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首页> 外文期刊>The journal of clinical psychiatry >Using data mining to explore complex clinical decisions: A study of hospitalization after a suicide attempt.
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Using data mining to explore complex clinical decisions: A study of hospitalization after a suicide attempt.

机译：使用数据挖掘来探索复杂的临床决策：自杀尝试后的住院研究。

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BACKGROUND: Medical education is moving toward developing guidelines using the evidence-based approach; however, controlled data are missing for answering complex treatment decisions such as those made during suicide attempts. A new set of statistical techniques called data mining (or machine learning) is being used by different industries to explore complex databases and can be used to explore large clinical databases. METHOD: The study goal was to reanalyze, using data mining techniques, a published study of which variables predicted psychiatrists' decisions to hospitalize in 509 suicide attempters over the age of 18 years who were assessed in the emergency department. Patients were recruited for the study between 1996 and 1998. Traditional multivariate statistics were compared with data mining techniques to determine variables predicting hospitalization. RESULTS: Five analyses done by psychiatric researchers using traditional statistical techniques classified 72% to 88% of patients correctly. The model developed by researchers with no psychiatric knowledge and employing data mining techniques used 5 variables (drug consumption during the attempt, relief that the attempt was not effective, lack of family support, being a housewife, and family history of suicide attempts) and classified 99% of patients correctly (99% sensitivity and 100% specificity). CONCLUSIONS: This reanalysis of a published study fundamentally tries to make the point that these new multivariate techniques, called data mining, can be used to study large clinical databases in psychiatry. Data mining techniques may be used to explore important treatment questions and outcomes in large clinical databases and to help develop guidelines for problems where controlled data are difficult to obtain. New opportunities for good clinical research may be developed by using data mining analyses.
机译：背景：医学教育正朝着使用基于证据的方法制定指南的方向发展。但是，缺少用于回答复杂治疗决定（例如在自杀未遂期间做出的决定）的受控数据。不同行业正在使用一套称为数据挖掘（或机器学习）的新统计技术来探索复杂的数据库，并可以用来探索大型临床数据库。方法：研究目标是使用数据挖掘技术重新分析一项已发表的研究，该研究变量预测了精神科医生决定在急诊室接受评估的18岁以上的509名自杀未遂患者中的住院决策。在1996年至1998年之间招募了患者进行研究。将传统的多元统计与数据挖掘技术进行比较，以确定预测住院的变量。结果：精神病学研究人员使用传统统计技术进行的五项分析正确分类了72％至88％的患者。由没有精神病学知识的研究人员使用数据挖掘技术开发的模型使用了5个变量（尝试期间的药物消耗，尝试失败的缓解，缺乏家庭支持，家庭主妇和自杀尝试的家族史）并分类99％的患者正确（99％的敏感性和100％的特异性）。结论：对已发表研究的重新分析从根本上试图指出这些新的多元技术（称为数据挖掘）可用于研究精神病学的大型临床数据库。数据挖掘技术可用于探索大型临床数据库中的重要治疗问题和结果，并帮助制定难以获得受控数据的问题的指南。通过使用数据挖掘分析，可以为良好的临床研究提供新的机会。

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