Result: ERTool: A Python Package for Efficient Implementation of the Evidential Reasoning Approach for Multi-Source Evidence Fusion.

Title:
ERTool: A Python Package for Efficient Implementation of the Evidential Reasoning Approach for Multi-Source Evidence Fusion.
Authors:
Shi T; Institute of Medical Technology, Peking University Health Science Center, Beijing, China.; National Institute of Health Data Science, Peking University, Beijing, China., Guo L; National Institute of Health Data Science, Peking University, Beijing, China.; Yau Mathematical Sciences Center, Tsinghua University, Beijing, China., Shen Z; Tandon School of Engineering, New York University, New York, NY, USA., Kong G; Institute of Medical Technology, Peking University Health Science Center, Beijing, China.; National Institute of Health Data Science, Peking University, Beijing, China.; Advanced Institute of Information Technology, Peking University, Hangzhou, China.
Source:
Health data science [Health Data Sci] 2024 Aug 05; Vol. 4, pp. 0128. Date of Electronic Publication: 2024 Aug 05 (Print Publication: 2024).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: American Association for the Advancement of Science Country of Publication: United States NLM ID: 9918419276606676 Publication Model: eCollection Cited Medium: Internet ISSN: 2765-8783 (Electronic) Linking ISSN: 27658783 NLM ISO Abbreviation: Health Data Sci Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: Washington, DC : American Association for the Advancement of Science, [2021]-
References:
Comput Biol Med. 2014 Aug;51:140-58. (PMID: 24946259)
Nature. 2020 Sep;585(7825):357-362. (PMID: 32939066)
Entry Date(s):
Date Created: 20240806 Latest Revision: 20240807
Update Code:
20250114
PubMed Central ID:
PMC11299007
DOI:
10.34133/hds.0128
PMID:
39104599
Database:
MEDLINE

Further Information

Background: Multi-source evidence fusion aims to process and combine evidence from different sources to support rational and reliable decision-making. The evidential reasoning (ER) approach is a helpful method to deal with information from multiple sources with uncertainty. It has been widely used in business analytics, healthcare management, and other fields for optimal decision-making. However, computerized implementation of the ER approach usually requires much expertise and effort. At present, some ER-based computerized tools, such as the intelligent decision system (IDS), have been developed by professionals to provide decision support. Nevertheless, IDS is not open source, and the user interfaces are a bit complicated for non-professional users. The lack of a free-to-access and easy-to-use computerized tool limits the application of ER. Methods: We designed and developed a Python package that could efficiently implement the ER approach for multi-source evidence fusion. Further, based on it, we built an online web-based system, providing not only real-time evidence fusion but also visualized illustrations of combined results. Finally, a comparison study between the Python package and IDS was conducted. Results: A Python package, ERTool, was developed to implement the ER approach automatically and efficiently. The online version of the ERTool provides a more convenient way to handle evidence fusion tasks. Conclusions: ERTool, compatible with Python 3 and can be installed through the Python Package Index at https://pypi.org/project/ERTool/, was developed to implement the ER approach. The ERTool has advantages in easy accessibility, clean interfaces, and high computing efficiency, making it a key tool for researchers and practitioners in multiple evidence-based decision-making. It helps bridge the gap between the algorithmic ER and its practical application and facilitates its widespread adoption in general decision-making contexts.
(Copyright © 2024 Tongyue Shi et al.)

Competing interests: G.K. is an editorial board member of the Health Data Science journal, and the authors declare no other competing interests.