Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data

被引:17
作者
Mar, Javier [1 ,2 ,3 ,4 ]
Gorostiza, Ania [1 ,2 ]
Ibarrondo, Oliver [1 ,2 ,3 ]
Cernuda, Carlos [5 ]
Arrospide, Arantzazu [1 ,2 ,3 ,4 ]
Iruin, Alvaro [3 ,6 ]
Larranaga, Igor [1 ,2 ]
Tainta, Mikel [2 ,7 ,8 ]
Ezpeleta, Enaitz [5 ]
Alberdi, Ane [5 ]
机构
[1] Debagoiena Integrated Healthcare Org, Basque Hlth Serv Osakidetza, Res Unit, Arrasate Mondragon, Guipuzcoa, Spain
[2] Kronikgune Inst Hlth Serv Res, Baracaldo, Spain
[3] Biodonostia Hlth Res Inst, Donostia San Sebastian, Guipuzcoa, Spain
[4] Hlth Serv Res Chron Patients Network REDISSEC, Bilbao, Vizcaya, Spain
[5] Mondragon Unibertsitatea, Fac Engn, Elect & Comp Dept, Arrasate Mondragon, Gipuzkoa, Spain
[6] Basque Hlth Serv Osakidetza, Gipuzkoa Mental Hlth Network, Donostia San Sebastian, Guipuzcoa, Spain
[7] Goierri Urola Garaia Integrated Healthcare Org, Dept Neurol, Basque Hlth Serv Osakidetza, Zumarraga, Guipuzcoa, Spain
[8] Fdn CITA Alzheimer Fundazioa, Donostia San Sebastian, Guipuzcoa, Spain
关键词
Dementia; depressive symptoms; machine learning; neuropsychiatric symptoms; predictive model; prevalence; psychotic symptoms; real-world data; ADMINISTRATIVE DATA; ALZHEIMERS-DISEASE; PREVALENCE; EPIDEMIOLOGY; AGITATION;
D O I
10.3233/JAD-200345
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Background: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated. Objective: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers. Methods: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS. Second, machine learning (random forest) algorithms were applied to build separate predictive models for psychotic and depressive symptom clusters in the training set (N = 3,003). Third, calibration and discrimination were assessed in the test set (N = 1,000) to assess the performance of the models. Results: Neuropsychiatric symptoms were noted in the electronic health record of 58% of patients. The area under the receiver operating curve reached 0.80 for the psychotic cluster model and 0.74 for the depressive cluster model. The Kappa index and accuracy also showed better discrimination in the psychotic model. Calibration plots indicated that both types of model had less predictive accuracy when the probability of neuropsychiatric symptoms was <25%. The most important variables in the psychotic cluster model were use of risperidone, level of sedation, use of quetiapine and haloperidol and the number of antipsychotics prescribed. In the depressive cluster model, the most important variables were number of antidepressants prescribed, escitalopram use, level of sedation, and age. Conclusion: Given their relatively good performance, the predictive models can be used to estimate prevalence of NPS in population databases.
引用
收藏
页码:855 / 864
页数:10
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