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

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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Adiabatic Markov Decision Process: Convergence of Value Iteration Algorithm

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4032875 ◽

2016 ◽

Vol 138 (6) ◽

Author(s):

Thai Duong ◽

Duong Nguyen-Huu ◽

Thinh Nguyen

Keyword(s):

Markov Decision Process ◽

Decision Process ◽

Transition Probability ◽

Transition Probability Matrix ◽

Rate Of Change ◽

Optimal Decision ◽

Iteration Algorithm ◽

Value Iteration ◽

Markov Decision ◽

Value Iteration Algorithm

Markov decision process (MDP) is a well-known framework for devising the optimal decision-making strategies under uncertainty. Typically, the decision maker assumes a stationary environment which is characterized by a time-invariant transition probability matrix. However, in many real-world scenarios, this assumption is not justified, thus the optimal strategy might not provide the expected performance. In this paper, we study the performance of the classic value iteration algorithm for solving an MDP problem under nonstationary environments. Specifically, the nonstationary environment is modeled as a sequence of time-variant transition probability matrices governed by an adiabatic evolution inspired from quantum mechanics. We characterize the performance of the value iteration algorithm subject to the rate of change of the underlying environment. The performance is measured in terms of the convergence rate to the optimal average reward. We show two examples of queuing systems that make use of our analysis framework.

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

Handbook of Research on Distributed Medical Informatics and E-Health ◽

10.4018/978-1-60566-002-8.ch010 ◽

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.

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

Journal of Clinical Medicine ◽

10.3390/jcm10071473 ◽

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.

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A Recommendation System for Electronic Health Records in the Context of the HOPE Project

2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) ◽

10.1109/cbms52027.2021.00108 ◽

2021 ◽

Author(s):

Ruben Vasallo Gonzalez ◽

Antonio Robles-Gomez ◽

Rafael Pastor-Vargas ◽

Juan M. Haut ◽

Nicolas A. Passadore ◽

...

Keyword(s):

Electronic Health Records ◽

Recommendation System ◽

Health Records ◽

Electronic Health

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Learning and Visualizing Chronic Latent Representations Using Electronic Health Records

10.21203/rs.3.rs-968569/v1 ◽

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.

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Screening diabetes mellitus 2 based on electronic health records using temporal features

Health Informatics Journal ◽

10.1177/1460458216663023 ◽

2016 ◽

Vol 24 (2) ◽

pp. 194-205 ◽

Cited By ~ 3

Author(s):

Angela Pimentel ◽

André V Carreiro ◽

Rogério T Ribeiro ◽

Hugo Gamboa

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Electronic Health Records ◽

Diabetic Patients ◽

Health Records ◽

Temporal Features ◽

Electronic Health ◽

Invasive Techniques

The prevalence of type 2 diabetes mellitus is increasing worldwide. Current methods of treating diabetes remain inadequate, and therefore, prevention with screening methods is the most appropriate process to reduce the burden of diabetes and its complications. We propose a new prognostic approach for type 2 diabetes mellitus based on electronic health records without using the current invasive techniques that are related to the disease (e.g. glucose level or glycated hemoglobin (HbA1c)). Our methodology is based on machine learning frameworks with data enrichment using temporal features. As as result our predictive model achieved an area under the receiver operating characteristics curve with a random forest classifier of 84.22 percent when including data information from 2009 to 2011 to predict diabetic patients in 2012, 83.19 percent when including temporal features, and 83.72 percent after applying temporal features and feature selection. We conclude that he pathology prediction is possible and efficient using the patient’s progression information over the years and without using the invasive techniques that are currently used for type 2 diabetes mellitus classification.

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