] For all target data in any first category of the reference data, any one of the target data is recorded as A. For each target data other than target data A in the first category, the similarity between target data A and each other target data is obtained, and the N target data with the greatest similarity are taken as the most similar data of target data A. The most similar data of target data A describes multiple other target data with small differences from A.

[0050] In this embodiment, the value of N is half of the number of target data included in each first category (needs to be rounded up); in other embodiments, N can be set to other values, which are not specifically limited in this embodiment.

[0051] The method for obtaining the similarity between the target data A and each other target data is the same as the method for obtaining the similarity between the reference data of any two patients in step S1, and will not be described in detail in this embodiment.

[0052] Each most similar data corresponds to a sample data. For all sample data corresponding to all the most similar data, the differences between these sample data are obtained. The larger the difference, the greater the difference between these sample data, indicating that multiple other target data with a smaller difference from A have different sample data, which further indicates that the target data A has a strong generalization ability. The smaller the difference, the smaller the difference between these sample data, indicating that multiple other target data with a smaller difference from A have sample data that is not much different, which further indicates that the target data A has a weaker generalization ability.

[0053] For example, when the target data is disease details, the reference data and sample data are medication data and nursing monitoring results, respectively. The greater the difference between these sample data (that is, the greater the difference), it means that for patients with similar medication data (that is, patients in each first category), N patients with similar or the same disease conditions have relatively obvious nursing monitoring results. Therefore, it means that the target data A (that is, disease details) has a strong generalization ability. The strong generalization ability means that under the disease represented by the disease details (that is, target data A), when the same or similar drugs are used for treatment, diverse physiological phenomena are exhibited (that is, diverse nursing monitoring results), which further indicates that the target data A is greatly affected by noise interference (for example, it is greatly affected by individual differences in patient weight, age, physiological condition, etc.). If the difference between these sample data is smaller (that is, the difference is smaller), it means that for patients with similar medication data (that is, patients in each first category), N patients with similar or identical illnesses have similar nursing monitoring results. Therefore, it means that the generalization ability of the target data A (that is, the illness details) is weak. The weak generalization ability means that under the disease represented by the illness details (that is, the target data A), when the same or similar drugs are used for treatment, the same or similar physiological phenomena are exhibited (that is, the nursing monitoring results are clear and single), which further indicates that the target data A is less affected by noise interference (for example, it is less affected by individual differences in patient weight, age, physiological condition, etc.).

[0054] As an example, for all sample data corresponding to all the most similar data, obtaining the differences between these sample data includes the following steps: Among all the sample data corresponding to all the most similar data, the similarity between any two sample data is obtained, the mean of the similarities between all sample data is recorded as b, and exp(-b) is recorded as the difference between these sample data. That is, the larger b is, the more these sample data are the same or similar, and the smaller the difference between these sample data is.

[0055] The method for obtaining the similarity between the sample data is the same as the method for obtaining the similarity between the reference data of any two patients in step S1, and will not be described in detail in this embodiment.

[0056] Further, the generalization interval of the target data A of each patient is obtained according to the above-mentioned differences; the generalization interval represents: under the same or similar reference data (that is, in the same first category), the range of the generalization ability exhibited by the target data A. The larger the range of the exhibited generalization ability (that is, the wider the generalization interval), for the target data corresponding to other reference data in the same first category (abbreviated as other target data), even if the other target data has a large difference from the target data A, it can be judged that the data quality of the other target data is close to the data quality of the target data A; if the range of the exhibited generalization ability is smaller (that is, the narrower the generalization interval), when the other target data has a large or small difference from the target data A, it can be judged that the data quality of the other target data is large or small different from the data quality of the target data A.

[0057] As an example, the generalization interval of the target data A of each patient is obtained according to the above-mentioned differences, including the following method: For all sample data corresponding to the most similar data of target data A, the difference between these sample data is recorded as a; The mean similarity between the target data A and all the most similar data is recorded as wa. In the first category to which the target data A belongs, all target data other than the target data A are obtained, and the mean similarity between the target data A and all other target data is recorded as the coordinate value of the target data A, recorded as h. The coordinate value h of the target data A is used to describe: the position of the target data A relative to other target data in the same first category.

[0058] The generalization interval of target data A is recorded as [(1-a / wa)×h, (1+a / wa)×h]; the larger a / wa is, the larger the difference between all sample data corresponding to the most similar data is, indicating that the sample data corresponding to other target data similar to target data A have obvious differences from the sample data corresponding to target data A, and the larger the range of the generalization interval of target data A is at this time; the smaller a / wa is, the smaller the difference between all sample data corresponding to the most similar data is, indicating that the sample data corresponding to other target data similar to target data A have no obvious differences from the sample data corresponding to target data A, and the generalization ability of target data A is relatively weak, and the range of the generalization interval of target data A is small.

[0059] As another example, the generalization interval of the target data A of each patient is obtained according to the above-mentioned difference, including the method of: In the first category to which the target data A belongs, any target data in the first category is recorded as the target data to be processed; Obtaining similarities between the target data to be processed and other target data in the first category, wherein the similarities constitute a vector, which is a similarity vector of the target data to be processed; For all target data in the first category to which target data A belongs, the PCA dimensionality reduction algorithm is used to reduce the similarity vectors of all target data to one dimension, where the dimensionality reduction result of the similarity vector of target data A is taken as h, and then the generalization interval of target data A is recorded as [(1-a / wa)×h, (1+a / wa)×h].

[0060] S202 , clustering the target data of all patients using the similarities between the target data and the generalization interval to obtain a plurality of second categories of the target data.

[0061] The target data in the same second category not only have the same or similar characteristics, but any target data is as close to the generalization range of another target data as possible, ensuring that all target data in each second category have the same or similar data quality, and to a certain extent avoiding the problem of unreliable second categories when the target data is affected by noise interference.

[0062] As an example, the target data of all patients are clustered by using the similarity between the target data and the generalization interval to obtain several second categories of the target data, including the following specific methods: The similarity of the target data of any two patients is recorded as y1. The intersection-and-union ratio of the generalization intervals of the target data of any two patients is recorded as y2, and the intersection-and-union ratio refers to the ratio of the intersection width to the union width of the two generalization intervals.

[0063] Let Y = (1 + y2 × y3) × y1 be the corrected similarity of the target data of the two patients.

[0064] Among them, for the target data Q1 and Q2 of any two patients, when the coordinate value of Q1 falls within the generalization interval of Q2, and the coordinate value of Q2 falls within the generalization interval of Q1, the parameter y3=1, otherwise the parameter y3=0.

[0065] The larger the intersection-over-combination ratio of the generalization intervals of the target data of any two patients, and the larger the corrected similarity when any target data is within the generalization interval of the other target data, the smaller it is otherwise. Compared with similarity, corrected similarity more accurately describes the similarity of data quality between target data, and avoids the interference of noise caused by individual differences of patients (such as differences in age, weight, and physiological conditions) to a certain extent.

[0066] exp(-Y) is used as the metric distance of the target data of any two patients, and then the metric distance is used to perform K-Means clustering on the target data of all patients to obtain several second categories of the target data. The above method of this embodiment makes any target data in each second category as far as possible within the generalization interval of another target data, thereby increasing the accuracy of the second category.

[0067] Step S3: For all nursing data of each patient, obtain the generalization interval and the second category of each nursing data.

[0068] When the target data is disease details, the reference data and sample data are medication data and nursing monitoring results, respectively; at this time, according to the methods of steps S1 and S2, the first category of medication data is first obtained, and then the generalization interval of each patient's disease details and several second categories of disease details are obtained based on the first category of medication data.

[0069] When the target data is medication data, the reference data and the sample data are disease details and nursing monitoring results, respectively; at this time, according to the method of steps S1 and S2, the first category of the disease details is first obtained, and then based on the first category of the disease details, the generalization interval of each patient's medication data and several second categories of medication data are obtained.

[0070] When the target data is the nursing monitoring result, the reference data and the sample data are the disease details and the medication data, respectively; at this time, according to the method of steps S1 and S2, the first category of the disease details is first obtained, and then based on the first category of the disease details, the generalization interval of the nursing monitoring results of each patient and several second categories of the nursing monitoring results are obtained.

[0071] So far, for all nursing data of each patient, the generalization interval of each nursing data of each patient and all second categories of each nursing data are obtained; all second categories of each nursing data also include the second category of the reference data.

[0072] Step S4, obtaining the generalization interval and the second category of the target data of each patient again according to the second category of the reference data; obtaining the offset error of the generalization interval of each target data in the second category to which the target data of each patient belongs.

[0073] First of all, it should be explained that the purpose of the generalization interval of each nursing data obtained in step S3 is: when one of the nursing data is similar or close (that is, within the first category of the reference data in step S2), the generalization interval determines the relationship between the difference between other nursing data (that is, between the target data A described in step S2 and other target data) and the difference between data quality, that is, when the generalization interval range is large, even if other nursing data have large differences, it can be judged that the data quality between other nursing data is close, and when the generalization interval range is small, when other nursing data have large or small differences, it can be judged that the data quality between other nursing data is large or small. The interference caused by noise when judging the difference between the data quality of nursing data is avoided.

[0074] However, the generalization interval of each nursing data in step S3 is obtained based on the first category of the reference data in step S1. When the reference data is clustered in step S1, the reference data is interfered by noise, resulting in unreliable first category of the reference data. The unreliable first category means that although some reference data have obvious differences, there are also some differences in the nursing data of the corresponding patients, but such differences are only caused by noise interference. For example, differences in patient weight or physiological conditions lead to differences in medication dosage, blood or urine test results or disease descriptions, but the data quality of the nursing data corresponding to the nursing methods or treatment methods required by these patients is the same. This results in the unreliable classification result of the first category obtained in step S1, and it is impossible to further accurately obtain the most similar data of the target data A, and it is impossible to accurately obtain the generalization interval of the target data A.

[0075] In step S3, the second category of each nursing data has been obtained, which also includes the second category of the reference data described in step S2; that is, the second category of the reference data is obtained in step S3, specifically: When the target data is disease details, the reference data and sample data are medication data and nursing monitoring results, respectively; at this time, according to the methods of steps S1 and S2, the first category of medication data is first obtained, and then the generalization interval of each patient's disease details and several second categories of disease details are obtained based on the first category of medication data.

[0076] When the target data is medication data, the reference data and the sample data are disease details and nursing monitoring results, respectively; at this time, according to the method of steps S1 and S2, the first category of the disease details is first obtained, and then based on the first category of the disease details, the generalization interval of each patient's medication data and several second categories of medication data are obtained.

[0077] When the target data is the nursing monitoring result, the reference data and the sample data are the disease details and the medication data, respectively; at this time, according to the method of steps S1 and S2, the first category of the disease details is first obtained, and then based on the first category of the disease details, the generalization interval of the nursing monitoring results of each patient and several second categories of the nursing monitoring results are obtained.

[0078] In summary, the following results were obtained for each type of nursing data: generalized intervals of disease details and several second categories of disease details, generalized intervals of medication data and several second categories of medication data, generalized intervals of nursing monitoring results and several second categories of nursing monitoring results. So far, the second category of each type of nursing data has been obtained.

[0079] At this time, any kind of nursing data is recorded as target data again; nursing data other than target data are recorded as reference data and sample data in turn. For reference data, whether the reference data is disease details or medication data, or nursing monitoring results, the second category corresponding to the reference data can be obtained.

[0080] Therefore, the second category of each type of nursing data includes the second category of reference data.

[0081] In this embodiment, the first category of the reference data described in step S2 is replaced by the second category of the reference data obtained in step S3; the generalization interval of the target data of each patient and the second category of the target data are obtained again using the method in step S2, thereby realizing the update of the generalization interval of the target data of each patient and the update of the second category of the target data.

[0082] When the target data is disease details, the reference data and sample data are respectively disease details and nursing monitoring results; the above realizes the update of the generalized interval of each patient's disease details and the second category of disease details. When the target data is medication data, the reference data and sample data are respectively disease details and nursing monitoring results; the above realizes the update of the generalized interval of each patient's medication data and the second category of medication data. When the target data is nursing monitoring results, the reference data and sample data are respectively disease details and medication data; the above realizes the update of the generalized interval of each patient's nursing monitoring results and the second category of nursing monitoring results.

[0083] In summary, the above process obtains the generalization interval of each patient's target data and the second category of the target data again according to the second category of the reference data, thereby updating the generalization interval of each nursing data of each patient and the second category of each nursing data.

[0084] Then, the first category of the reference data described in step S2 is replaced with the updated second category of the reference data; the generalization interval of the target data of each patient and the second category of the target data are obtained again using the method in step S2, and this process once again realizes the update of the generalization interval of the target data of each patient and the second category of the target data. By analogy, the following process is continuously performed: the generalization interval of the target data of each patient and the second category of the target data are obtained again according to the second category of the reference data (that is, the updated second category).

[0085] After each execution of this process, that is, after the generalization interval of each patient's target data and the second category of the target data are updated once, the generalization intervals of all target data in the second category to which the target data of each patient belongs are obtained, and the offset error of the generalization interval of each target data is obtained among the generalization intervals of these target data. The offset error describes the difference between the generalization interval of each target data and the generalization intervals of other target data in the same second category.

[0086] As an example, obtaining the generalization intervals of all target data, and obtaining the offset error of the generalization interval of each target data among the generalization intervals of the target data, includes the following method: For the target data in the second category, any generalization interval among the generalization intervals of all target data is recorded as B, and other generalization intervals other than B are recorded as reference intervals. The difference B1 between the left endpoint of B and the left endpoint of each reference interval is obtained, and the difference B2 between the right endpoint of B and the endpoint of each reference interval is obtained. [B1, B2] is recorded as the error feature between each reference interval and B. The error features of all reference intervals and B are reduced to one dimension by PCA, and the mean of all dimensionality reduction results is recorded as the offset error of generalization interval B.

[0087] The set of offset errors of the generalization intervals of all target data in the second category is recorded as the error distribution after each execution of this process.

[0088] Obtain the error distributions of the three most recent executions of the above process. When the variances and means of the three error distributions are the same, stop executing the above process. At the same time, stop executing the above process after executing the above process multiple times (for example, after 6 times).

[0089] S5. Obtain abnormal data according to the offset error of the generalization interval of each target data in the second category.

[0090] After execution is stopped, the set of offset errors of the generalization intervals of all target data in the second category is recorded as the final error distribution. The final error distribution includes the offset error of the generalization interval of each target data. The final error distribution represents the distribution of noise (that is, the distribution of individual differences in patients such as age, weight, and physiological conditions); however, considering that each type of nursing data of each patient used in steps S1 to S4 refers to the nursing data that has been entered into the data, there may be low-quality nursing data in these data. At this time, the above-mentioned noise distribution also includes the noise caused by low-quality nursing data.

[0091] This embodiment takes into account that among the nursing data of a large number of patients in the database, low-quality nursing data accounts for a minority, so the noise caused by the nursing data is abnormal in the final error distribution. This embodiment uses the LOF anomaly detection algorithm to obtain the abnormal offset error in the final error distribution, and the target data corresponding to the offset error is recorded as abnormal data.

[0092] In the quality control module, abnormal data is regarded as detected low-quality data. When clinical care for some patients ends, all nursing data of these patients are recorded as nursing data of newly added patients. The quality of the nursing data of the newly added patients is judged based on the detected low-quality data and the generalization interval and the second category of the target data after stopping execution in step S4.

[0093] As an example, the quality of nursing data for newly added patients may include: For all second categories of target data after execution is stopped in step S4; the target data in the nursing data of the newly added patient is recorded as data F, the similarity between data F and each target data in the second category is calculated, and the average of the similarities between data F and all target data in the second category is recorded as the membership of the second category; the category with the largest membership is taken as the second category to which data F belongs.

[0094] For any abnormal data in the second category to which data F belongs, the similarity between data F and the abnormal data is recorded as the abnormal value of data F and the abnormal data. When the abnormal value falls into the generalization interval of the abnormal data, the abnormal data is recorded as the target abnormal data, otherwise it is recorded as invalid abnormal data.

[0095] The target data other than all abnormal data in the second category to which data F belongs is recorded as normal data, and the similarity between data F and normal data is recorded as the normal value of data F and normal data. When the normal value falls into the generalization interval of normal data, the normal data is recorded as target normal data, otherwise it is recorded as invalid normal data.

[0096] Obtain the intersection and union ratio of the generalization intervals of any pair of target abnormal data and target normal data, and record it as a normal indicator of data F. For all target abnormal data and target normal data, obtain all normal indicators of data F. When the maximum value of the normal indicator is less than the first preset threshold th1, the target data in the nursing data of the newly added patient is recorded as low-quality data, otherwise it is not regarded as low-quality data.

[0097] In particular, when there is no target abnormal data (that is, the abnormal value does not fall into the generalization interval of any abnormal data), but there is target normal data, data F is no longer regarded as low-quality data. When there is no target normal data (that is, the normal value does not fall into the generalization interval of any normal data), but there is target abnormal data, data F is directly regarded as low-quality data. If neither the target abnormal data nor the low-quality data exists, data F is also directly regarded as low-quality data.

[0098] When the target data is disease details, the above method is used to determine whether the disease details in the nursing data of the newly added patient are low-quality data. When the target data is medication data, the above method is used to determine whether the medication data in the nursing data of the newly added patient are low-quality data. When the target data is nursing monitoring results, the above method is used to determine whether the nursing monitoring results in the newly added nursing data are low-quality data.

[0099] In this embodiment, as long as there is one low-quality data among all the nursing data of each patient, all the nursing data of the patient are abnormal.

[0100] So far, in this embodiment, whenever nursing data of a new patient is added, it is possible to obtain whether the nursing data of the new patient is of low quality.

[0101] When low-quality nursing data appears, all nursing data of the patient are marked with a "low quality" label. In this implementation, the nursing data marked as "low quality" is not used as a data set for training machine learning models or neural network models, which are used to achieve intelligent clinical care (such as formulating a reasonable nursing plan).

[0102] It should be noted that this embodiment is described by taking th1=0.4 as an example, and other embodiments may be set to other values. For example, in some embodiments, in order to ensure that the data set for training the machine learning model or the neural network model has a high data quality, the value of th1 is set to be larger, such as th1=0.55; in other embodiments, if the data set for training the machine learning model or the neural network model includes large-scale nursing data, the value of th1 is set to be smaller, such as th1=0.3. For another example, in some embodiments, when the number of all patients is less than 1000, th1=0.3 is set, when the number of all patients is greater than or equal to 1000 and less than 6000, th1=0.4, and when the number of all patients is greater than or equal to 6000, th1=0.55.

[0103] In other embodiments, when low-quality data appears, a manual check is performed, and professional medical staff re-determine whether the low-quality data has obvious missing, incomplete, unreasonable, etc. If so, it will be marked with a "low quality" label. If not, all nursing data of the patient will no longer be regarded as low-quality data (or abnormal data) in all embodiments of the present invention.

[0104] In some other embodiments, after the nursing data of a newly added patient is entered into the database, all target normal data of the nursing data of the newly added patient are displayed for reference. Medical staff can judge whether the nursing data of the newly added patient is abnormal (such as missing, incomplete, unreasonable, etc.) by comparing the nursing data of the newly added patient with the target normal data.

[0105] It should be noted that as the patient's nursing data is entered (that is, entered into the database), there is more and more nursing data in the database, and the quality judgment results of the nursing data are becoming more and more credible; for the low-quality nursing data (recorded as nursing data Q) obtained after a certain entry of nursing data, after the subsequent entry of nursing data, part of the nursing data in these nursing data Q may no longer be of low quality. The reason is that when the nursing data Q is obtained, there is less nursing data in the database, resulting in unreliable nursing data Q. At this time, the low-quality nursing data obtained after the subsequent entry of nursing data is used as the final reliable low-quality nursing data, and the "low quality" label of the nursing data in the nursing data Q that is no longer of low quality is deleted.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An electronic information quality control system for intelligent clinical nursing, characterized in that: The system includes the following modules: The data collection module is used to obtain several types of nursing data for each patient, including: disease details, medication data, and nursing monitoring results; The data analysis module is used to record any kind of nursing data as target data; the nursing data other than the target data are recorded as reference data and sample data in sequence. When the target data is disease details, the reference data and sample data are medication data and nursing monitoring results, respectively; when the target data is medication data, the reference data and sample data are disease details and nursing monitoring results, respectively; when the target data is nursing monitoring results, the reference data and sample data are disease details and medication data, respectively; the reference data of all patients are clustered to obtain the first category of the reference data; Obtaining the generalization interval of target data of each patient and the second category of target data according to the first category of reference data, including: obtaining a number of most similar data of the target data of each patient in the first category of reference data, and obtaining the generalization interval of the target data of each patient according to the difference between the sample data corresponding to the number of most similar data; clustering the target data of all patients by using the similarity between the target data, the generalization interval and whether the target data is within the generalization interval, to obtain a number of second categories of target data; For all nursing data of each patient, a generalization interval of all nursing data of each patient and a second category of each nursing data are obtained; the generalization interval of all nursing data includes the second category of the reference data; According to the second category of the reference data, the generalization interval of the target data of each patient and the second category of the target data are obtained again; in the second category to which the target data of each patient belongs, the offset error of the target data of each patient is obtained; and the abnormal data in the second category is obtained according to the offset errors of the target data of all patients; The quality control module determines the quality of the target data of the newly added patients according to the abnormal data in the second category and whether the target data of the newly added patients falls into the generalization interval of the abnormal data.

2. The electronic information quality control system for intelligent clinical nursing according to claim 1, characterized in that: The clustering of the reference data of all patients to obtain the first category of reference data includes the following specific steps: The similarity of the reference data of any two patients is obtained, denoted as x, and exp(-x) is denoted as the metric distance of the reference data of any two patients. The reference data of all patients are clustered using the K-Means clustering algorithm according to the metric distance to obtain several first categories, and exp() represents an exponential function with a natural constant as the base.

3. The electronic information quality control system for intelligent clinical nursing according to claim 1 is characterized in that: The step of obtaining a plurality of most similar data of the target data of each patient and obtaining a generalization interval of the target data of each patient according to the difference between the sample data corresponding to the plurality of most similar data includes the following specific steps: Each first category of the reference data includes target data corresponding to the reference data of each patient; For all target data in any first category of the reference data, the target data of any patient is recorded as A. For each target data other than the target data A in the first category, the similarity between the target data A and each other target data is obtained, and the target data with the greatest similarity are taken as the most similar data of the target data A; For all sample data corresponding to the most similar data of target data A, the difference between all sample data is recorded as a; The mean similarity between the target data A and all the most similar data is recorded as wa; In the first category to which the target data A belongs, the average value of the similarity between the target data A and all other target data is obtained and recorded as the coordinate value of the target data A, which is recorded as h; The generalization interval of the target data A is recorded as [(1-a / wa)×h, (1+a / wa)×h].

4. The electronic information quality control system for intelligent clinical nursing according to claim 3 is characterized in that: The specific steps for obtaining the differences between all sample data are as follows: In all the sample data, the similarity between any two sample data is obtained, the mean of the similarities between all the sample data is recorded as b, exp(-b) is recorded as the difference between all the sample data, and exp() represents an exponential function with a natural constant as the base.

5. The electronic information quality control system for intelligent clinical nursing according to claim 3 is characterized in that: The method of clustering the target data of all patients by using the similarity between the target data, the generalization interval, and whether the target data is within the generalization interval to obtain several second categories of the target data includes the following specific steps: The similarity of the target data of any two patients is recorded as y1; the intersection and union ratio of the generalization intervals of the target data of any two patients is recorded as y2; Let Y = (1 + y2 × y3) × y1 be the corrected similarity of the target data of any two patients; Among them, for any two patients' target data Q1 and Q2, when the coordinate value of Q1 falls within the generalization interval of Q2, and the coordinate value of Q2 falls within the generalization interval of Q1, the parameter y3=1, otherwise the parameter y3=0; exp(-Y) is used as the metric distance between the target data of any two patients, and then the metric distance is used to perform K-Means clustering on the target data of all patients to obtain several second categories of the target data; exp() represents an exponential function with a natural constant as the base.

6. The electronic information quality control system for intelligent clinical nursing according to claim 1, characterized in that: The generalization interval of the target data of each patient and the second category of the target data are obtained again according to the second category of the reference data; the offset error of the target data of each patient is obtained in the second category to which the target data of each patient belongs; and the abnormal data in the second category is obtained according to the offset error of the target data of all patients, including the following specific steps: H1: The second category of the reference data is used as the first category of the reference data; then the generalization interval of the target data of each patient and the second category of the target data are obtained according to the first category of the reference data, and for all the nursing data of each patient, the generalization interval of all the nursing data of each patient and the second category of each nursing data are obtained; the generalization interval of all the nursing data includes the second category of the reference data; H2: Repeat the steps in H1; After each execution of the steps in H1, the offset error of the generalization interval of each target data in the second category to which the target data of each patient belongs is obtained; the set consisting of the offset errors of the generalization intervals of all target data in all the second categories is recorded as the error distribution after each execution of H1; when the error distribution no longer changes, the steps in H1 are stopped; after stopping the steps in H1, the abnormal data in the second category is obtained according to the error distribution.

7. The electronic information quality control system for intelligent clinical nursing according to claim 6, characterized in that: The specific steps of obtaining the offset error of the generalization interval of each target data are as follows: For the target data in the second category, any generalization interval among the generalization intervals of all target data is recorded as B, and the other generalization intervals other than B are recorded as reference intervals. The difference B1 between the left endpoint of B and the left endpoint of each reference interval is obtained, and the difference B2 between the right endpoint of B and the right endpoint of each reference interval is obtained. [B1, B2] is recorded as the error feature between each reference interval and B. The error characteristics of all reference intervals and B are subjected to PCA dimensionality reduction, and the mean of all dimensionality reduction results is recorded as the offset error of the generalized interval B.

8. The electronic information quality control system for intelligent clinical nursing according to claim 6, characterized in that: The specific steps of obtaining abnormal data in the second category according to the error distribution are as follows: The final error distribution includes the offset error of the generalization interval of each target data; the LOF anomaly detection algorithm is used to obtain the abnormal offset error in the final error distribution, and the target data corresponding to the offset error is recorded as abnormal data.

9. The electronic information quality control system for intelligent clinical nursing according to claim 1, characterized in that: The specific steps of judging the quality of the target data of the newly added patient according to whether the abnormal data in the second category and the target data of the newly added patient fall into the generalization interval of the abnormal data are as follows: The target data of the newly added patient is recorded as data F, and the second category to which the data F belongs is obtained from all the second categories of the target data; For any abnormal data in the second category to which data F belongs, the similarity between data F and the abnormal data is recorded as the abnormal value of data F and the abnormal data. When the abnormal value falls into the generalization interval of the abnormal data, the abnormal data is recorded as the target abnormal data; The target data other than all abnormal data in the second category to which data F belongs is recorded as normal data, and the similarity between data F and normal data is recorded as the normal value of data F and normal data. When the normal value falls into the generalization interval of normal data, the normal data is recorded as the target normal data; The quality of the target data of the newly added patients is judged based on the generalization interval between the target abnormal data and the target normal data.

10. The electronic information quality control system for intelligent clinical nursing according to claim 9, characterized in that: The method of judging the quality of the target data of the newly added patient according to the generalization interval of the target abnormal data and the target normal data includes the following specific steps: Obtain the intersection and union ratio of the generalization intervals of any pair of target abnormal data and target normal data, and record it as a normal indicator of data F. For all target abnormal data and target normal data, obtain all normal indicators of data F. When the maximum value of the normal indicator is less than the first preset threshold th1, the target data of the newly added patient is recorded as low-quality data, otherwise it is not recorded as low-quality data.

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