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Predicting ICU readmissions based on bedside medical text notes

机译：预测基于床边医学文本笔记的ICU阅约

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Patients are often discharged prematurely from Intensive Care Units (ICU) due to clinical resource limitations, economic pressure or poor discharge planning. The readmission of such patients is associated with an increased risk of death and is currently viewed as a marker for poor quality care. Several studies have focused on predicting which patients are likely to be readmitted, using techniques such as logistic regression or machine learning algorithms, and based on physiological data measured during the patients' stay at the ICU. So far, no published algorithms have been able to predict readmissions to a satisfactory degree. In this work we hypothesize that physicians' and nurses' notes could give a better explanation of both ICU discharges and readmissions, and propose using the text notes in an ICU database in order to build classification models for the prediction of readmissions. We tested the use of Fuzzy Fingerprints and other traditional text classifiers and compared them to a previously proposed model based on numerical data, obtaining very relevant improvements in the classification results, namely an AUC=0.8.
机译：患者常常过早地从重症监护病房（ICU）排出，由于临床资源的限制，经济压力或排放计划不周。此类患者再次住院与死亡的风险增加，并且目前被视为对医疗服务质素差的标志。几项研究已经集中于预测哪些患者可能被再次入院，使用诸如逻辑回归或机器学习算法，并根据患者的留在ICU期间测量的生理数据。到目前为止，还没有公布的算法已经能够预测再入院到令人满意的程度。在这项工作中，我们推测，医生和护士的注意事项可能给双方ICU排放和再入院更好的解释，并使用在ICU数据库中的文字说明，以建立分类模型重新接纳的预测建议。我们测试了使用模糊的指纹和其它传统的文本分类器，并将它们与基于数值数据之前提出的模型，获得的分类结果，即是AUC = 0.8密切相关的改进。

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