Qinzheng Li1, Yanli Zhao2, Yu Chen3,4, Jirong Yue5, Yan Xiong6. 1. School of Mechanical Engineering, Sichuan University, Chengdu, China. 2. Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China. 3. Department of Applied Mechanics, Sichuan University, Chengdu, China. 4. Medical Big Data Center, Sichuan University, Chengdu, China. 5. Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, China. yuejirong11@hotmail.com. 6. School of Mechanical Engineering, Sichuan University, Chengdu, China. xy@scu.edu.cn.
Abstract
PURPOSE: To develop a machine learning model that predicts delirium risk in geriatric internal medicine inpatients. METHODS: A prospective cohort study of internal medicine wards in a tertiary care hospital in China. Blinded observers assessed delirium using the Confusion Assessment Method (CAM). The data set was randomly divided into a training set (70%) and a test set (30%). The model was trained on the training set using the decision tree and the five-fold cross-validation, and then the model performance was evaluated on the test set. Under-sampling was used to address the class imbalance. The discriminatory power of the model was measured by the area under the receiver operating characteristic curve (AUC) and F1 score. The data set comprised 740 patients from March 2016 to January 2017. RESULTS: The training set included 518 patients; the median (IQR) age was 84 (79-87) years; 364 (70.3%) were men; 71 (13.7%) with delirium. The test set included 222 patients; the median (IQR) age was 84.5 (79-87) years; 163 (73.4%) were men; 30 (13.5%) with delirium. In total, the data set included 740 hospital admissions with a median (IQR) age of 84 (79-87) years, 527 (71.2%) were men, and 101 (13.6%) with delirium. From 32 potential predictors, we included five variables in the predictive model: depression, cognitive impairment, types of drugs, nutritional status, and activity of daily life (ADL). The mean AUC on the training set was 0.967, the AUC and F1 score on the test set was 0.950 and 0.810, respectively. The model achieved 93.3% sensitivity, 94.3% specificity, 71.8% positive predictive value, 98.9% negative predictive value, and 94.1% accuracy on the test set. CONCLUSION: This machine learning model may allow more precise targeting of delirium prevention and could support clinical decision making in geriatric internal medicine wards.
PURPOSE: To develop a machine learning model that predicts delirium risk in geriatric internal medicine inpatients. METHODS: A prospective cohort study of internal medicine wards in a tertiary care hospital in China. Blinded observers assessed delirium using the Confusion Assessment Method (CAM). The data set was randomly divided into a training set (70%) and a test set (30%). The model was trained on the training set using the decision tree and the five-fold cross-validation, and then the model performance was evaluated on the test set. Under-sampling was used to address the class imbalance. The discriminatory power of the model was measured by the area under the receiver operating characteristic curve (AUC) and F1 score. The data set comprised 740 patients from March 2016 to January 2017. RESULTS: The training set included 518 patients; the median (IQR) age was 84 (79-87) years; 364 (70.3%) were men; 71 (13.7%) with delirium. The test set included 222 patients; the median (IQR) age was 84.5 (79-87) years; 163 (73.4%) were men; 30 (13.5%) with delirium. In total, the data set included 740 hospital admissions with a median (IQR) age of 84 (79-87) years, 527 (71.2%) were men, and 101 (13.6%) with delirium. From 32 potential predictors, we included five variables in the predictive model: depression, cognitive impairment, types of drugs, nutritional status, and activity of daily life (ADL). The mean AUC on the training set was 0.967, the AUC and F1 score on the test set was 0.950 and 0.810, respectively. The model achieved 93.3% sensitivity, 94.3% specificity, 71.8% positive predictive value, 98.9% negative predictive value, and 94.1% accuracy on the test set. CONCLUSION: This machine learning model may allow more precise targeting of delirium prevention and could support clinical decision making in geriatric internal medicine wards.
Authors: Vanja C Douglas; Christine S Hessler; Gurpreet Dhaliwal; John P Betjemann; Keiko A Fukuda; Lama R Alameddine; Rachael Lucatorto; S Claiborne Johnston; S Andrew Josephson Journal: J Hosp Med Date: 2013-08-07 Impact factor: 2.960
Authors: Juan Antonio Martinez; Ana Belastegui; Iban Basabe; Xabier Goicoechea; Cristina Aguirre; Nerea Lizeaga; Iratxe Urreta; Jose Ignacio Emparanza Journal: BMJ Open Date: 2012-09-14 Impact factor: 2.692