Enhancing Medical Decision Making: A Semantic Technology-Based Framework for Efficient Diagnosis Inference

Author:

Beimel Dizza12,Albagli-Kim Sivan12

Affiliation:

1. Department of Computer and Information Sciences, Ruppin Academic Center, Emek Hefer 4025000, Israel

2. Dror (Imri) Aloni Center for Health Informatics, Ruppin Academic Center, Emek Hefer 4025000, Israel

Abstract

In the dynamic landscape of healthcare, decision support systems (DSS) confront continuous challenges, especially in the era of big data. Background: This study extends a Q&A-based medical DSS framework that utilizes semantic technologies for disease inference based on a patient’s symptoms. The framework inputs “evidential symptoms” (symptoms experienced by the patient) and outputs a ranked list of hypotheses, comprising an ordered pair of a disease and a characteristic symptom. Our focus is on advancing the framework by introducing ontology integration to semantically enrich its knowledgebase and refine its outcomes, offering three key advantages: Propagation, Hierarchy, and Range Expansion of symptoms. Additionally, we assessed the performance of the fully implemented framework in Python. During the evaluation, we inspected the framework’s ability to infer the patient’s disease from a subset of reported symptoms and evaluated its effectiveness in ranking it prominently among hypothesized diseases. Methods: We conducted the expansion using dedicated algorithms. For the evaluation process, we defined various metrics and applied them across our knowledge base, encompassing 410 patient records and 41 different diseases. Results: We presented the outcomes of the expansion on a toy problem, highlighting the three expansion advantages. Furthermore, the evaluation process yielded promising results: With a third of patient symptoms as evidence, the framework successfully identified the disease in 94% of cases, achieving a top-ranking accuracy of 73%. Conclusions: These results underscore the robust capabilities of the framework, and the enrichment enhances the efficiency of medical experts, enabling them to provide more precise and informed diagnostics.

Publisher

MDPI AG

Reference47 articles.

1. The role of Information and Communication Technologies in healthcare: Taxonomies, perspectives, and challenges;Aceto;J. Netw. Comput. Appl.,2018

2. Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0;Aceto;J. Ind. Inf. Integr.,2020

3. Health 4.0: Applications, management, technologies and review: Array;Estrela;Med. Technol. J.,2018

4. Artificial intelligence in clinical decision support: Challenges for evaluating AI and practical implications;Magrabi;Yearb. Med. Inform.,2019

5. A survey of data mining and deep learning in bioinformatics;Lan;J. Med. Syst.,2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3