Alzheimer's & dementia (Amsterdam, Netherlands) · 2026
Advancing dementia identification using machine learning and real-world sequential health data.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Gonzalez-Prieto Cristian, Yelanchezian Mukish, Dobbie Gillian, Wilson Daniel, Rivera-Rodriguez Claudia, Oulaghan Bede, Yates Susan, Cullum Sarah
Аннотация
INTRODUCTION: Timely identification of dementia remains a major clinical challenge globally, with many cases being unrecognized. This study evaluated whether using machine learning models with routinely collected health data can support dementia case finding.
METHOD: De-identified datasets were used to create a nested case-control of 8195 individuals with dementia and 8195 matched controls. Four models incorporating both cross-sectional and longitudinal features were developed and tested.
RESULTS: The best-performing model achieved an area under the curve of 0.86 (95% confidence interval [CI]: 0.84-0.87), with sensitivity of 73.3% (95% CI: 72.3-74.2) and specificity of 87.5% (95% CI: 86.7-88.2). Key predictors included time-stamped International Classification of Diseases 10th Revision diagnostic codes, health-care use, referral to aged residential care, and hospital delirium assessments.
DISCUSSION: A unique feature was the inclusion of "timestamp data" that allowed us to assess the longitudinal changes which may have improved performance of the model. These findings demonstrate the potential for using machine learning with routine health data to enhance early dementia detection.
HIGHLIGHTS: Machine learning models using routine health data in a real-world setting in New Zealand had good accuracy for identification of dementia (area under the curve 0.86, sensitivity 73.3%, and specificity 87.5%).Longitudinal sequential "timestamp" data is a unique feature that improved the performance of machine learning models for dementia case finding.Key predictors included International Classification of Diseases 10th Revision codes, health-care use, referrals to aged residential care, and positive delirium scores.
METHOD: De-identified datasets were used to create a nested case-control of 8195 individuals with dementia and 8195 matched controls. Four models incorporating both cross-sectional and longitudinal features were developed and tested.
RESULTS: The best-performing model achieved an area under the curve of 0.86 (95% confidence interval [CI]: 0.84-0.87), with sensitivity of 73.3% (95% CI: 72.3-74.2) and specificity of 87.5% (95% CI: 86.7-88.2). Key predictors included time-stamped International Classification of Diseases 10th Revision diagnostic codes, health-care use, referral to aged residential care, and hospital delirium assessments.
DISCUSSION: A unique feature was the inclusion of "timestamp data" that allowed us to assess the longitudinal changes which may have improved performance of the model. These findings demonstrate the potential for using machine learning with routine health data to enhance early dementia detection.
HIGHLIGHTS: Machine learning models using routine health data in a real-world setting in New Zealand had good accuracy for identification of dementia (area under the curve 0.86, sensitivity 73.3%, and specificity 87.5%).Longitudinal sequential "timestamp" data is a unique feature that improved the performance of machine learning models for dementia case finding.Key predictors included International Classification of Diseases 10th Revision codes, health-care use, referrals to aged residential care, and positive delirium scores.