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SYSTEM AND METHOD FOR IDENTIFYING COMPLEX PATIENTS, FORECASTING OUTCOMES AND PLANNING FOR POST DISCHARGE CARE

机译:用于识别复杂患者的系统和方法,预测出院后护理的预测结果和规划

摘要

Techniques are described for identifying complex patients and forecasting patient outcomes based on a variety of factors including medical, socio-economic, mental and behavioral. According to an embodiment, a method can include employing one or more machine learning models to identify complex patients and predict patient outcomes like length of stay, potential discharge trajectories with likelihoods, discharge destinations, readmission likelihood and safety. These models are applied to respective patients that are currently admitted to a hospital and expected to be placed after discharge from the hospital, wherein the one or more discharge forecasting machine learning models predict the discharge destinations based on clinical data points and non-clinical data points collected for the respective patients. The method can further include providing discharge information identifying the discharge destinations predicted for the respective patients to one or more care providers to facilitate managing and coordinating inpatient and post-discharge care for the respective patients.
机译:描述了用于鉴定复杂患者的技​​术和基于包括医疗,社会经济,精神和行为的各种因素的患者结果。根据一个实施例,一种方法可以包括采用一个或多个机器学习模型来识别复杂的患者并预测患者结果,如保持长度,潜在的放电轨迹,具有可能性,排出目的地,入院似然和安全性。这些模型适用于当前录取医院的各个患者,并且预计在医院排放后将被放置,其中一个或多个放电预测机器学习模型基于临床数据点和非临床数据点预测排放目的地收集各自的患者。该方法还可以包括提供识别针对各个患者预测的放电目的地的放电信息,以便于为各个患者管理和协调住院病,以及出院后护理。

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