APLUS: A Python library for usefulness simulations of machine learning models in healthcare.
APLUS: A Python library for usefulness simulations of machine learning models in healthcare.
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DOI：
10.1016/j.jbi.2023.104319
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发表时间：
2023-03
影响因子：
4.5
通讯作者：
Shah, Nigam H.
中科院分区：
文献类型：
--
作者：
Wornow, Michael;Ross, Elsie Gyang;Callahan, Alison;Shah, Nigam H.
关键词：
尽管创建了数千个机器学习（ML）模型，但用ML改善患者护理的承诺在很大程度上仍未实现。在临床实践中的采用是滞后的，这在很大程度上是由于ML从业者如何评估模型与成功整合到医疗服务中所需的条件之间的脱节。模型只是护理交付工作流程的一个组成部分，其约束条件决定了临床医生对模型输出采取行动的能力。然而，在其相应的工作流程的上下文中评估模型的有用性的方法目前是有限的。为了弥合这一差距，我们开发了APLUS，一个可重复使用的框架，通过模拟定量评估的效用，从集成到临床工作流程的模型。我们描述了APLUS模拟引擎和工作流程规范语言，并将其应用于评估一种新的ML为基础的筛查途径，用于检测外周动脉疾病在斯坦福大学医疗保健。
Despite the creation of thousands of machine learning (ML) models, the promise of improving patient care with ML remains largely unrealized. Adoption into clinical practice is lagging, in large part due to disconnects between how ML practitioners evaluate models and what is required for their successful integration into care delivery. Models are just one component of care delivery workflows whose constraints determine clinicians’ abilities to act on models’ outputs. However, methods to evaluate the usefulness of models in the context of their corresponding workflows are currently limited. To bridge this gap we developed APLUS, a reusable framework for quantitatively assessing via simulation the utility gained from integrating a model into a clinical workflow. We describe the APLUS simulation engine and workflow specification language, and apply it to evaluate a novel ML-based screening pathway for detecting peripheral artery disease at Stanford Health Care.
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