Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform:Challenges and Strategies in Data Reuse

Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.

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.

Secure approach to sharing digitized medical data in a cloud environment

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records.

Deletion and Recovery Scheme of Electronic Health Records Based onMedical Certificate Blockchain

The trusted sharing of Electronic Health Records(EHRs)can realize the efficient use of medical data resources.Generally speaking,EHRs are widely used in blockchain-based medical data platforms.EHRs are valuable private assets of patients,and the ownership belongs to patients.While recent research has shown that patients can freely and effectively delete the EHRs stored in hospitals,it does not address the challenge of record sharing when patients revisit doctors.In order to solve this problem,this paper proposes a deletion and recovery scheme of EHRs based on Medical Certificate Blockchain.This paper uses cross-chain technology to connect the Medical Certificate Blockchain and the Hospital Blockchain to real-ize the recovery of deleted EHRs.At the same time,this paper uses the Medical Certificate Blockchain and the InterPlanetary File System(IPFS)to store Personal Health Records,which are generated by patients visiting different medical institutions.In addition,this paper also combines digital watermarking technology to ensure the authenticity of the restored electronic medical records.Under the combined effect of blockchain technology and digital watermarking,our proposal will not be affected by any other rights throughout the process.System analysis and security analysis illustrate the completeness and feasibility of the scheme.

Misdiagnosis Features of Ancient Clinical Records Based on Apriori Algorithm

Objective:To analyze misdiagnosis features in clinical cases of“Classified Medical Cases of Famous Physicians”and“Supplement to Classified Case Records of Celebrated Physicians.”Materials and Methods:Two hundred and five ancient misdiagnosed cases were analyzed in aspects of locations(exterior-interior type,qi-blood type and Zang‑Fu organs type)and patterns(heat-cold type and deficiency-excess type)by Apriori Algorithm Method.Results:The main types of misdiagnosis in those medical casesare as follows::Zang‑Fu location misjudgment,misjudging the interior as the exterior,misjudging deficiency pattern as excess pattern,and misjudging cold pattern as heat pattern.Among them,the most outstanding type is the misjudgment of deficiency–cold pattern as excess–heat pattern.Conclusions:(1)Accurate judgment of location and differentiation of deficiency and excess patterns are the key points in diagnosing the diseases correctly.The confusion of true deficiency–cold and pseudo‑excess–heat pattern should be taken seriously.(2)Data mining on ancient clinical cases offers a new methodology for assisting clinical diagnosis of traditional Chinese medicine.

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.

Secure and Efficient Data Storage and Sharing Scheme Based on Double Blockchain

In the digital era,electronic medical record(EMR)has been a major way for hospitals to store patients’medical data.The traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data sharing.The storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low throughput.To address these issues,we propose a secure and efficient medical data storage and sharing scheme based on double blockchain.In our scheme,we encrypt the original EMR and store it in the cloud.The storage blockchain stores the index of the complete EMR,and the shared blockchain stores the index of the shared part of the EMR.Users with different attributes can make requests to different blockchains to share different parts according to their own permissions.Through experiments,it was found that cloud storage combined with blockchain not only solved the problem of limited storage capacity of blockchain,but also greatly reduced the risk of leakage of the original EMR.Content Extraction Signature(CES)combined with the double blockchain technology realized the separation of the privacy part and the shared part of the original EMR.The symmetric encryption technology combined with Ciphertext-Policy Attribute-Based Encryption(CP–ABE)not only ensures the safe storage of data in the cloud,but also achieves the consistency and convenience of data update,avoiding redundant backup of data.Safety analysis and performance analysis verified the feasibility and effectiveness of our scheme.

Using real world data to assess cardiovascular outcomes of two antidiabetic treatment classes

AIM To evaluate the effect on cardiovascular outcomes of sodium-glucose co-transporter-2(SGLT2) inhibitors in a real world setting by analyzing electronic medical records.METHODS We used Tri Net X, a global federated research network providing statistics on electronic health records(EHR). The analytics subset contained EHR from approximately 38 Million patients in 35 Health Care Organizations in the United States. The records of 46,909 patients who had taken SGLT2 inhibitors were compared to 189,120 patients with dipeptidyl peptidase(DPP) 4 inhibitors. We identified five potential confounding factors and built respective strata: elderly, hypertension, chronic kidney disease(CKD), and co-medication with either insulin or metformin. Cardiovascular events were countedas stroke(ICD10 code: I63) or myocardial infarction(ICD10: I21) occurring within three years after the first instance of the respective medication in the patients' records.RESULTS Of the 46909 patients with SGLT2 inhibitors in their EHR, 1667 patients(3.6%) had an ICD code for stroke or for myocardial infarction within the first three years after the first instance of the medication. In the control group, there were 10680 events of 189120 patients(5.6%), which represents a risk ratio of 0.63(95%CI: 0.60-0.66). The overall incidence of stroke or myocardial infarction in the strata with a potential confounding risk factor reached from 4.9% in patients taking metformin to 12.5% in the stratum with the highest risk(concomitant CKD). In all strata, the difference in risk of experiencing a cardiovascular event was similarly in favor of SGLT2 vs control, with Risk Ratio ranging from 0.62 to 0.81.CONCLUSION Real world data replicated the results from randomized clinical trials, confirmed the cardiovascular advantages of SGLT2 inhibitors, and showed its applicability to the US population.

Real-World Data for the Drug Development in the Digital Era

Randomized clinical trials(RCTs)have long been recognized the gold standard for regulatory approval in the drug development.However,RCTs may not be feasible in some diseases and/or under certain situations,and findings from RCTs may not be generalized to real-world patients in routine clinical practice.Real-world evidence(RWE),which is generated from various real-world data(RWD),has become more and more important for the drug development and clinical decision-making in the digital era.This paper described RWD and real-world data studies(RWDSs),followed by the characteristics and differences between RCTs and RWDSs.Furthermore,the challenges and limitations of RWD and RWE were discussed.Finally,this paper highlights that the efforts must be made during RWE generation from data collection/database selection,study design,statistical analysis,and interpretation of the results to minimize the biases and confounding effects.

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.

Best Practices and Innovative Scenarios of Integrated Patient Data Management to Improve Continuity of Care and Scientific Research

Clinical data have strong features of complexity and multi-disciplinarity. Clinical data are generated both from the documentation of physicians＇ interactions with the patient and by diagnostic systems. During the care process, a number of different actors and roles （physicians, specialists, nurses, etc.） have the need to access patient data and document clinical activities in different moments and settings. Thus, data sharing and flexible aggregation based on different users＇ needs have become more and more important for supporting continuity of care at home, at hospitals, at outpatient clinics. In this paper, the authors identify and describe needs and challenges for patient data management at provider level and regional- （or inter-organizational-） level, because nowadays sharing patient data is needed to improve continuity and quality of care. For each level, the authors describe state-of-the-art Information and Communication Technology solutions to collect, manage, aggregate and share patient data. For each level some examples of best practices and solution scenarios being implemented in the Italian Healthcare setting are described as well.

Translation in Data Mining to Advance Personalized Medicine for Health Equity

Personalized medicine is the development of “tailored” therapies that reflect traditional medical approaches with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention and diagnosis, as well as treatment strategies. Today’s healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of “big data”. For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These “big data” repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians’ interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of “big data” and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine. In closing, we present our fourth perspective, an overview to some of the ethical concerns related to personalized medicine and health equity.

基于古今医案云平台探究中医治疗失眠的辨证思路及用药规律研究

目的运用古今医案云平台整理并分析中医治疗失眠的辨证思路及用药规律。方法收集古今医案云平台(V2.3.8)名医医案库中名老中医及其书籍中的医案数据,以古今医案云平台(V2.3.8)对用药频次、药味药性归经、舌象脉象等进行统计分析、聚类分析、复杂网络分析。结果共纳入医案794个,涉及中药477味,主要有柴胡、白芍、甘草、酸枣仁、川芎、远志、赤芍、当归、合欢皮、茯苓、郁金、栀子。中药药性以平为主;药味多属甘、苦、辛;主要归肝经。高频中药系统聚类分析聚为4组,第一组:酸枣仁、茯苓;第二组:远志、合欢皮、郁金;第三组:白芍、栀子;第四组:柴胡、川芎、赤芍。药物关联分析得到:柴胡与白芍、柴胡与赤芍的关联性最强,其次是柴胡与川芎。复杂网络分析得到7组中医证候,从高到底依次为:肝阳上亢证,痰热扰心证,肝火扰心证,气滞血瘀证,心脾两虚证,肝肾阴虚证,肝郁气滞证。结论中医治疗失眠以清热助眠为法,平调阴阳为纲,多使用清热安神之品,值得临床参考运用。

基于后结构化技术的临床病种库系统设计与应用

目的:为解决传统临床病种库系统存在的依赖大量人工判断、缺乏辅助标注、电子病历数据可用性差等问题,设计一种基于后结构化技术的临床病种库系统。方法:先通过I2B2标准以及双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)模型构建实体识别模型,形成病历模板库,然后组合病历模板库形成关系模板,抽取复杂的医学实体,实现电子病历的后结构化。之后,基于电子病历后结构化技术构建包括病历结构化、结构化评估、数据标注、常规功能和系统管理5个模块的临床病种库系统。结果:该系统可以将电子病历文本转化为结构化语言,提供更精细化的数据要素提取、更智能的结构化服务,提高了临床和科研工作的效率。结论:该系统提高了临床病种的数据可用性,减轻了用户数据加工的工作强度,保证了数据应用的高质量,为医学研究、临床辅助决策打下了坚实的基础。

基于电子病历的电子化衰弱指数构建思路

目的:全面梳理国内外电子化衰弱指数的研究进展,为国内构建电子化衰弱指数提供思路。方法:检索了四川大学数字图书馆中相关文献,并从衰弱风险因子的选择及应用效果评价等方面进行总结。结果:建议进一步扩增衰弱风险指标池,并利用医学术语系统及特征选择等方法构建适用于国内的电子化衰弱指数。结论:电子化衰弱指数对于量化和分级老年人的衰弱状况具有重要作用,有利于早期发现和管理衰弱症状,甚至能对高风险因素进行预防干预,从而降低疾病带来的负担。

中医馆健康信息平台应用现状与思考

本文基于中医馆健康信息平台应用现场调研和问卷调研结果,发现中医馆健康信息平台推广应用过程中存在后续经费投入不足、与医院信息系统对接困难等一系列问题。笔者基于调研结果,就如何更好地推广应用中医馆健康信息平台提出相关建议,以期更有效地提升基层中医馆信息化水平和诊疗能力。

人工智能驱动档案数据智治探索——基于智慧医疗档案管理“提智增效”的考察

在数字化、网络化日益普及的今天,档案数据已不再是孤立、静态的信息孤岛,而是需要与其他数据资源进行整合、关联和共享的重要资产。本研究将关注人工智能技术在实现医疗档案数据与其他数据资源的互联互通、共建共享方面的作用和价值,探索构建基于人工智能的档案数据智治新模式。对人工智能在档案数据管理和应用中可能带来的挑战和风险进行深入剖析,并提出相应的应对策略和解决方案。

信息链视域下电子病历数据驱动临床决策的需求模型构建

[目的/意义]在信息链视域下,提炼电子病历数据驱动临床决策的用户需求,构建需求模型,帮助弥合临床决策支持服务与现实临床工作需要的差距,为电子病历数据提供价值释放靶点,拓展信息链的应用域,助益临床决策支持系统和平台建设,进而为面向临床的情报服务提供指导。[方法/过程]以信息链为理论基础,利用模板分析的方法,通过对访谈资料的分析,提炼了7个一级需求主题、24个二级主题、54个三级主题、43个四级主题以及2个五级主题,构建了电子病历数据驱动临床决策的需求层级模型。[结果/结论]沿着信息链,可以将电子病历数据驱动临床决策的用户需求归纳为病历的智能化记录、临床关键信息的组织与提取识别、电子病历数据驱动的疾病风险预测、疾病诊疗经验与知识的提炼补充、疾病诊断辅助、病情异常原因分析、治疗方案的辅助制定与推荐。医生对电子病历数据驱动临床决策的应用采纳呈现出不同程度的积极性,表现出了对数据确权不清、信息技术成熟度不高等风险的担忧。

基于数据驱动中医证治规律研究的核心问题及解决策略

辨证论治是中医核心诊疗思维,是决定临床疗效的关键。如今,基于临床数据研究是探索中医证治规律的主要方法,但未真正而全面地剖析出“病-证-方-药-效”关键因素的内在关系,导致研究结果的临床价值较低。因此,笔者系统梳理了电子病历与临床研究匹配性差、数据治理影响数据准确性、数据分析方法难以发掘中医证治规律等核心问题,并在数据驱动的背景下,建立中医临床科研大数据平台、开发以人工智能为核心的数据治理与分析技术,从而实现临床科研一体化,为中医证治规律研究提供新思路与方法,推动中医药的发展。

基于电子病历的临床决策支持研究可视化分析

目的/意义探讨基于电子病历的临床决策支持领域研究现状、研究热点与前沿。方法/过程基于文献计量方法,运用CiteSpace 6.2.R2软件绘制国家/地区分布、作者合作、机构合作、关键词共现和聚类科学知识图谱进行可视化分析,通过Python进行聚类热度挖掘与分析。结果/结论基于电子病历数据的临床决策支持领域呈现快速发展态势,美国、中国为主要研究国家,国内外机构之间存在较强合作关系,关键词主要涉及电子病历、人工智能等。