scholarly journals Learning and Visualizing Chronic Latent Representations Using Electronic Health Records

2021 ◽  
Author(s):  
David Chushig-Muzo ◽  
Cristina Soguero-Ruiz ◽  
Pablo de Miguel Bohoyo ◽  
Inmaculada Mora-Jiménez
Keyword(s):  
Health Status ◽  
Electronic Health Records ◽  
Chronic Conditions ◽  
Dimensional Space ◽  
High Dimensional ◽  
Diabetic Patients ◽  
Two Dimensional ◽  
Health Records ◽  
Electronic Health ◽  
Latent Representations

Abstract Background: Nowadays, patients with chronic diseases such as diabetes and hypertension have reached alarming numbers worldwide. These diseases increase the risk of developing acute complications and involve a substantial economic burden and demand for health resources. The widespread adoption of Electronic Health Records (EHRs) is opening great opportunities for supporting decision-making. Nevertheless, data extracted from EHRs are complex (heterogeneous, high-dimensional and usually noisy), hampering the knowledge extraction with conventional approaches. Methods: We propose the use of the Denoising Autoencoder (DAE), a Machine Learning (ML) technique allowing to transform high-dimensional data into latent representations (LRs), thus addressing the main challenges with clinical data. We explore in this work how the combination of LRs with a visualization method can be used to map the patient data in a two-dimensional space, gaining knowledge about the distribution of patients with different chronic conditions. Furthermore, this representation can be also used to characterize the patient's health status evolution, which is of paramount importance in the clinical setting. Results: To obtain clinical LRs, we considered real-world data extracted from EHRs linked to the University Hospital of Fuenlabrada in Spain. Experimental results showed the great potential of DAEs to identify patients with clinical patterns linked to hypertension, diabetes and multimorbidity. The procedure allowed us to find patients with the same main chronic disease but different clinical characteristics. Thus, we identified two kinds of diabetic patients with differences in their drug therapy (insulin and non-insulin dependant), and also a group of women affected by hypertension and gestational diabetes. We also present a proof of concept for mapping the health status evolution of synthetic patients when considering the most significant diagnoses and drugs associated with chronic patients. Conclusions: Our results highlighted the value of ML techniques to extract clinical knowledge, supporting the identification of patients with certain chronic conditions. Furthermore, the patient's health status progression on the two-dimensional space might be used as a tool for clinicians aiming to characterize health conditions and identify their more relevant clinical codes.

A Web-Based Application to Exchange Electronic Health Records and Medical Images in Ophthalmology

2009 ◽  
pp. 152-164
Author(s):  
Isabel de la Torre Díez
Keyword(s):  
Electronic Health Records ◽  
Medical Images ◽  
Universal Access ◽  
Diabetic Patients ◽  
Health Records ◽  
Web Based ◽  
Clinical Document ◽  
Extensible Markup ◽  
Electronic Health ◽  
Web Based System

This chapter describes a Web -based application to store and exchange Electronic Health Records (EHR) and medical images in Ophthalmology: TeleOftalWeb 3.2. The Web -based system has been built on Java Servlet and Java Server Pages (JSP) technologies. Its architecture is a typical three-layered with two databases. The user and authentication information is stored in a relational database: MySQL 5.0. The patient records and fundus images are achieved in an Extensible Markup Language (XML) native database: dbXML 2.0. The application uses XML-based technologies and Health Level Seven/Clinical Document Architecture (HL7/CDA) specifications. The EHR standardization is carried out. The main application object is the universal access to the diabetic patients EHR by physicians wherever they are.

scholarly journals Implementing high‐dimensional propensity score principles to improve confounder adjustment in UK electronic health records

2020 ◽  
Vol 29 (11) ◽  
pp. 1373-1381
Author(s):  
John Tazare ◽  
Liam Smeeth ◽  
Stephen J. W. Evans ◽  
Elizabeth Williamson ◽  
Ian J. Douglas
Keyword(s):  
Propensity Score ◽  
Electronic Health Records ◽  
High Dimensional ◽  
Health Records ◽  
Electronic Health ◽  
Confounder Adjustment

scholarly journals Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records

2021 ◽  
Vol 10 (7) ◽  
pp. 1473
Author(s):  
Ru Wang ◽  
Zhuqi Miao ◽  
Tieming Liu ◽  
Mei Liu ◽  
Kristine Grdinovac ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Early Detection ◽  
Electronic Health Records ◽  
Predictive Model ◽  
Validation Cohort ◽  
Risk Index ◽  
Diabetic Patients ◽  
Health Records ◽  
Derivation Cohort ◽  
Electronic Health

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94,127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

Time-to-Event Predictive Modeling for Chronic Conditions Using Electronic Health Records

2014 ◽  
Vol 29 (3) ◽  
pp. 14-20 ◽  
Author(s):  
Yu-Kai Lin ◽  
Hsinchun Chen ◽  
Randall A. Brown ◽  
Shu-Hsing Li ◽  
Hung-Jen Yang
Keyword(s):  
Electronic Health Records ◽  
Predictive Modeling ◽  
Chronic Conditions ◽  
Time To Event ◽  
Health Records ◽  
Electronic Health

scholarly journals Digital Phenotypes for Personalized Medicine

2021 ◽  
Author(s):  
Carlos Molina ◽  
Belén Prados-Suarez
Keyword(s):  
Health Status ◽  
Personalized Medicine ◽  
Electronic Health Records ◽  
Health State ◽  
Health Records ◽  
The Future ◽  
Electronic Health ◽  
Definition Of

In this paper we propose a new definition of digital phenotype to enrich the formulation with information stored in the Electronic Health Records (EHR) plus data obtained using wearables. On this basis, we describe how to use this formalism to represent the health state of a patient in a given moment (retrospective, present, or future) and how can it be applied for personalized medicine to find out the mutations that should be introduced at present to reach a better health status in the future.

scholarly journals Patient Use and Experience With Online Access to Electronic Health Records in Norway: Results From an Online Survey (Preprint)

2019 ◽  
Author(s):  
Paolo Zanaboni ◽  
Per Egil Kummervold ◽  
Tove Sørensen ◽  
Monika Alise Johansen
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Care Providers ◽  
National Health ◽  
Chronic Conditions ◽  
Online Survey ◽  
Health Condition ◽  
Care Providers ◽  
Health Records ◽  
Electronic Health

BACKGROUND The electronic health record (EHR) has been fully established in all Norwegian hospitals. Patient-accessible electronic health records (PAEHRs) are available to citizens aged 16 years and older through the national health portal Helsenorge. OBJECTIVE This study aimed at understanding how patients use PAEHRs. Three research questions were addressed in order to explore (1) characteristics of users, (2) patients’ use of the service, and (3) patient experience with the service. METHODS We conducted an online survey of users who had accessed their EHR online at least once through the national health portal. Patients from two of the four health regions in Norway were invited to participate. Quantitative data were supplemented by qualitative information. RESULTS A total of 1037 respondents participated in the survey, most of whom used the PAEHR regularly (305/1037, 29.4%) or when necessary (303/1037, 29.2%). Service utilization was associated with self-reported health, age, gender, education, and health care professional background. Patients found the service useful to look up health information (687/778, 88.3%), keep track of their treatment (684/778, 87.9%), prepare for a hospital appointment (498/778, 64.0%), and share documents with their general practitioner (292/778, 37.5%) or family (194/778, 24.9%). Most users found it easy to access their EHR online (965/1037, 93.1%) and did not encounter technical challenges. The vast majority of respondents (643/755, 85.2%) understood the content, despite over half of them acknowledging some difficulties with medical terms or phrases. The overall satisfaction with the service was very high (700/755, 92.7%). Clinical advantages to the patients included enhanced knowledge of their health condition (565/691, 81.8%), easier control over their health status (685/740, 92.6%), better self-care (571/653, 87.4%), greater empowerment (493/674, 73.1%), easier communication with health care providers (493/618, 79.8%), and increased security (655/730, 89.7%). Patients with complex, long-term or chronic conditions seemed to benefit the most. PAEHRs were described as useful, informative, effective, helpful, easy, practical, and safe. CONCLUSIONS PAEHRs in Norway are becoming a mature service and are perceived as useful by patients. Future studies should include experimental designs focused on specific populations or chronic conditions that are more likely to achieve clinically meaningful benefits. Continuous evaluation programs should be conducted to assess implementation and changes of wide-scale routine services over time.

scholarly journals Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records

Informatics ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 17 ◽  
Author(s):  
Sheikh S. Abdullah ◽  
Neda Rostamzadeh ◽  
Kamran Sedig ◽  
Amit X. Garg ◽  
Eric McArthur
Keyword(s):  
Cluster Analysis ◽  
Electronic Health Records ◽  
Dimension Reduction ◽  
Visual Analytics ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Health Records ◽  
Wide Range ◽  
Electronic Health ◽  
And Cluster Analysis

Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such as clustering. Cluster analysis often requires dimension reduction to achieve efficient processing time and mitigate the curse of dimensionality. Given a wide range of techniques for dimension reduction and cluster analysis, it is not straightforward to identify which combination of techniques from both families leads to the desired result. The ability to derive useful and precise insights from EHRs requires a deeper understanding of the data, intermediary results, configuration parameters, and analysis processes. Although these tasks are often tackled separately in existing studies, we present a visual analytics (VA) system, called Visual Analytics for Cluster Analysis and Dimension Reduction of High Dimensional Electronic Health Records (VALENCIA), to address the challenges of high-dimensional EHRs in a single system. VALENCIA brings a wide range of cluster analysis and dimension reduction techniques, integrate them seamlessly, and make them accessible to users through interactive visualizations. It offers a balanced distribution of processing load between users and the system to facilitate the performance of high-level cognitive tasks in such a way that would be difficult without the aid of a VA system. Through a real case study, we have demonstrated how VALENCIA can be used to analyze the healthcare administrative dataset stored at ICES. This research also highlights what needs to be considered in the future when developing VA systems that are designed to derive deep and novel insights into EHRs.

scholarly journals Optimal treatment recommendations for diabetes patients using the Markov decision process along with the South Korean electronic health records

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sang-Ho Oh ◽  
Su Jin Lee ◽  
Juhwan Noh ◽  
Jeonghoon Mo
Keyword(s):  
Electronic Health Records ◽  
Markov Decision Process ◽  
Decision Process ◽  
Recommendation System ◽  
Transition Probability ◽  
Treatment Recommendation ◽  
Diabetic Patients ◽  
Health Records ◽  
Markov Decision ◽  
Electronic Health

AbstractThe extensive utilization of electronic health records (EHRs) and the growth of enormous open biomedical datasets has readied the area for applications of computational and machine learning techniques to reveal fundamental patterns. This study’s goal is to develop a medical treatment recommendation system using Korean EHRs along with the Markov decision process (MDP). The sharing of EHRs by the National Health Insurance Sharing Service (NHISS) of Korea has made it possible to analyze Koreans’ medical data which include treatments, prescriptions, and medical check-up. After considering the merits and effectiveness of such data, we analyzed patients’ medical information and recommended optimal pharmaceutical prescriptions for diabetes, which is known to be the most burdensome disease for Koreans. We also proposed an MDP-based treatment recommendation system for diabetic patients to help doctors when prescribing diabetes medications. To build the model, we used the 11-year Korean NHISS database. To overcome the challenge of designing an MDP model, we carefully designed the states, actions, reward functions, and transition probability matrices, which were chosen to balance the tradeoffs between reality and the curse of dimensionality issues.

scholarly journals A Stochastic Multivariate Irregularly Sampled Time Series Imputation Method for Electronic Health Records

2021 ◽  
Vol 1 (3) ◽  
pp. 166-181
Author(s):  
Muhammad Adib Uz Zaman ◽  
Dongping Du
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Electronic Health Records ◽  
Missing Values ◽  
Time Series Data ◽  
Temporal Information ◽  
Series Data ◽  
High Dimensional ◽  
Health Records ◽  
Electronic Health

Electronic health records (EHRs) can be very difficult to analyze since they usually contain many missing values. To build an efficient predictive model, a complete dataset is necessary. An EHR usually contains high-dimensional longitudinal time series data. Most commonly used imputation methods do not consider the importance of temporal information embedded in EHR data. Besides, most time-dependent neural networks such as recurrent neural networks (RNNs) inherently consider the time steps to be equal, which in many cases, is not appropriate. This study presents a method using the gated recurrent unit (GRU), neural ordinary differential equations (ODEs), and Bayesian estimation to incorporate the temporal information and impute sporadically observed time series measurements in high-dimensional EHR data.

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