AI 中文总结
该研究针对医疗保健人工智能模型部分观测数据问题，提出特征充分性分析（FSA），通过估计缺失变量分布来评估数据充分性，经案例研究验证其能进行患者特定评估、提供特征排序方法等，助力医疗人工智能系统部署。
AI 中文摘要
实现疾病的早期及时诊断和治疗是一项重大挑战。基于患者数据训练的机器学习算法在预测患者健康状态方面显示出前景，但应用时面临并非所有预测所需临床变量都可用的问题。我们定义了全特征容量（FFC）概念，引入特征充分性分析（FSA）来确定人工智能模型所需临床特征子集是否足以实现FFC，它能估计缺失变量的潜在分布。通过两个案例研究表明FSA可进行患者特定评估，还提供了基于预测充分性的可临床解释的特征排序方法等，有助于医疗人工智能系统的前瞻性部署。
英文摘要
Achieving early and timely diagnosis and treatment for disease is a major challenge. Recent applications of machine learning (ML) algorithms trained on patient data have shown promise in many different settings for predicting the patient health state. A challenge often faced when applying these ML algorithms is that at any given time, not all clinical variables (features) needed as input to perform prediction tasks are available. We define the concept of full-feature-capacity (FFC) to refer to prediction performance when such algorithms make use of all features on which they were trained. We then introduce Feature Sufficiency Analysis (FSA) - an analysis for determining whether a subset of all clinical features needed by an AI model is sufficient to achieve FFC. FSA estimates the underlying distributions of missing variables conditioned on features that are available. FSA provides a patient-specific assessment of whether the existing set of measured features achieves FFC. If yes, then there is no need to acquire further inputs and a ML-based prediction. We provide two case studies: prediction of need for postoperative prolonged ventilation in patients recovering from heart surgery; 10-year mortality prediction in an outpatient cohort. We also demonstrate that FSA also provides a clinically interpretable feature-ranking methodology based on prediction sufficiency, identifies intrinsically hard-to-predict patient populations, and has the potential to perform cost-aware optimization for clinical data acquisition. FSA provides a generic computational approach for determining whether incomplete clinical information is sufficient to support trustworthy AI-assisted clinical decision-making, thereby facilitating the prospective deployment of healthcare AI systems across diverse clinical settings.
