Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

Maturity Assessment of Hospital Information Systems Based on Electronic Medical Record Adoption Model (EMRAM)— Private Hospital Cases in Iran

Introduction: Today, information technology is considered as an important national development principle in each country which is applied in different fields. Health care as a whole and the hospitals could be regarded as a field and organizations with most remarkable IT applications respectively. Although different benchmarks and frameworks have been developed to assess different aspects of Hospital Information Systems (HISs) by various researchers, there is not any suitable reference model yet to benchmark HIS in the world. Electronic Medical Record Adoption Model (EMRAM) has been currently presented and is globally well-known to benchmark the rate of HIS utilization in the hospitals. Notwithstanding, this model has not been introduced in Iran so far. Methods: This research was carried out based on an applied descriptive method in three private hospitals of Isfahan—one of the most important provinces of Iran—in the year 2015. The purpose of this study was to investigate IT utilization stage in three selected private hospitals. Conclusion: The findings revealed that HIS is not at the center of concern in studied hospitals and is in the first maturity stage in accordance with EMRAM. However, hospital managers are enforced and under the pressure of different beneficiaries including insurance companies to improve their HIS. Therefore, it could be concluded that these types of hospitals are still far away from desirable conditions and need to enhance their IT utilization stage significantly.

Oxaliplatin Induced Neurotoxicity among Patients with Colorectal Cancer: Documentation in Medical Records—A Pilot Study

Patients with colorectal cancer (CRC) can have chemotherapy with oxaliplatin postoperatively. Oxaliplatin can cause acute and chronic neurotoxicity. It is important to be aware of neurotoxic side effects so they can be documented and action taken at an early stage. The study aimed to identify and explore neurotoxic side effects documented in the medical records of patients with colorectal cancer treated with oxaliplatin-based adjuvant chemotherapy. Data in this study were medical records;presenting documentation about patients treated at the University Hospital in the south of Sweden between 2009 and 2010. A summative content analysis approach was used to explore the neurotoxic side effects. Identification and quantification of the content of medical records were carried out by using a study-specific protocol. “Cold sensitivity” and “tingling in the hands” were the most frequently documented neurotoxicity-related terms in the medical records. This identification was followed by interpretation. Three categories were identified in the interpretive part of the study: acute, chronic, and degree of neurotoxicity. The results show the importance of awareness of neurotoxic side effects so that they can be documented and action taken at an early stage. The documentation could be more reliable if patient-reported structured measurements were used, combined with free descriptions in the medical records. Being able to follow the progression of the symptoms during and after treatment would improve patient’s safety and also quality of life. The protocol that we developed and used in this review of medical records may be helpful to structure the documentation in the electronic system for documentation of neurotoxicity side effects.

Analysis on the mechanism of core drugs in treating exogenous cough in Clinical Guidelines and Medical Records

Objective:To explore the core drug of Tian-shi Ye(1666 C.E.–1745 C.E.,a famous medical experts in Qing Dynasty)for treating exogenous cough and its mechanism of action.Methods:The database of prescriptions for treating exogenous cough was established in Clinical Guide to Medical Records,and the complex network was constructed with frequency analysis and Gephi 0.9.2 software to obtain the core drug for exogenous cough,and the network of traditional Chinese medicine-component-disease-target regulation was constructed through network pharmacology to reveal the potential mechanism.Results:Xingren(Amygdalus Communis Vas)is the core drug for the treatment of exogenous cough in the“Clinical Guidelines and Medical Records”.It contains 19 active ingredients and forms 27 traditional Chinese medicine-disease targets for the treatment of acute bronchitis.Kyoto Encyclopedia of Genes and Genomes analysis involving 114 pathways,including arachidonic acid metabolism pathways,peroxidase pathways,estrogen metabolism pathways,and tryptophan metabolism pathways are the main signal pathways involved.Conclusion:Xingren(Amygdalus Communis Vas)-acute bronchitis has a multi-molecule,multi-target,and multi-pathway,and may be regulating the arachidonic acid metabolism pathway,peroxidase pathway,estrogen metabolism pathway,and tryptophan metabolism pathway.Such interventions as various biological processes in the body play a role in the treatment of acute bronchitis.

Problems and Effective Measures of Enhancing Medical Record Coders’ Professional Qualities

Medical record coding plays an important role in hospital management and the improvement of medical record coding quality is quite important to hospital management.The professional qualities of medical record coders significantly affect the improvement of coding quality.At present,Medical record coders lack in professional knowledge,clinical knowledge and disease classification knowledge.In order to improve the quality of medical record coding,medical record coders should receive regular professional training,and hospital management departments should strengthen the monitoring of the quality of medical record coding.When encountering problems in the process of coding,medical record coders should take the initiative to consult and learn from clinicians.At the same time,medical record coders should cultivate a sense of responsibility,which can continuously improve their professional qualities and achieve the objective of improving the quality of medical record coding.

A Digital System for Managing HL7/CDA Electronic Medical Records Stored in iButtons^(█)

The goal of this research was to develop a digital system that could allow managing electronic medical records (EMRs) codified under specifications of the Health Level 7/Clinical Document Architecture (HL7/CDA) international standard, and saving them in a portable digital storage device called iButton?. To this end, an USB-based hardware interface for reading and storing EMRs in iButtons was designed and implemented. In addition, a software application for invoking read/write operations on stored EMRs and showing their content on a graphical user interface was also developed, following the Extreme Programming (XP) software development methodology and using Visual Basic .NET as programming language. Tests conducted on the hardware interface showed that it was able to recognize any iButton type, reading and writing data on them as well. Moreover, the system helped in creating empty EMRs in conformance with the HL7/CDA standard, adding and viewing information, and updating it in the iButton. This system offers an easy way for managing and visualizing medical records codified in HL7/CDA, and allows patients to take their updated medical history with them everywhere.

A Method for Extracting Electronic Medical Record Entities by Fusing Multichannel Self-Attention Mechanism with Location Relationship Features

With the implementation of the“Internet+”strategy,electronic medi-cal records are generally applied in the medicalfield.Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients’states,but the existing methods for extracting entities from electronic medical records have problems of redundant information,overlapping entities,and low accuracy rates.Therefore,this paper proposes an entity extrac-tion method for electronic medical records based on the network framework of BERT-BiLSTM,which incorporates a multichannel self-attention mechanism and location relationship features.First,the text input sequence was encoded using the BERT-BiLSTM network framework,and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mech-anism.Then,the position relation characteristic was used to extract the local semantic message of the text,and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained.Next,the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix.Finally,the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus,and the exper-imental results indicate that the proposed method surpasses existing methods in terms of precision,recall,F1 value and training time.

Application of the Data Mining Algorithm in the Clinical Guide Medical Records

Objective:This study analyzed the data of the medical cases in the book,“Clinical Guide Medical records”using a data mining method,to provide a reference for Ye Tianshi’s academic thoughts.Methods:We used the web version of the ancient and modern medical records cloud platform to complete distribution statistics,association rules,cluster analysis,and complex network analysis of all the medical records in the“Clinical Guide Medical records.”These methods were used to summarize the baseline data and to identify the core relationship between Chinese medicine diseases and Chinese medicine,as well as the Chinese medicine Classification.Results:A total of 2572 medical records,3136 visits,and 2879 prescriptions of 1127 traditional Chinese medicines were included in this study.The most common diseases(such as hematemesis),syndromes(such as liver–stomach disharmony),symptoms(such as rapid pulse),disease sites(such as gastric cavity),disease properties(such as Yang deficiency),treatment methods(such as activating Yang),and traditional Chinese medicines(such as Poria cocos)were identified.Furthermore,medicines with a warm,flat,cold,sweet,or bitter taste with its effects on the lungs,spleen,and heart were the most common.The observed effects of the drugs included clearing dampness,promoting diuresis,and strengthening the spleen.The association analysis showed that the associations between TCM diseases and traditional Chinese medicines that had a high confidence were“phlegm and fluid retention–Poria cocos,”“diarrhea–Poria cocos,”etc.The cluster analysis showed that traditional Chinese medicines were classified into five categories.The complex network showed the core relationship between nine high-frequency diseases and nine high-frequency traditional Chinese medicine.Conclusion:This study revealed the most important relationships between traditional Chinese medicines diseases and traditional Chinese medicines and classified the most used traditional Chinese medicines.These findings may help the coming generations of doctors to make accurate diagnoses and treat patients effectively and to improve the clinicians’efficacy in clinical diagnosis and treatment.

Joint Development of Evidence-Based Medical Record by Doctors and Patients through Integrated Chinese and Western Medicine on Digestive System Diseases

Building the clinical therapeutic evaluation system by combing the evaluation given by doctors and patients can form a more comprehensive and objective evaluation system.A literature search on the practice of evidence-based evaluation was conducted in key biomedical databases,i.e.PubMed,Excerpt Medica Database,China Biology Medicine disc and China National Knowledge Infrastructure.However,no relevant study on the subjects of interest was identified.Therefore,drawing on the principles of narrative medicine and expert opinion from systems of Chinese medicine and Western medicine,we propose to develop and pilot-test a novel evidence-based medical record format that captures the perspectives of both patients and doctors in a clinical trial.Further,we seek to evaluate a strategic therapeutic approach that integrates the wisdom of Chinese medicine with the scientific basis of Western medicine in the treatment of digestive system disorders.Evaluation of therapeutic efficacy of remedies under the system of Chinese medicine is an imperative ongoing research.The present study intends to identify a novel approach to assess the synergistic benefits achievable from an integrated therapeutic approach combining Chinese and Western system of medicine to treat digestive system disorders.

Analysis of Herbal Medicine Prescriptions for Patients in An Academic Korean Medical Hospital:A Cross Sectional Study of Electronic Medical Records(2010–2013)

Objective： To obtain fundamental information for the standardization of herbal medicine in Korea. Methods： We analyzed the herbal medicine prescription data of patients at the Pusan National University Korean Medicine Hospital from March 2010 to February 2013. We used the Dongui-Bogam （Dong Yi Bao Jian） to classify prescribed herbal medicines. Results： The study revealed that the most frequently prescribed herbal medicine was ‘Liuwei Dihuang Pill （LWDHP, 六味地黄丸）＇ which was used for invigorating ‘Shen （Kidndy）-yin＇. ‘LWDHP＇ was most frequently prescribed to male patients aged 50-59, 60-69, 70-79 and 80-89 years, and ‘Xionggui Tiaoxue Decoction （XGTXD, 芎归调血饮）＇ was most frequently prescribed to female patients aged 30-39 and 40-49 years. According to the International Classification of Diseases （ICD） codes,‘Diseases of the musculoskeletal system and connective tissue＇ showed the highest prevalence. ‘LWDHP＇ and ＇XGTXD＇ was the most frequently prescribed in categories 5 and 3, respectively. Based on the percentage of prescriptions for each sex, ‘Ziyin Jianghuo Decoction （滋阴降火汤）＇ was prescribed to mainly male patients, and ‘XGTXD＇ with ‘Guima Geban Decoction （桂麻各半汤）＇ were prescribed to mainly female patients. Conclusion： This study analysis successfully determined the frequency of a variety of herbal medicines, and many restorative herbal medicines were identified and frequently administered.

The model of taking electronic medical records as the core for information construction in hospitals＂

The development of hospital information has been carried out for nearly 50 years, and originally started Le hospital information system （HIS）1 So far HIS isas the hospital information system （HIS）J So far HIS is the most widely and deeply used management system for hospitals in China.2 ＂General function standard for hospital information system＂ issued by China＇s Ministry of Health in 2002 defined that ＂The hospital information system refers to using of computer hardware and software technology, network communications technology, and other modem technology to comprehensively manage personnel, logistics, and finance in various departments in hospital. Gather, store, treat, extract, transport, aggregate,and process data in various stages of the medical activities, so that provide comprehensive and automatic information management and service to the hospital.＂

Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

The China Conference on Knowledge Graph and Semantic Computing(CCKS)2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records.Two annotated data sets and some other additional resources for these two subtasks were provided for participators.This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results.The pre-trained language models are widely applied in this evaluation task.Data argumentation and external resources are also helpful.

An efficient approach to representing and mining knowledge from Qing court medical records

Research on Qing Court Medical Records （RQCMR） is a large-volume book which was edited and annotated by the sixth co-author Keji, Chen and his colleagues, and consists of all the medical records of imperial families and aristocrats of the Qing dynasty. To reveal and utilize their high value both in traditional Chinese medicine research and modem clinical practice, we have developed a method of transforming the Qing Court Medical Records （QCMR） into a computer-read- able, structured representation, so that statistical analysis and data mining can be accurately performed. The method consists of a frame ontology based medical language, called MedL, for representing QCMR, a parser for compiling MedL frames into a database, and an explorative pattem mining technique. With this method the entire RQCMR volume is transformed into a database and medical patterns may be mined from the database.

Comparative visual analytics for assessing medical records with sequence embedding

Machine learning for data-driven diagnosis has been actively studied in medicine to provide better healthcare.Supporting analysis of a patient cohort similar to a patient under treatment is a key task for clinicians to make decisions with high confidence.However,such analysis is not straightforward due to the characteristics of medical records:high dimensionality,irregularity in time,and sparsity.To address this challenge,we introduce a method for similarity calculation of medical records.Our method employs event and sequence embeddings.While we use an autoencoder for the event embedding,we apply its variant with the self-attention mechanism for the sequence embedding.Moreover,in order to better handle the irregularity of data,we enhance the self-attention mechanism with consideration of different time intervals.We have developed a visual analytics system to support comparative studies of patient records.To make a comparison of sequences with different lengths easier,our system incorporates a sequence alignment method.Through its interactive interface,the user can quickly identify patients of interest and conveniently review both the temporal and multivariate aspects of the patient records.We demonstrate the effectiveness of our design and system with case studies using a real-world dataset from the neonatal intensive care unit of UC Davis.

Word Embedding Bootstrapped Deep Active Learning Method to Information Extraction on Chinese Electronic Medical Record

Electronic medical record (EMR) containing rich biomedical information has a great potential in disease diagnosis and biomedical research. However, the EMR information is usually in the form of unstructured text, which increases the use cost and hinders its applications. In this work, an effective named entity recognition (NER) method is presented for information extraction on Chinese EMR, which is achieved by word embedding bootstrapped deep active learning to promote the acquisition of medical information from Chinese EMR and to release its value. In this work, deep active learning of bi-directional long short-term memory followed by conditional random field (Bi-LSTM+CRF) is used to capture the characteristics of different information from labeled corpus, and the word embedding models of contiguous bag of words and skip-gram are combined in the above model to respectively capture the text feature of Chinese EMR from unlabeled corpus. To evaluate the performance of above method, the tasks of NER on Chinese EMR with “medical history” content were used. Experimental results show that the word embedding bootstrapped deep active learning method using unlabeled medical corpus can achieve a better performance compared with other models.

Development of Medical Informatization in the Era of Big Data

The purpose of this paper is to discuss the development of medical informatization in the era of big data.Through literature review and theoretical analysis,the development of medical informatization in the era of big data is deeply discussed.The results show that medical informatization has developed rapidly in the era of big data,and its role in clinical decision-making,scientific research,teaching,and management has become increasingly prominent.The development of medical informatization in the era of big data has important purposes and methods,which can produce important results and conclusions and provide strong support for the development of the medical field.

Mobile EMR Use for Epidemiological Surveillance on a Medical Service Trip in Honduras: A Pilot Study

Rationale: Medical treatment on short-term primary care medical service trips (MSTs) is generally symptom-based and supplemented by point-of-care testing. This pilot study contributes to the effective planning for such austere settings based on predicted symptomology. Objective: We aimed to prospectively document the epidemiology of patients seen during two low-resource clinics on a MST in Honduras and apply predefined case definitions adapted from guidelines used by international healthcare organizations (e.g. World Health Organization). Methods: An observational design was used to track the epidemiology during two clinics on an MST in Limon, Honduras in March 2015. The QuickChart mobile electronic medical record (EMR) application was piloted to document diagnoses according to predefined case definitions. Results: The most commonly diagnosed syndromes were upper respiratory complaints (20.19%), nonspecific abdominal complaints (20.19%), general pain (15.38%), hypertension (9.62%), pruritus (6.73%), and asthma/ COPD (4.81%). The case definitions accounted for 94% of all complaints and diagnoses on the brigade. Discussion: The distribution of common patient diagnoses on this MST was similar to that which had been reported elsewhere. The use of broader symptom-based case definitions for epidemiologic surveillance could also facilitate the syndromic management of patients seen on MSTs, and improve the consistency of treatment offered. Conclusion: Case definitions for common syndromes on primary care MSTs may be a feasible method of standardizing patient management. Preliminary use of the QuickChart EMR was acceptable for documentation of epidemiology in the field. Further study is necessary to investigate the reliability of syndromic diagnostic criteria between different clinicians and in a variety of MST settings.

Identification of Cirrhosis Subtypes Through Heterogeneous Medical Information Network

Background:Liver cirrhosis is a complex and heterogeneous disease,with a mortality rate of up to 57%,resulting in 1.03 million deaths per year.The prevalence of liver cirrhosis is on the rise.Patients with liver cirrhosis have a variety of clinical phenotypes and are prone to various complications related to liver cirrhosis.Therefore,there is an urgent need to improve the early prevention and clinical management of cirrhosis and its complications.Methods:We use a precise medical approach to analyze and characterize the complex manifestations of cirrhotic patient populations,and we propose a Heterogeneous Medical Record Network(HEMnet)that includes electronic medical records,molecular interaction networks,and domain knowledge.We train the network embedding vector on HEMnet to obtain the low-dimensional vector representation of each node.With these vectors,we enriched the original medical record and identified six subtypes of cirrhosis.Results:Subtype 1 is characterized by heart disease,and subtype 2 has the strongest association with metabolic-related diseases.Subtype 3 was characterized by Chronic gastritis diseases.Subtype 4 was characterized by Liver cirrhosis-related complications-serous effusion.Subtype 5 had the strongest association with hepatitis-cirrhosis-related complications diseases and gallbladder disease.Subtype 6 was most strongly associated with Liver cirrhosis-related complications and hepatic carcinoma.By assessing the human disease-gene association of each subtype,the rich phenotype and biological functions of each subtype at the gene level were matched to the disease comorbidities and clinical differences we identified through EHR.Conclusion:Our approach demonstrates the utility of applying a precision medicine paradigm to cirrhosis and the prospect of extending this approach to other complexes,multifactorial diseases.

Classification of metabolic-associated fatty liver disease subtypes based on TCM clinical phenotype

Objective:To classify the subtypes of metabolic-associated fatty liver disease(MAFLD)and provide new insights into the heterogeneity of MAFLD.Methods:Electronic medical records(EMR)of MAFLD diagnosed in accordance with the diagnostic criteria of Hubei Provincial Hospital of Traditional Chinese Medicine from 2016-2020 were included in the study.for physical annotation,and the data on each clinical phenotype was normalized according to corresponding aspirational standards.The MAFLD heterogeneous medical record network(HEMnet)was constructed using sex,age,disease diagnosis,symptoms,and Western medicine prescriptions as nodes and the co-occurrence times between phenotypes as edges.K-means clustering was used for disease classification.Relative risk(RR)was used to assess the specificity of each phenotype.Statistical methods were used to compare differences in laboratory indicators among subtypes.Results:A total of patients(12,626)with a mean age of 55.02(±14.21)years were included in the study.MAFLD can be divided into five subtypes:digestive diseases(C0),mental disorders and gynecological diseases(C1),chronic liver diseases and decompensated complications(C2),diabetes mellitus and its complications(C3),and immune joint system diseases(C4).Conclusions:Patients with MAFLD experience various symptoms and complications.The classification of MAFLD based on the HEMnet method is highly reliable.

Serum Sodium Fluctuation Prediction among ICU Patients Using Neural Network Algorithm:Analysis of the MIMIC-IV Database

Sodium homeostasis disorder is one of the most common abnormal symptoms of elderly patients in intensive care unit(ICU),which may lead to physiological disorders of many organs.The current prediction of serum sodium in ICU is mainly based on the subjective judgment of doctors’experience.This study aims at this problem by studying the clinical retrospective electronic medical record data of ICU to establish a machine learning model to predict the short-term serum sodium value of ICU patients.The data set used in this study is the open-source intensive care medical information set Medical Information Mart for Intensive Care(MIMIC)-IV.The time point of serum sodium detection was selected from the ICU clinical records,and the ICU records of 25risk factors related to serum sodium were extracted from the patients within the first 12 h for statistical analysis.A prediction model of serum sodium value within 48 h was established using a feedforward neural network,and compared with previous methods.Our research results show that the neural network learning model can predict the development of serum sodium in patients using physiological indicators recorded in clinical electronic medical records within 12 h,and has better prediction effect than the serum sodium formula and other machine learning models.