A roadmap to implementing machine learning in healthcare: from concept to practice.

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Frontiers in digital health Pub Date : 2025-01-20 eCollection Date: 2025-01-01 DOI:10.3389/fdgth.2025.1462751

Adam Paul Yan, Lin Lawrence Guo, Jiro Inoue, Santiago Eduardo Arciniegas, Emily Vettese, Agata Wolochacz, Nicole Crellin-Parsons, Brandon Purves, Steven Wallace, Azaz Patel, Medhat Roshdi, Karim Jessa, Bren Cardiff, Lillian Sung

{"title":"A roadmap to implementing machine learning in healthcare: from concept to practice.","authors":"Adam Paul Yan, Lin Lawrence Guo, Jiro Inoue, Santiago Eduardo Arciniegas, Emily Vettese, Agata Wolochacz, Nicole Crellin-Parsons, Brandon Purves, Steven Wallace, Azaz Patel, Medhat Roshdi, Karim Jessa, Bren Cardiff, Lillian Sung","doi":"10.3389/fdgth.2025.1462751","DOIUrl":null,"url":null,"abstract":"Background: The adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve pediatric patient outcomes using electronic health records data.Objective: To provide examples from the PREDICT experience illustrating how common challenges with clinical ML deployment were addressed.Materials and methods: We present common challenges in developing and deploying models in healthcare related to the following: identify clinical scenarios, establish data infrastructure and utilization, create machine learning operations and integrate into clinical workflows.Results: We show examples of how these challenges were overcome and provide suggestions for pragmatic solutions while maintaining best practices.Discussion: These approaches will require refinement over time as the number of deployments and experience increase.","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1462751"},"PeriodicalIF":3.2000,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11788154/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in digital health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fdgth.2025.1462751","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}

引用次数: 0

Abstract

Background: The adoption of machine learning (ML) has been slow within the healthcare setting. We launched Pediatric Real-world Evaluative Data sciences for Clinical Transformation (PREDICT) at a pediatric hospital. Its goal was to develop, deploy, evaluate and maintain clinical ML models to improve pediatric patient outcomes using electronic health records data.

Objective: To provide examples from the PREDICT experience illustrating how common challenges with clinical ML deployment were addressed.

Materials and methods: We present common challenges in developing and deploying models in healthcare related to the following: identify clinical scenarios, establish data infrastructure and utilization, create machine learning operations and integrate into clinical workflows.

Results: We show examples of how these challenges were overcome and provide suggestions for pragmatic solutions while maintaining best practices.

Discussion: These approaches will require refinement over time as the number of deployments and experience increase.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

在医疗保健中实施机器学习的路线图：从概念到实践。

背景：机器学习（ML）在医疗保健领域的应用进展缓慢。我们在一家儿科医院推出了面向临床转化的儿科真实世界评估数据科学（PREDICT）。其目标是开发、部署、评估和维护临床ML模型，以使用电子健康记录数据改善儿科患者的预后。目的：提供来自PREDICT经验的例子，说明如何解决临床ML部署的常见挑战。材料和方法：我们提出了在医疗保健中开发和部署模型的共同挑战，涉及以下方面：确定临床场景，建立数据基础设施和利用，创建机器学习操作并集成到临床工作流程中。结果：我们展示了如何克服这些挑战的示例，并在保持最佳实践的同时为实用的解决方案提供建议。讨论：随着部署数量和经验的增加，这些方法将需要随着时间的推移而改进。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊