Digital Variation of Machine Learning Through Basic Diagnostic Test Application Approach - an Integrative Literature Review

Aug 3, 2026

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Electronic Medical Records (EMRs) use machine learning models to receive and store clinical data for basic diagnostic testing, enabling earlier detection of clinical diagnoses across different populations. An integrative review synthesized new findings from 11 selected articles drawn from four databases, following predetermined abstract and full-text criteria. PRISMA screening identified 1,962 articles, with the final selection used to inform algorithmic diagnostic determination. Network-based models dominated (66.6%), with racial and body-system detection performance reported across studies.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Challenges of Healthcare Data Digitization

Role of EMRs in Early Basic Diagnosis

Data Types and Diagnostic Standardization

Methods (Integrative Review Approach)

Database Search and Selection Criteria

PRISMA Identification and Final Included Studies

Results (Model Performance and Coverage)

Dominant Network Models

Population and Body-System Detection

Racial Representation in Included Articles

Discussion and Implications

Accuracy for Disease Diagnosis in Clinical Settings

Need for Further Application Development

Poltekita: Jurnal Ilmu Kesehatan [http://jurnal.poltekkespalu.ac.id/index.php/JIK](http://jurnal.poltekkespalu.ac.id/index.php/JIK) Vol. 17 No.3 November 2023: Hal. 694-706 p-ISSN: 1907-459X e-ISSN: 2527-7170 Article Review Digital Variation of Machine Learning Through Basic Diagnostic Test Application Approach: an Integrative Literature Review Sanatang 1*, Muhammad Aqmal Ismail1 1 Faculty of Informatics Engineering, State University of Makassar, Makassar, South Sulawesi, Indonesia (Correspondence author email, [sanatang@unm.ac.id](sanatang@unm.ac.id)) ABSTRACT Electronic Medical Records (EMRs) are digital applications of machine learning models that function to receive and store clinical data related to medical information for the purposes of basic clinical diagnostic tests. The integrative review aims to provide a synthesis of new findings from several articles on EMRs for the early detection of basic clinical diagnoses with a variety of existing populations. Using four databases, we reviewed 11 articles. All authors involved review abstracts and full text according to predetermined criteria. The selected articles are then integrated into the publication quality assessment matrix, further included in machine learning algorithms for diagnostic determination of the disease. Reviewed articles are excluded in the form of artificial intelligence. The PRISMA flowchart identified 1962 articles and the final selection found 11 articles. Circulating system networks dominate machine learning models (66. 6%). The study netted an average population of 490.5 and the artificial intelligence system managed to detect 9 body systems from different body systems. A total of 11 articles were selected, more than half of which were Caucasian (80.90%) and white (72.95%), but only 1 article was represented by Caucasian ethnicity, while white race was almost in every article. African-American and Black racial groups were in the middle position at 29.95% and 17.50%. The racial representation with the least percentage below 10% was Hispanic and Asian (6. 10% and 2. 17%). This machine learning has proven to be very accurate for detecting disease diagnoses in hospital, other health clinics. Therefore, the further development of this application for the purpose of establishing clinical diagnosis precisely and accurately. Keywords: Machine Learning, Diagnosis, Electronic Medical Records [https://doi.org/10.33860/jik.v17i3.3355](https://doi.org/10.33860/jik.v17i3.3355) © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY SA) license ([https://creativecommons.org/licenses/by-sa/4.0/](https://creativecommons.org/licenses/by-sa/4.0/)). INTRODUCTION In the era of the industrial revolution 4.0 manufacturing the health industry is growing very rapidly, at least in 2020 it is reported that there are around 2.3 trillion gigabytes of patient data 1. The complexity of the data, if not managed properly, will cause serious problems, especially related to the process of storing and managing and security of patient electronic data in health care facilities. To answer these challenges, an electronic digitization system is needed that is designed to solve problems and accelerate services to patients in healthcare facilities, especially in hospitals. The existence of information technology will greatly help accelerate the process of storing and processing patient electronic data to reduce workload beyond the capacity and work ability of human resources, besides that this system will improve and develop new data automatically in order to help doctors and other health workers to determine early and appropriate diagnosis. With the increasing number of patient visits in hospitals, it certainly has logical consequences for the analysis of very large amounts of data. Therefore, synchronization of electronic digital systems, especially informatics eng

FAQs

What does the integrative review focus on regarding EMRs and machine learning?

The review focuses on how EMRs apply machine learning models to receive and store clinical data and support early basic diagnostic tests across different populations.

How were the articles selected in the review?

The PRISMA flow identified 1,962 articles, and predetermined criteria were used to screen abstracts and full texts from four databases, resulting in 11 included articles.

What diagnostic model characteristics and performance results are highlighted?

Circulating system network architectures dominated machine learning models (66.6%), and the reported system performance included detection across multiple body systems, with summarized population statistics and racial representation across studies.

Aug 3, 2026

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