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.

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 Electronic Health Records as Valuable Data Sources in the Health Care Quality Improvement Process

2019 ◽  
Vol 6 ◽  
pp. 233339281985287 ◽  
Author(s):  
Katja Wikström ◽  
Maija Toivakka ◽  
Päivi Rautiainen ◽  
Hilkka Tirkkonen ◽  
Teppo Repo ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Early Detection ◽  
Electronic Health Records ◽  
Regional Differences ◽  
Health Care Quality ◽  
Data Retrieval ◽  
Care Quality ◽  
Health Records ◽  
Valuable Data ◽  
Electronic Health

Background: In North Karelia, Finland, the regional electronic health records (EHRs) enable flexible data retrieval and area-level analyses. The aim of this study was to assess the early detection of type 2 diabetes (T2D) in the region and to evaluate the performed activities in order to improve the processes between the years 2012 and 2017. Methods: Patients with T2D were identified from the EHRs using the ICD-10 codes registered during any visit to either primary or specialized care. The prevalence of T2D was calculated for the years 2012, 2015, and 2017 on the municipality level. The number of people found in the EHRs with diabetes was compared with the number found in the national register of medication reimbursement rights. Results: In 2012, the age-adjusted prevalence of T2D in North Karelia varied considerably between municipalities (5.5%-8.6%). These differences indicate variation in the processes of early diagnosis. The findings were discussed in the regional network of health professionals treating patients with T2D, resulting in sharing experiences and best practices. In 2017, the differences had notably diminished, and in most municipalities, the prevalence exceeded 8%. The regional differences in the prevalence and their downward trend were observed both in the EHRs and in the medication reimbursement rights register. Conclusion: Clear differences in the prevalence of T2D were detected between municipalities. After visualizing these differences and providing information for the professionals, the early detection of T2D improved and the regional differences decreased. The EHRs are a valuable data source for knowledge-based management and quality improvement.

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.

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.

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