Analysis and visualization of longitudinal assessments of clinical, functional and psychosocial state of AD patients from the Massachusetts home care program

项目总结 这项研究将支持MassAITC调查人员与 马萨诸塞州联邦老年事务执行办公室(EOEA) 管理马萨诸塞州的家庭护理计划。EOEA支持27个人的网络 管理马萨诸塞州家庭护理计划的地区外地机构。EOEA 已与老龄化服务接入点(ASAPS)签订了合同，以确保所有消费者 以适当和具有成本效益的方式接受全面的临床评估。 为此，参与该计划的护理经理和/或注册护士 在每次家访时填写一份详尽的病人记录。麻省理工学院提议 使用这些独特的数据为几个不同的任务开发机器学习模型，这些任务 将有助于理解预测提前终止的因素。我们的最终目标是 确定可能导致干预的可修改的预测因素，并作为实现 为了达到这个目标，这个项目首先清理数据，使其为机器学习做好准备 分析和探索有足够数据的预测问题。

Project summary This research will support MassAITC investigators in a collaboration with the Commonwealth of Massachusetts Executive Office of Elder Affairs (EOEA), which administers state home care programs in Massachusetts. EOEA support a network of 27 regional field agencies administering the Massachusetts state home care program. EOEA has contracted with Aging Service Access Points (ASAPs) to ensure that all consumers receive a comprehensive clinical evaluation in an appropriate and cost-effective manner. To this end, a Care Manager and/or Registered Nurse who is part of the program completes an extensive patient record with each home visit. MassAITC is proposing to use these unique data to develop machine learning models for several different tasks that will help to understand the factors that predict early termination. Our eventual goal is to identify modifiable predictors that could lead to interventions, and as a first step towards that goal, this project begins by cleaning the data to make it ready for machine learning analysis and explore the prediction problems for which sufficient data is present.