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.

Medical Knowledge Extraction and Analysis from Electronic Medical Records Using Deep Learning

Objectives Medical knowledge extraction (MKE) plays a key role in natural language processing (NLP) research in electronic medical records (EMR),which are the important digital carriers for recording medical activities of patients.Named entity recognition (NER) and medical relation extraction (MRE) are two basic tasks of MKE.This study aims to improve the recognition accuracy of these two tasks by exploring deep learning methods.Methods This study discussed and built two application scenes of bidirectional long short-term memory combined conditional random field (BiLSTM-CRF) model for NER and MRE tasks.In the data preprocessing of both tasks,a GloVe word embedding model was used to vectorize words.In the NER task,a sequence labeling strategy was used to classify each word tag by the joint probability distribution through the CRF layer.In the MRE task,the medical entity relation category was predicted by transforming the classification problem of a single entity into a sequence classification problem and linking the feature combinations between entities also through the CRF layer.Results Through the validation on the I2B2 2010 public dataset,the BiLSTM-CRF models built in this study got much better results than the baseline methods in the two tasks,where the F1-measure was up to 0.88 in NER task and 0.78 in MRE task.Moreover,the model converged faster and avoided problems such as overfitting.Conclusion This study proved the good performance of deep learning on medical knowledge extraction.It also verified the feasibility of the BiLSTM-CRF model in different application scenarios,laying the foundation for the subsequent work in the EMR field.

A Proposed Layered Architecture to Maintain Privacy Issues in Electronic Medical Records

Securing large amounts of electronic medical records stored in different forms and in many locations, while making availability to authorized users is considered as a great challenge. Maintaining protection and privacy of personal information is a strong motivation in the development of security policies. It is critical for health care organizations to access, analyze, and ensure security policies to meet the challenge and to develop the necessary policies to ensure the security of medical information. The problem, then, is how we can maintain the availability of the electronic medical records and at the same time maintain the privacy of patients’ information. This paper will propose a novel architecture model for the Electronic Medical Record (EMR), in which useful statistical medical records will be available to the interested parties while maintaining the privacy of patients’ information.

Cautionary note:Electronic medical records,a potential disaster in the making?

Concern is expressed that electronic medical records may actually compromise care.Reports are electronically collated with patient charts, but when are they examined? Current electronic transmission of results to patients' electronic medical records do not seem to notify of new information.The unknown time from prescription to patient action and the variable time required for individual test performance seem to mandate that a physician attempting to be conscientious would have to examine all sections of every patient medical record in their practice, every day.That is quite inefficient and error-prone.Electronic medical record still contains what appear to be dangerous "bugs" which compromise our ability to provide the care we believe our patients deserve? I remain unsure that outpatient electronic medical records are "ready for prime time."

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.

Machine-Learning-Enabled Obesity Level Prediction Through Electronic Health Records

Obesity is a critical health condition that severely affects an individual’s quality of life andwell-being.The occurrence of obesity is strongly associated with extreme health conditions,such as cardiac diseases,diabetes,hypertension,and some types of cancer.Therefore,it is vital to avoid obesity and or reverse its occurrence.Incorporating healthy food habits and an active lifestyle can help to prevent obesity.In this regard,artificial intelligence(AI)can play an important role in estimating health conditions and detecting obesity and its types.This study aims to see obesity levels in adults by implementing AIenabled machine learning on a real-life dataset.This dataset is in the form of electronic health records(EHR)containing data on several aspects of daily living,such as dietary habits,physical conditions,and lifestyle variables for various participants with different health conditions(underweight,normal,overweight,and obesity type I,II and III),expressed in terms of a variety of features or parameters,such as physical condition,food intake,lifestyle and mode of transportation.Three classifiers,i.e.,eXtreme gradient boosting classifier(XGB),support vector machine(SVM),and artificial neural network(ANN),are implemented to detect the status of several conditions,including obesity types.The findings indicate that the proposed XGB-based system outperforms the existing obesity level estimation methods,achieving overall performance rates of 98.5%and 99.6%in the scenarios explored.

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.

Impact of Laboratory Value Flowsheet in Electronic Health Record (EHR) Documentation Time

Research on the use of EHR is contradictory since it presents contradicting results regarding the time spent documenting. There is research that supports the use of electronic records as a tool to speed documentation;and research that found that it is time consuming. The purpose of this quantitative retrospective before-after project was to measure the impact of using the laboratory value flowsheet within the EHR on documentation time. The research question was: “Does the use of a laboratory value flowsheet in the EHR impact documentation time by primary care providers (PCPs)?” The theoretical framework utilized in this project was the Donabedian Model. The population in this research was the two PCPs in a small primary care clinic in the northwest of Puerto Rico. The sample was composed of all the encounters during the months of October 2019 and December 2019. The data was obtained through data mining and analyzed using SPSS 27. The evaluative outcome of this project is that there is a decrease in documentation time after implementation of the use of the laboratory value flowsheet in the EHR. However, patients per day increase therefore having an impact on the number of patients seen per day/week/month. The implications for clinical practice include the use of templates to improve workflow and documentation as well as decreasing documentation time while also increasing the number of patients seen per day. .

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.＂

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.

A Secure Signcryption Scheme for Electronic Health Records Sharing in Blockchain

In the existing Electronic Health Records(EHRs),the medical information of patients is completely controlled by various medical institutions.As such,patients have no dominant power over their own EHRs.These personal data are not only inconvenient to access and share,but are also prone to cause privacy disclosure.The blockchain technology provides a new development direction in the medical field.Blockchain-based EHRs are characterized by decentralization,openness and non-tampering of records,which enable patients to better manage their own EHRs.In order to better protect the privacy of patients,only designated receivers can access EHRs,and receivers can authenticate the sharer to ensure that the EHRs are real and effective.In this study,we propose an identity-based signcryption scheme with multiple authorities for multiple receivers,which can resist N-1 collusion attacks among N authorities.In addition,the identity information of receivers is anonymous,so the relationship between them and the sharer is not disclosed.Under the random oracle model,it was proved that our scheme was secure and met the unforgeability and confidentiality requirements of signcryption.Moreover,we evaluated the performance of the scheme and found that it had the moderate signcryption efficiency and excellent signcryption attributes.

An Investigation on the Barriers and Facilitators of the Implementation of Electronic Health Records (EHR)

The application of technology in health care, in the form of electronic health records (EHR), is the most important and necessary issue in order to improve the quality of health care, and studies have shown that, not only is it a way to integrate information and represent the condition of patients, and a dynamic source for health care, however it leads to gain access to clinical information and records, electronic communications, comprehensive training and management, and ultimately enhancing the public health;the aim of this study is to investigate the factors influencing the implementation of EHR, which are known as barriers and facilitators. The research is conducted in the form of a review research, and with the help of the Keywords of EHR;barriers and facilitators, articles, from 2008 to 2013, were searched and studied in the Internet-databases. The results of the studies show that the most effective factors include: efficiency, motivation, management, and the participation of end users. Factors such as technical aspects ease of use, available resources, and human resources, have limited effects. And security and privacy, the expected output, lack of time, and workload have relative effects, and also the relation between the patient and clinical staff, has no effects in the process of implementing EHR.

Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

Background:Electronic Health Record(EHR)systems are used as an efficient and effective technique for sharing patient’s health records among different hospitals and various other key stakeholders of the healthcare industry to achieve better diagnosis and treatment of patients globally.However,the existing EHR systems mostly lack in providing appropriate security,entrusted access control and handling privacy and secrecy issues and challenges in current hospital infrastructures.Objective:To solve this delicate problem,we propose a Blockchain-enabled Hyperledger Fabric Architecture for different EHR systems.Methodology:In our EHR blockchain system,Peer nodes from various organizations(stakeholders)create a ledger network,where channels are created to enable secure and private communication between different stakeholders on the ledger network.Individual patients and other stakeholders are identified and registered on the network by unique digital certificates issued by membership service provider(MSP)component of the fabric architecture.Results:We created and implemented different Chaincodes to handle the business logic for executing separate EHR transactions on the network.The proposed fabric architecture provides a secure,transparent and immutable mechanism to store,share and exchange EHRs in a peer-to-peer network of different healthcare stakeholders.It ensures interoperability,scalability and availability in adapting the existing EHRs for strengthening and providing an effective and secure method to integrate and manage patient records among medical institutions in the healthcare ecosystem.

Psychiatric electronic health records privacy in Jordan: A policy brief

Psychiatric health records are highly sensitive data which requires special policy to maintain its privacy,without affecting data accessibility.The current authors reviewed social,ethical and legal underpinnings for psychiatric electronic health records(EHR),and suggests a policy to maintain privacy and confidentiality of the psychiatric data,without affecting data accessibility.The purpose of this policy brief is to discuss and provide alternatives regarding psychiatric electronic health records privacy and information access.The current policy applied in Jordan still immature to ensure high levels of reliability,as the psychiatric data is openly accessed to the non-specialized personnel.Sensitive personal data policy is recommended in this paper with developing overriding mechanisms to counteract obstacles to data accessibility.

Opportunities for nurses in the era of electronic health records

Nursing leaders are currently faced with opportunities to advance nursing’s role in the use of electronic health records (EHRs). Nurse leaders can advance the design of EHRs with nurse informaticists to improve health outcomes of individual and populations of patients.

Nursing decision support system:application in electronic health records

The clinical decision support system makes electronic health records(EHRs)structured,intelligent,and knowledgeable.The nursing decision support system(NDSS)is based on clinical nursing guidelines and nursing process to provide intelligent suggestions and reminders.The impact on nurses’work is mainly in shortening the recording time,improving the quality of nursing diagnosis,reducing the incidence of nursing risk events,and so on.However,there is no authoritative standard for the NDSS at home and abroad.This review introduces development and challenges of EHRs and recommends the application of the NDSS in EHRs,namely the nursing assessment decision support system,the nursing diagnostic decision support system,and the nursing care planning decision support system(including nursing intervene),hoping to provide a new thought and method to structure impeccable EHRs.

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.

Ensuring Information Security in Electronic Health Record System Using Cryptography and Cuckoo Search Algorithm

In the contemporary era,the abundant availability of health information through internet and mobile technology raises concerns.Safeguarding and maintaining the confidentiality of patients’medical data becomes paramount when sharing such information with authorized healthcare providers.Although electronic patient records and the internet have facilitated the exchange of medical information among healthcare providers,concerns persist regarding the security of the data.The security of Electronic Health Record Systems(EHRS)can be improved by employing the Cuckoo Search Algorithm(CS),the SHA-256 algorithm,and the Elliptic Curve Cryptography(ECC),as proposed in this study.The suggested approach involves usingCS to generate the ECCprivate key,thereby enhancing the security of data storage in EHR.The study evaluates the proposed design by comparing encoding and decoding times with alternative techniques like ECC-GA-SHA-256.The research findings indicate that the proposed design achieves faster encoding and decoding times,completing 125 and 175 iterations,respectively.Furthermore,the proposed design surpasses other encoding techniques by exhibiting encoding and decoding times that are more than 15.17%faster.These results imply that the proposed design can significantly enhance the security and performance of EHRs.Through the utilization of CS,SHA-256,and ECC,this study presents promising methods for addressing the security challenges associated with EHRs.

Using AI and Precision Nutrition to Support Brain Health during Aging

Artificial intelligence, often referred to as AI, is a branch of computer science focused on developing systems that exhibit intelligent behavior. Broadly speaking, AI researchers aim to develop technologies that can think and act in a way that mimics human cognition and decision-making [1]. The foundations of AI can be traced back to early philosophical inquiries into the nature of intelligence and thinking. However, AI is generally considered to have emerged as a formal field of study in the 1940s and 1950s. Pioneering computer scientists at the time theorized that it might be possible to extend basic computer programming concepts using logic and reasoning to develop machines capable of “thinking” like humans. Over time, the definition and goals of AI have evolved. Some theorists argued for a narrower focus on developing computing systems able to efficiently solve problems, while others aimed for a closer replication of human intelligence. Today, AI encompasses a diverse set of techniques used to enable intelligent behavior in machines. Core disciplines that contribute to modern AI research include computer science, mathematics, statistics, linguistics, psychology and cognitive science, and neuroscience. Significant AI approaches used today involve statistical classification models, machine learning, and natural language processing. Classification methods are widely applicable to problems in various domains like healthcare, such as informing diagnostic or treatment decisions based on patterns in data. Dean and Goldreich, 1998, define ML as an approach through which a computer has to learn a model by itself from the data provided but no specification on the sort of model is provided to the computer. They can then predict values for things that are different from the values used in training the models. NLP looks at two interrelated concerns, the task of training computers to understand human languages and the fact that since natural languages are so complex, they lend themselves very well to serving a number of very useful goals when used by computers.

Integrating Electronic Systems for Requesting Clinical Laboratory Test into Digital Clinical Records: Design and Implementation

Clinical laboratory tests are basic elements that support healthcare tasks such as disease detection, diagnosis and monitoring of response to treatments. Current laboratory information systems focus on the patient database, tests and results, with multiple modules available, connecting with the various analytical systems or work areas. However laboratory information systems functioned as “islands of information”, because their design was fundamentally inward-looking and disconnected from other healthcare computer applications. Actually, the Electronic Health Register (EHR) is considered by clinicians as a tool with great potential healthcare benefits. The EHR, in the sense of a unique and complete record of a patient’s healthcare and state of health, regardless of the healthcare level used, is a real attempt to eliminate these “islands of information” and need modules to act as “bridges” with the laboratory information systems. This type of module, which in generic terms may be referred to as a laboratory test request module, has become an essential feature of the EHR. These modules need to use a laboratory coding system as a common language for exchanging information, ensuring that tests and results are unequivocally identified. The development of the laboratory test request module requires the commitment of professionals and political authorities, being necessary time for their design and an adequate pilot phase. The laboratory professionals have to assume a leadership role in the whole process of design, development and implementation of these modules, integrating in the equipment of information technologies of healthcare providers. In our manuscript we review the elements that may prove electronic systems for requesting clinical laboratory test into digital clinical records and the key elements to move from theory to practice.