CN108597614A - A kind of auxiliary diagnosis decision-making technique based on Chinese electronic health record - Google Patents
A kind of auxiliary diagnosis decision-making technique based on Chinese electronic health record Download PDFInfo
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- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
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Abstract
Auxiliary diagnosis decision-making technique is that one in Clinical Decision Support Systems (Clinical decision support system, CDSS) actively applies branch very much.The exact classification that it electronic health record data set using Chinese that current auxiliary diagnosis decision-making technique fails to well, can not effectively excavate the correlation rule of symptom and disease in Chinese electronic health record, can not reduce symptom characteristic dimension well, can not carry out a variety of diseases in higher-dimension symptom characteristic space very accurately.For these problems, The present invention gives a kind of auxiliary diagnosis decision-making techniques based on Chinese electronic health record.The disease that Chinese electronic health record is concentrated is cleaned with symptom information first, then the correlation rule of disease and symptom affairs is excavated, the present invention is ranked up according to disease and the confidence level of symptom affairs correlation rule, and therefrom carry out feature selecting on the basis of base grader classification results, classification of diseases is carried out with decision tree classifier according to the feature vector that feature selecting goes out, realizes the prediction to disease.
Description
Technical field
The present invention relates to Data Minings, and in particular to feature selecting algorithm, sorting algorithm.
Background technology
Earliest auxiliary diagnosis decision-making technique mainly all concentrates on Knowledge based engineering CDSS researchs and realizes, system structure
Substantially it is made of man-machine interface, knowledge base and inference machine three parts.Knowledge base is by the rule from medical literature, clinical guidelines
Etc. building, and according to these knowledge transformations at rule and determined by human-computer interaction interface offer with certain inference mechanism
Plan supports service.But medicine and medical knowledge are developing always, are changing, and knowledge in knowledge base to be enable to keep up with development
Step need huge input and consumption.So the CDSS based on non-knowledge, i.e. the auxiliary diagnosis decision based on electronic health record
The research of method has gradually become the hot spot and main flow direction of Current Diagnostic Study on DSS.
Different from Knowledge based engineering CDSS, the auxiliary diagnosis decision-making technique based on electronic health record passes through to extensive electronics disease
Go through being associated the operations such as rule digging, classification, recurrence and capable of constantly finding new knowledge to help doctor in disease for data set
Better decision is made during disease diagnosis.The Ministry of Public Health formulated the fundamental norms of electronic health record with 2011, and in the whole nation
The pilot work of 110 hospital electronic health records in range.Through development in a few years, electronic medical record system has become measurement doctor
One major criterion of institute's IT application level, electronic medical record system are also accumulated from considerable data volume, and these
Data are all the truthful datas of patient, this all has diagnosis and treatment and medical research of disease etc. huge value and wide development
Foreground, applying especially based on electronic health record have played important function during the medical diagnosis on disease of CDSS, and into
One step advances the research and development of domestic disease diagnosing system.
Auxiliary diagnosis Research of Decision based on Chinese electronic health record is still at an early stage, at home, Zhou Zhihua, ginger
It is remote et al. that machine learning model is analyzed for disease forecasting but is Single diseases disease forecasting model, it is difficult to be directly applied for
In the prediction of the multi-class medical data collection of multiple features.Although a large amount of artificial neural network has been used to medical diagnosis, refreshing
Need to extract feature from a large amount of sample with Training diagnosis prediction model, it is difficult to be directly used in structure minority class through network model
Medical conditions diagnostic model.2007, Jiang Lin etc. used SVM technologies, and 2 type glycosurias are established to 14 features of 436 cases
Disease forecasting model, to improve estimated performance, it is proposed that a kind of optimal feature subset selection method finally selects 4 kinds of features, this four
A feature corresponds to index highest (susceptibility 86.66%, specificity 64.22%, accuracy rate 70.14%).Meanwhile also using decision
Tree, multi-layer perception (MLP) method are tested, the results showed that support the effect of SVM best.
It is existing to study mainly for Knowledge based engineering auxiliary diagnosis decision-making technique, while in Chinese electronic health record data set
Because there are a large amount of characteristic attribute, causing characteristic attribute dimension height to cause, matrix operation amount is big, training sample is sparse and excessively quasi-
The problems such as conjunction, limits always the classification quality of traditional classifier, so feature selecting is can not ignore in classification of diseases problem
One important engineering.
Invention content
The present invention provides a kind of auxiliary diagnosis decision-making technique based on Chinese electronic health record, utilizes Apriori correlation rules
Algorithm (being already belonging to existing algorithm) carries out the excavation of illness rule, and feature row is carried out using the confidence level of illness rule as standard
Then sequence is selected to divide forward, be selected most according to the sequence that the classifying quality of base grader is standard progress character subset again
It is trained with decision tree classifier centering text electronic health record after excellent character subset, finally completes trained grader again
Auxiliary diagnosis decision task.
It is achieved through the following technical solutions：
A kind of auxiliary diagnosis decision-making technique based on Chinese electronic health record, which is characterized in that include the following steps, it is a kind of right
Disease and symptom information carry out the data preprocessing method of data cleansing in Chinese electronic health record data set, one kind based on disease with
The method that symptom is associated rule digging, a method of feature selecting being carried out based on illness correlation rule, one kind is based on pass
The feature selection approach of connection rule carries out the grader of disease forecasting after selecting feature vector；
The sub- medical record data of a kind of centering message concentrates the data prediction of disease and symptom information progress data cleansing
Method, this method first have to Chinese electronic health record data of the removal containing value of having vacant position and (are already belonging to routine techniques in the prior art
Means), it is secondly standardized the attribute value in i.e. uniform data source to the data information of disease and symptom, then carries out feature two
Value is to convert character type data to the two-value data of Boolean type, finally carries out label coding to disease category.
A kind of method being associated rule digging based on disease and symptom, core operation are to be calculated with Apriori
Method is associated rule digging to disease and symptom.
A kind of method carrying out feature selecting based on illness correlation rule, core operation are two frequent episodes to illness
Collection rule carries out confidence level sequence, and the classification performance of base grader is then recycled to carry out sequence as character subset evaluation criterion
Selection method SFS selects character subset forward.
A kind of point carrying out disease forecasting after selecting feature vector based on the feature selection approach of correlation rule
Class device, core operation are the character subset selected first according to above method, then recycle decision tree classifier to Chinese
Electronic health record is trained and predicts.
Advantageous effect
Disease and the data of symptom information progress data cleansing is concentrated to locate in advance the sub- medical record data of middle message in the present invention
In reason method：Make symptom and disease data standardized format by building near synonym phrase, then by the disease in Chinese electronic health record
Shape characteristic information is converted into the higher-dimension sparse matrix of Boolean type.
It is associated in the method for rule digging based on disease and symptom in the present invention：Using Apriori algorithm to disease
During disease is associated rule digging with symptom, the illness that the method for closing Frequent Set item eliminates partial redundance is used to close
Connection rule.
In the method for the illness correlation rule progress feature selecting of the present invention：It is calculated by using Apriori correlation rules
Method carries out rule digging to the data set of disease and symptom, then carries out feature ordering by the confidence level of two frequent item sets rule
After being divided as the character subset of standard using base grader classifying quality, feature more preferable than traditional feature selection approach effect
Subset smaller.
Disease forecasting is carried out after selecting feature vector based on the feature selection approach of correlation rule in the present invention
In the method for grader：After obtaining character subset by the above-mentioned feature selection approach based on correlation rule, in conjunction with decision tree point
Disease is trained with symptom data collection, adjusts ginseng in class device centering text electronic health record, final to obtain classification of diseases model, with tradition
Grader classifying quality is compared, accuracy higher of classifying.
Compared to traditional auxiliary diagnosis decision-making technique, the present invention fundamentally improves complicated more in Chinese electronic health record
The disease forecasting accuracy rate of symptom characteristic.This research method, which is suitable for providing a large amount of medicine for clinician, to be supported, to help
Clinician is helped to make most rational diagnosis, selection optimal treatment measure.
Description of the drawings
Attached drawing is for carrying a further understanding of the present invention, and a part for constitution instruction, with following tool
Body embodiment is used to explain the disclosure together, but is not construed as limiting the invention.In the accompanying drawings：
Fig. 1 is the Technology Roadmap of the auxiliary diagnosis decision-making technique based on Chinese electronic health record；
Fig. 2 is the flow chart of Chinese electronic health record data prediction.
Fig. 3 is the flow chart of 2 Frequent Sets of excavation illness based on Apriori algorithm.
The flow chart of feature selecting algorithm of the positions Fig. 4 based on correlation rule.
Specific implementation mode
Auxiliary diagnosis decision-making technique be Clinical Decision Support Systems (Clinical decision support system,
CDSS one in) is actively applied branch very much.Current auxiliary diagnosis decision-making technique fails to utilize Chinese electronics well
Medical record data collection can not effectively excavate the correlation rule of symptom and disease in Chinese electronic health record, can not reduce disease well
Shape characteristic dimension, the exact classification that a variety of diseases can not be carried out in higher-dimension symptom characteristic space very accurately.It is asked for these
Topic, The present invention gives a kind of auxiliary diagnosis decision-making techniques based on Chinese electronic health record.Chinese electronic health record is concentrated first
Disease cleaned with symptom information, then excavate disease and symptom affairs correlation rule, the present invention is according to disease and disease
The confidence level of shape affairs correlation rule is ranked up, and therefrom carries out feature selecting on the basis of base grader classification results,
Classification of diseases is carried out with decision tree classifier according to the feature vector that feature selecting goes out, realizes the prediction to disease.
In order to deepen the understanding of the present invention, below in conjunction with existing method and attached drawing 2, the invention will be further described,
Existing method is only used for explaining the present invention, is not intended to limit the scope of the present invention..
Content included by the present invention：A kind of auxiliary diagnosis decision-making technique based on Chinese electronic health record includes four portions
Point：A kind of sub- medical record data of centering message concentrates the data preprocessing method of disease and symptom information progress data cleansing；It is a kind of
The method for being associated rule digging based on disease and symptom；A method of feature selecting is carried out based on illness correlation rule；
A kind of grader carrying out disease forecasting after selecting feature vector based on the feature selection approach of correlation rule.
The first step：The sub- medical record data of centering message is concentrated carries out pretreatment work to disease and symptom information, such as Fig. 2 institutes
Show.
1.1：Chinese electronic health record data of the removal containing value of having vacant position；
1.2：The attribute value in i.e. uniform data source is standardized to the data information of disease and symptom；
1.3：It is to convert character type data to the two-value data of Boolean type to carry out feature binaryzation；
1.4：Label coding is carried out to disease category.
1.5:Finally obtain illness data set.
Second step：Based on the method that disease and symptom are associated rule digging, that is, it is based on Apriori algorithm principle and excavates
Illness binomial Frequent Set, as shown in Figure 3.
2.1：Minimum support threshold value min_sup is defined first, then scans Chinese electronic health record illness data set；
2.2：Judge whether to also need to generate Candidate Set Ci (maximum value of acquiescence i is 2), enter step 2.3 if necessary,
If you do not need to being generated into step 2.4；
2.3：Generate the Candidate Set Ci of Chinese electronic health record illness data set；
2.4：It calculates in Candidate Set Ci so the support sup of element, then is compared with minimum support threshold value min_sup
Compared with, if Ci set in also have element j be put into step 2.5 more than minimum support threshold value min_sup, be otherwise just put into step
Rapid 2.6；
2.5：Generate frequent item set Li；
2.6：The frequent 2 item collection L2 that illness is filtered out from frequent 2 item collection, 2 item collections of remaining non-illness is filtered out, most
Throughout one's life at the frequent 2 item collection L2 of symptom.
Third walks：Feature selecting is carried out based on illness correlation rule, as shown in Figure 4.
3.1：Calculate the confidence level of each characteristic attribute in the frequent 2 item collection L2 of illness；
3.2：Two frequent item set L2 of illness are ranked up according to confidence level parameter first；
3.3：Then sequence is carried out to the binomial Frequent Set L2 after sequence again and divides character subset forward, if do not divided
It finishes, then enters step 3.4, step 3.5 is put into if dividing to finish；
3.4：Setting divides step-length, and it is 1 that acquiescence, which divides step-length,；
3.5：Finally recycle base grader FtTrained classification accuracy ftAs character subset evaluation criterion 3.6：Iteration
Filter out the optimal feature vector V of classification accuracy best grader F and final output.
4th step：The feature vector V selected is walked based on third, then in decision tree classifier centering text electronic health record
Disease is trained with symptom data collection, adjusts ginseng, finally obtains optimal classification of diseases device.
Innovative point
Propose a kind of auxiliary diagnosis decision-making technique based on Chinese electronic health record and existing auxiliary diagnosis decision-making technique
It compares, it is the training that data source carries out classification of diseases model that the present invention, which has used a large amount of Chinese electronic health record,.To middle message
The feature of disease and symptom and classification information have carried out the work of the data predictions such as data cleansing in sub- medical record data, and by disease
The matrix of a Boolean is converted into symptom data information completely, in order to reduce the sparse of disease and symptom Boolean matrix
Property, the dimension of symptom characteristic value is reduced, present invention firstly provides a kind of feature selection approach based on correlation rule, effectively
The dimension for reducing symptom characteristic value improves the accuracy of classification of diseases.
Method proposed by the present invention is concentrated with good performance in the electronic health record data of the more disease categories of more symptom characteristics,
It improves the accuracy rate of a variety of classification of diseases predictions and reduces the dimension of feature vector.
Claims (5)
1. a kind of auxiliary diagnosis decision-making technique based on Chinese electronic health record, which is characterized in that based on the auxiliary of Chinese electronic health record
The diagnosis decision method is helped to include：
Step 1: a kind of sub- medical record data of centering message concentrates the data prediction side of disease and symptom information progress data cleansing
Method；
Step 2: a kind of method being associated rule digging based on disease and symptom；
Step 3: a kind of method carrying out feature selecting based on illness correlation rule；(crucial characteristic step)
Step 4: a kind of classification carrying out disease forecasting after selecting feature vector based on the feature selection approach of correlation rule
Device.
2. the auxiliary diagnosis decision-making technique according to claim 1 based on Chinese electronic health record, which is characterized in that described one
The sub- medical record data of kind centering message concentrates the data preprocessing method of disease and symptom information progress data cleansing:
This method first has to removal containing the Chinese electronic health record data of value of having vacant position, secondly to the data information of disease and symptom into
Row standardization is the attribute value in uniform data source, then carries out feature binaryzation and converts character type data to the two of Boolean type
Value Data finally carries out label coding to disease category.
3. the auxiliary diagnosis decision-making technique according to claim 1 based on Chinese electronic health record, which is characterized in that described one
The method that kind is associated rule digging based on disease and symptom, core operation are with Apriori algorithm to disease and symptom
It is associated rule digging.
4. the auxiliary diagnosis decision-making technique according to claim 1 based on Chinese electronic health record, which is characterized in that Yi Zhongji
In the method that illness correlation rule carries out feature selecting：Core operation is to carry out confidence level row to two frequent item set rules of illness
Then sequence recycles the classification performance of base grader to carry out sequence selection method SFS choosings forward as character subset evaluation criterion
Select character subset.
5. the auxiliary diagnosis decision-making technique according to claim 1 based on Chinese electronic health record, which is characterized in that Yi Zhongji
The grader of disease forecasting is carried out after selecting feature vector in the feature selection approach of correlation rule, core operation is first
Then the character subset selected according to above method recycles decision tree classifier centering text electronic health record to be trained and in advance
It surveys.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109659033A (en) * | 2018-12-18 | 2019-04-19 | 浙江大学 | A kind of chronic disease change of illness state event prediction device based on Recognition with Recurrent Neural Network |
| CN110781216A (en) * | 2019-11-05 | 2020-02-11 | 广东工业大学 | Traditional Chinese medicine symptom association rule mining method and device and storage medium |
| CN111192680A (en) * | 2019-12-25 | 2020-05-22 | 山东众阳健康科技集团有限公司 | Intelligent auxiliary diagnosis method based on deep learning and collective classification |
| CN111292818A (en) * | 2020-01-17 | 2020-06-16 | 同济大学 | Query reconstruction method for electronic medical record description |
| CN111341454A (en) * | 2018-12-19 | 2020-06-26 | 中国电信股份有限公司 | Data mining method and device |
| CN111785372A (en) * | 2020-05-14 | 2020-10-16 | 浙江知盛科技集团有限公司 | Collaborative filtering disease prediction system based on association rule and electronic equipment thereof |
| CN111968747A (en) * | 2020-08-20 | 2020-11-20 | 卫宁健康科技集团股份有限公司 | VTE intelligent prevention and control management system |
| CN112560900A (en) * | 2020-09-08 | 2021-03-26 | 同济大学 | Multi-disease classifier design method for sample imbalance |
| CN112635070A (en) * | 2020-12-14 | 2021-04-09 | 创业慧康科技股份有限公司 | Patient clinical information acquisition and display method and device |
| CN113421653A (en) * | 2021-06-23 | 2021-09-21 | 平安科技（深圳）有限公司 | Medical information pushing method and device, storage medium and computer equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110201529A1 (en) * | 2010-02-12 | 2011-08-18 | Liang-Tsung Huang | System for analyzing and screening disease related genes using microarray database |
| CN107358014A (en) * | 2016-11-02 | 2017-11-17 | 华南师范大学 | The clinical pre-treating method and system of a kind of physiological data |
| CN107451416A (en) * | 2017-08-28 | 2017-12-08 | 昆明理工大学 | A kind of sle auxiliary diagnostic equipment and method |
-
2018
- 2018-04-12 CN CN201810324593.5A patent/CN108597614A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110201529A1 (en) * | 2010-02-12 | 2011-08-18 | Liang-Tsung Huang | System for analyzing and screening disease related genes using microarray database |
| CN107358014A (en) * | 2016-11-02 | 2017-11-17 | 华南师范大学 | The clinical pre-treating method and system of a kind of physiological data |
| CN107451416A (en) * | 2017-08-28 | 2017-12-08 | 昆明理工大学 | A kind of sle auxiliary diagnostic equipment and method |
Non-Patent Citations (4)
| Title |
|---|
| 王美华 等: ""一种基于关联规则与支持向量机的基因表达数据分类模型"", 《计算机应用与软件》 * |
| 胡可云、田凤占、黄厚宽: "《数据挖掘理论与应用》", 30 April 2008, 北京交通大学出版社 * |
| 钟颖、胡雪蕾、陆建峰: ""基于关联规则和决策树的中医胃炎诊断分析"", 《中国中医药信息杂志》 * |
| 陈希蔓: ""维度缩减的原因"", 《HTTPS://SAMANTHACHEN.GITHUB.IO/2016/08/08/机器学习_周志华_笔记11/》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109659033A (en) * | 2018-12-18 | 2019-04-19 | 浙江大学 | A kind of chronic disease change of illness state event prediction device based on Recognition with Recurrent Neural Network |
| CN111341454B (en) * | 2018-12-19 | 2023-07-25 | 中国电信股份有限公司 | Data mining method and device |
| CN111341454A (en) * | 2018-12-19 | 2020-06-26 | 中国电信股份有限公司 | Data mining method and device |
| CN110781216A (en) * | 2019-11-05 | 2020-02-11 | 广东工业大学 | Traditional Chinese medicine symptom association rule mining method and device and storage medium |
| CN111192680A (en) * | 2019-12-25 | 2020-05-22 | 山东众阳健康科技集团有限公司 | Intelligent auxiliary diagnosis method based on deep learning and collective classification |
| CN111192680B (en) * | 2019-12-25 | 2021-06-01 | 山东众阳健康科技集团有限公司 | Intelligent auxiliary diagnosis method based on deep learning and collective classification |
| CN111292818A (en) * | 2020-01-17 | 2020-06-16 | 同济大学 | Query reconstruction method for electronic medical record description |
| CN111292818B (en) * | 2020-01-17 | 2022-04-19 | 同济大学 | Query reconstruction method for electronic medical record description |
| CN111785372A (en) * | 2020-05-14 | 2020-10-16 | 浙江知盛科技集团有限公司 | Collaborative filtering disease prediction system based on association rule and electronic equipment thereof |
| CN111968747A (en) * | 2020-08-20 | 2020-11-20 | 卫宁健康科技集团股份有限公司 | VTE intelligent prevention and control management system |
| CN111968747B (en) * | 2020-08-20 | 2023-12-12 | 卫宁健康科技集团股份有限公司 | VTE intelligent control management system |
| CN112560900A (en) * | 2020-09-08 | 2021-03-26 | 同济大学 | Multi-disease classifier design method for sample imbalance |
| CN112560900B (en) * | 2020-09-08 | 2023-01-20 | 同济大学 | Multi-disease classifier design method for sample imbalance |
| CN112635070A (en) * | 2020-12-14 | 2021-04-09 | 创业慧康科技股份有限公司 | Patient clinical information acquisition and display method and device |
| CN113421653B (en) * | 2021-06-23 | 2022-09-09 | 平安科技（深圳）有限公司 | Medical information pushing method and device, storage medium and computer equipment |
| CN113421653A (en) * | 2021-06-23 | 2021-09-21 | 平安科技（深圳）有限公司 | Medical information pushing method and device, storage medium and computer equipment |
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