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

9315428库珀这个项目将适用于大型医疗数据库的各种国家的最先进的机器学习技术，已被证明在其他领域的有用。 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）建议似乎具有成本效益且未充分利用的一般临床策略，（2）向个体医生提供关于诊断和治疗行动的患者特定建议，这些建议在临床上似乎同样有效，但成本低于计划的行动，（3）建议比计划更有效的行动，（4）检测不寻常的和潜在昂贵的临床情况。 调查中使用的数据库包含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. ***