| 标题 |
 Predicting Discharge to Long-term Acute Care (LTAC) Facility With Machine Learning Using Electronic Health Record Data 相关领域
医学
期限（时间）
健康档案
电子健康档案
医疗急救
急症护理
重症监护医学
医疗保健
经济增长
量子力学
物理
经济
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| DOI |
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| 其它 | RATIONALE: Intensive care is a substantial financial burden to the US healthcare system. These increasing costs for ICU services are driven in part by a subgroup of chronically critically ill patients who are typically dependent on long-term life support. Therefore, the transition between ICU and long-term acute care (LTAC) is a critical juncture in the continuum of care. Improving prediction of LTAC facility disposition could have important benefits to improve resource management and promote decreased costs for patients, payors, and hospitals alike. We aimed to use a machine-learning technique to predict patients' discharge to LTAC based on early clinical indicators in the ICU. |
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