Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm
Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm
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DOI：
10.1186/s12913-020-5005-1
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发表时间：
2020-02-27
影响因子：
2.8
通讯作者：
Hudaib, Abdul-Rahman
中科院分区：
文献类型：
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作者：
Hercus, Catherine;Hudaib, Abdul-Rahman
背景妄想症是会诊-联络精神病学(CLP)常见的诊断方法。许多研究表明，在转诊至中电之前，患者会被误诊。很少有研究考虑使用多变量方法误诊的潜在因素。目的确定转诊时被误诊为CLP的病例数，利用与妄想误诊相关的输入变量，建立准确的预测分类算法。方法在墨尔本阿尔弗雷德医院进行了一项回顾性观察研究，收集了CLP在5个月期间就诊的所有患者的数据。收集的数据与误诊的推定因素有关。建立了机器学习-Logistic回归分类器模型，对准确诊断与误诊的病例进行分类。结果74例新转诊病例中有35例误诊。该预测算法的平均受试者工作特征(ROC)曲线下面积(AUC)为79%，平均分类准确率为72%，灵敏度为77%，特异度为67%。结论：在医院环境中，妄想症常被误诊。我们的研究结果支持机器学习-Logistic预测分类器在医疗保健领域的潜在应用。
Background Delirium is a frequent diagnosis made by Consultation-Liaison Psychiatry (CLP). Numerous studies have demonstrated misdiagnosis prior to referral to CLP. Few studies have considered the factors underlying misdiagnosis using multivariate approaches. Objectives To determine the number of cases referred to CLP, which are misdiagnosed at time of referral, to build an accurate predictive classifier algorithm, using input variables related to delirium misdiagnosis. Method A retrospective observational study was conducted at Alfred Hospital in Melbourne, collecting data from a record of all patients seen by CLP for a period of 5 months. Data was collected pertaining to putative factors underlying misdiagnosis. A Machine Learning-Logistic Regression classifier model was built, to classify cases of accurate delirium diagnosis vs. misdiagnosis. Results Thirty five of 74 new cases referred were misdiagnosed. The proposed predictive algorithm achieved a mean Receiver Operating Characteristic (ROC) Area under the curve (AUC) of 79%, an average 72% classification accuracy, 77% sensitivity and 67% specificity. CONCLUSIONS: Delirium is commonly misdiagnosed in hospital settings. Our findings support the potential application of Machine Leaning-logistic predictive classifier in health care settings.