Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Yew Pui Ying, Devera Ryan, Liang Yue, Khalifa Razan A El, Sun Ju, Chi Nai-Ching, Chou Ying-Chyi, Tonellato Peter J, Chi Chih-Lin
Аннотация
INTRODUCTION: Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD.
METHODS: We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi-label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors.
RESULTS: Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictive power, syndemic factors related to dementia, social behaviors, mental and physical health can improve model performance further.
DISCUSSION: Our study demonstrated that the MCC patterns among people with ADRD can be learned using a machine-learning approach with syndemic framework adjustments.
HIGHLIGHTS: Machine learning models can learn the MCC patterns for people with ADRD. The learned MCC patterns should be adjusted and individualized by syndemic factors. The model can predict which disease is developing based on existing MCC patterns. As a result, this model enables early specific MCC identification and prevention.
METHODS: We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi-label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors.
RESULTS: Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictive power, syndemic factors related to dementia, social behaviors, mental and physical health can improve model performance further.
DISCUSSION: Our study demonstrated that the MCC patterns among people with ADRD can be learned using a machine-learning approach with syndemic framework adjustments.
HIGHLIGHTS: Machine learning models can learn the MCC patterns for people with ADRD. The learned MCC patterns should be adjusted and individualized by syndemic factors. The model can predict which disease is developing based on existing MCC patterns. As a result, this model enables early specific MCC identification and prevention.