Improving the Cost Effectiveness of Health Care Through Machine Learning Applied to Large Clinical Databases

9315428 Cooper这个项目将应用于大型医疗数据库，各种最先进的机器学习技术已经在其他领域被证明是有用的。这些学习技术将生成可用于完成以下任务的计算机模型：(1)提出似乎具有成本效益但未被充分利用的一般临床政策；(2)为个别医生提供针对患者的诊断和治疗行动建议，这些建议在临床上似乎同样有效，但成本低于计划行动；(3)建议比计划行动更有效的行动；(4)发现不寻常且可能昂贵的临床情况。调查中使用的数据库包含有关社区获得性肺炎患者的信息。＊＊＊

9315428 Cooper This project will apply to large medical databases a variety of state-of-the-art machine-learning techniques that have proven useful in other domains. These learning techniques will generate computer models that can be used to accomplish the following tasks: (1) suggesting general clinical policies that seem cost-effective and underutilized, (2) providing individual physicians with patient-specific suggestions about diagnostic and therapeutic actions that appear to be equally efficacious clinically but cost less than the planned action, (3) suggesting more effective actions than may be planned, and (4) detection of clinical situations that are unusual and potentially costly. The database to be used in the investigation contains information about patients with community-acquired pneumonia. ***