Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study.

BACKGROUND: Older adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals. METHODS: Data were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008-2018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008-2012, 2008-2014, and 2008-2018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full-variable set were used to predict 4-, 6-, and 10-year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model. RESULTS: Random forest in full-variable set and XGBoost in LASSO set were the optimal models for 4-year prediction. Support vector machine was the optimal model for 6-year prediction on both sets. For 10-year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini-Mental State Examination, and psychological well-being score were also important predictors. CONCLUSIONS: Machine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions.

Predicting Alzheimer's Disease with Interpretable Machine Learning.

INTRODUCTION: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. METHODS: We included 1,219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. SHapley Additive exPlanation (SHAP) was applied to identify key predictors of optimal models. RESULTS: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic, self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, logistic regression performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein, and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. CONCLUSION: Interpretable machine learning showed promise in screening high-risk AD individual and could further identify key predictors for targeted prevention.

Latent trajectories of frailty and risk prediction models among geriatric community dwellers: an interpretable machine learning perspective.

BACKGROUND: This study aimed to identify long-term frailty trajectories among older adults (≥65) and construct interpretable prediction models to assess the risk of developing abnormal frailty trajectory among older adults and examine significant factors related to the progression of frailty. METHODS: This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018 (N = 4083). Frailty was defined by the frailty index. The whole study consisted of two phases of tasks. First, group-based trajectory modeling was used to identify frailty trajectories. Second, easy-to-access epidemiological data was utilized to construct machine learning algorithms including naïve bayes, logistic regression, decision tree, support vector machine, random forest, artificial neural network, and extreme gradient boosting to predict the risk of long-term frailty trajectories. Further, Shapley additive explanations was employed to identify feature importance and open-up the black box model of machine learning to further strengthen decision makers' trust in the model. RESULTS: Two distinct frailty trajectories (stable-growth: 82.54%, rapid-growth: 17.46%) were identified. Compared with other algorithms, random forest performed relatively better in distinguishing the stable-growth and rapid-growth groups. Physical function including activities of daily living and instrumental activities of daily living, marital status, weight, and cognitive function were top five predictors. CONCLUSIONS: Interpretable machine learning can achieve the primary goal of risk stratification and make it more transparent in individual prediction beneficial to primary screening and tailored prevention.

Interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database.

OBJECTIVES: To explore the heterogeneous disability trajectories and construct explainable machine learning models for effective prediction of long-term disability trajectories and understanding the mechanisms of predictions among the elderly Chinese at community level. METHODS: This study retrospectively collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study between 2002 and 2018. A total of 4149 subjects aged 65 + in 2002 with completed activities of daily living (ADL) information for at least three waves were included. The mixed growth model was used to identify disability trajectories, and five machine learning models were further established to predict disability trajectories using epidemiological variables. An explainable approach was deployed to understand the model's decisions. RESULTS: Three distinct disability trajectories, including normal class (77.3%), progressive class (15.5%), and high-onset class (7.2%), were identified for three-class prediction. The latter two were further merged into abnormal class, accompanied by normal class for two-class prediction. Machine learning, especially random forest and extreme gradient boosting achieved good performance in both two tasks. ADL, age, leisure activity, cognitive function, and blood pressure were key predictors. CONCLUSION: The findings suggest that machine learning showed good performance and maybe of additional value in analyzing quality indicators in predicting disability trajectories, thereby providing basis to personalize intervention measures.

Machine learning-based prediction of disability risk in geriatric patients with hypertension for different time intervals.

BACKGROUND: The risk of disability in older adults with hypertension is substantially high, and prediction of disability risk is crucial for subsequent management. This study aimed to construct prediction models of disability risk for geriatric patients with hypertension at different time intervals, as well as to assess the important predictors and influencing factors of disability. METHODS: This study collected data from the Chinese Longitudinal Healthy Longevity and Happy Family Study. There were 1576, 1083 and 506 hypertension patients aged 65+ in 2008 who were free of disability at baseline and had completed outcome information in follow-up of 2008-2012, 2008-2014, 2008-2018. We built five machine learning (ML) models to predict the disability risk. The classic statistical logistic regression (classic-LR) and shapley additive explanations (SHAP) was further introduced to explore possible causal factors and interpret the optimal models' decisions. RESULTS: Among the five ML models, logistic regression, extreme gradient boosting, and deep neural network were the optimal models for detecting 4-, 6-, and 10-year disability risk with their AUC-ROCs reached 0.759, 0.728, 0.694 respectively. The classic-LR revealed potential casual factors for disability and the results of SHAP demonstrated important features for risk prediction, reinforcing the trust of decision makers towards black-box models. CONCLUSION: The optimal models hold promise for screening out hypertensive old adults at high risk of disability to implement further targeted intervention and the identified key factors may be of additional value in analyzing the causal mechanisms of disability, thereby providing basis to practical application.

Predicting the long-term cognitive trajectories using machine learning approaches: A Chinese nationwide longitudinal database.

OBJECTIVES: This study aimed to explore the long-term cognitive trajectories and its' determinants, and construct prediction models for identifying high-risk populations with unfavorable cognitive trajectories. METHODS: This study included 3502 older adults aged 65-105 years at their first observations in a 16-year longitudinal cohort study. Cognitive function was measured by the Chinese version Mini Mental State Examination. The heterogeneity of cognitive function was identified through mixed growth model. Machine learning algorithms, namely regularized logistic regression (r-LR), support vector machine (SVM), random forest (RF), and super learner (SL) were used to predict cognitive trajectories. Discrimination and calibration metrics were used for performance evaluation. RESULTS: Two distinct trajectories were identified according to the changes of MMSE scores: intact cognitive functioning (93.6%), and dementia (6.4%). Older age, female gender, Han ethnicity, having no schooling, rural residents, low-frequency leisure activities, and low baseline BADL score were associated with a rapid decline in cognitive function. r-LR, SVM, and SL performed well in predicting cognitive trajectories (Sensitivity: 0.73, G-mean: 0.65). Age and psychological well-being were key predictors. CONCLUSION: Two cognitive trajectories were identified among older Chinese, and the identified key factors could be targeted for constructing early risk prediction models.