医疗保健公民科学中的机器学习：范围审查。

IF 3.7 2区医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Medical Informatics Pub Date : 2024-12-19 DOI:10.1016/j.ijmedinf.2024.105766

Ranga Baminiwatte , Blessing Torsu , Dmitry Scherbakov , Abolfazl Mollalo, Jihad S. Obeid, Alexander V. Alekseyenko, Leslie A. Lenert

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引用次数: 0

摘要

目的：本范围审查旨在澄清医疗保健领域内公民主导的科学研究（所谓的公民科学）的定义和轨迹，检查机器学习（ML）的整合程度和公民科学家在健康相关项目中的参与水平。材料和方法：在2024年1月和9月，我们在PubMed、Scopus、Web of Science和EBSCOhost平台上对同行评审的出版物进行了全面搜索，这些出版物结合了医疗保健领域的公民科学和机器学习（ML）。如果公民只是被动的数据提供者，或者只有专业科学家参与，则文章被排除在外。结果：在最初筛选的1395篇文章中，有56篇文章从2013年到2024年符合纳入标准。大多数研究项目在美国进行（n = 20, 35.7%），其次是德国（n = 6, 10.7%），西班牙、加拿大和英国各贡献了3项研究（5.4%）。数据收集是公民科学家参与的主要形式（n = 29, 51.8%），包括捕获图像、在线共享数据和邮寄样本。数据注释是下一个最常见的活动（n = 15,26.8%），其次是参与ML模型挑战（n = 8,14.3%）和决策贡献（n = 3,5.4%）。蚊类（n = 10, 34.5%）和空气污染样本（n = 7, 24.2%）是市民采集ML分析的主要数据对象。分类任务是最常用的ML方法（n = 30, 52.6%），卷积神经网络是最常用的算法（n = 13, 20%）。讨论和结论：医疗保健中的公民科学目前是美国和欧洲的构想，并在亚洲不断扩展。公民正在贡献数据，并为ML方法标记数据，但很少分析或领导研究。使用“众包”数据和“公民科学”的项目应该根据公民的参与程度加以区分。

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Machine learning in healthcare citizen science: A scoping review

Objectives

This scoping review aims to clarify the definition and trajectory of citizen-led scientific research (so-called citizen science) within the healthcare domain, examine the degree of integration of machine learning (ML) and the participation levels of citizen scientists in health-related projects.

Materials and Methods

In January and September 2024 we conducted a comprehensive search in PubMed, Scopus, Web of Science, and EBSCOhost platform for peer-reviewed publications that combine citizen science and machine learning (ML) in healthcare. Articles were excluded if citizens were merely passive data providers or if only professional scientists were involved.

Results

Out of an initial 1,395 screened, 56 articles spanning from 2013 to 2024 met the inclusion criteria. The majority of research projects were conducted in the U.S. (n = 20, 35.7 %), followed by Germany (n = 6, 10.7 %), with Spain, Canada, and the UK each contributing three studies (5.4 %). Data collection was the primary form of citizen scientist involvement (n = 29, 51.8 %), which included capturing images, sharing data online, and mailing samples. Data annotation was the next most common activity (n = 15, 26.8 %), followed by participation in ML model challenges (n = 8, 14.3 %) and decision-making contributions (n = 3, 5.4 %). Mosquitoes (n = 10, 34.5 %) and air pollution samples (n = 7, 24.2 %) were the main data objects collected by citizens for ML analysis. Classification tasks were the most prevalent ML method (n = 30, 52.6 %), with Convolutional Neural Networks being the most frequently used algorithm (n = 13, 20 %).

Discussion and Conclusions

Citizen science in healthcare is currently an American and European construct with growing expansion in Asia. Citizens are contributing data, and labeling data for ML methods, but only infrequently analyzing or leading studies. Projects that use “crowd-sourced” data and “citizen science” should be differentiated depending on the degree of involvement of citizens.

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来源期刊

International Journal of Medical Informatics 医学-计算机：信息系统

CiteScore

8.90

自引率

4.10%

发文量

217

审稿时长

42 days

期刊介绍： International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.