针对急性肾损伤患者分层预测死亡率，提升预后判断精度。
Population stratification for prediction of mortality in post-AKI patients
- 按患者特征分层建模，提升死亡风险预测准确性。
- 多因素影响下分组建模优于统一模型，可更精准识别高危人群。
- 适用于重症监护与出院后随访的临床决策支持系统。
急性肾损伤（AKI）是影响高达20%住院患者的严重临床状况，与短期非计划再入院及出院后死亡风险相关。通过基于预测模型和机器学习的随访规划，可降低患者风险和医疗支出。由于AKI具有多因素特性，针对不同患者类别定制预测模型可提高预测准确性。本文展示了采用该分层策略的部分结果。
原文摘要 · Abstract (English)
Acute kidney injury (AKI) is a serious clinical condition that affects up to 20% of hospitalised patients. AKI is associated with short term unplanned hospital readmission and post-discharge mortality risk. Patient risk and healthcare expenditures can be minimised by followup planning grounded on predictive models and machine learning. Since AKI is multi-factorial, predictive models specialised in different categories of patients can increase accuracy of predictions. In the present article we present some results following this approach.
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