Abstract: TH-PO019
Development of Machine Learning-Based Models for Detection of Cognitive Impairment in Patients Receiving Maintenance Hemodialysis
Session Information
- Augmented Intelligence for Prediction and Image Analysis
October 24, 2024 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Augmented Intelligence, Digital Health, and Data Science
- 300 Augmented Intelligence, Digital Health, and Data Science
Authors
- Ling, Tsai-chieh, National Cheng Kung University Hospital, Tainan, Tainan, Taiwan
- Chang, Chiung-Chih, Chang Gung Memorial Hospital Linkou, Kaohsiung, Taiwan
- Wu, Jia-Ling, National Cheng Kung University, Tainan, Taiwan
- Lin, Wei-Ren, National Cheng Kung University Hospital, Tainan, Tainan, Taiwan
- Sun, Chien Yao, National Cheng Kung University Hospital, Tainan, Tainan, Taiwan
- Huang, Chieh-Hsin, National Cheng Kung University Hospital, Tainan, Tainan, Taiwan
- Chang, Yu Tzu, National Cheng Kung University Hospital, Tainan, Taiwan
Background
Cognitive impairment is common but frequently undiagnosed in the dialysis population. There is no recommended screening tool specifically designed for them. We aimed to develop and validate a quick and accurate screening tool composed of only a few items in the Mini-Mental State Examination (MMSE) and Cognitive Abilities Screening Instrument (CASI) using machine-learning based approaches in hemodialysis patients.
Methods
We conducted a cross-sectional observational study in which the MMSE and CASI were administered in 508 hemodialysis patients and randomly divided into a derivation set (70%) and a validation set (30%). Cognitive impairment was defined as a CASI score below the 20th percentile of age- and education-matched norms. Using three to five key items from MMSE and CASI as predictors, we developed six machine learning models, including Lasso, classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGboost), support vector machine (SVM), and artificial neural networks (ANN) in derivation set. We then evaluated the predictive performance of these models in the validation set.
Results
The derivation samples (n = 357) had a mean (SD) age of 64.13 (11.92) years and a mean education level of 8.76 (4.91) years. Around 40% participants were identified as cognitive impairment according to their total CASI score. Among all the developed machine-learning models, the RF model achieved the highest performance of prediction, with an accuracy of 0.94, an area under the curve (AUC) of 0.95, and an F1 score of 0.92 in the validation set. The other models, except for CART, performed equally well in terms of AUC. The RF and SVM models demonstrated the greatest net benefit over clinical thresholds according to decision curve analysis.
Conclusion
Our study demonstrates that using machine-learning models can efficiently identify patients with CASI score < 20 percentile of age- and education-matched norms with only several questions in CASI and MMSE within 5 minutes. Since cognitive impairment implicates decision making and is associated with poor outcomes, early detection might improves patient care and enables timely referral.
Funding
- Clinical Revenue Support