Mungas Dan, Gavett Brandon, Rojas-Saunero L Paloma, Zhou Yixuan, Hayes-Larson Eleanor, Shaw Crystal, Farias Sarah Tomaszewski, Widaman Keith, Fletcher Evan, Corrada Maria M, Gilsanz Paola, Glymour Maria, Olichney John, DeCarli Charles, Whitmer Rachel, Mayeda Elizabeth Rose
Department of Neurology, UC Davis School of Medicine, Sacramento, California, USA.
Department of Epidemiology, University of California Los Angeles Fielding School of Public Health, Los Angeles, California, USA.
Alzheimers Dement. 2025 Aug;21(8):e70508. doi: 10.1002/alz.70508.
Clinical diagnosis (normal cognition, mild cognitive impairment [MCI], dementia) is critical for understanding cognitive impairment and dementia but can be resource intensive and subject to inconsistencies due to complex clinical judgments that are required. Machine learning approaches might provide meaningful additions and/or alternatives to traditional clinical diagnosis.
The study sample was composed of three harmonized longitudinal cohorts of demographically diverse older adults. We used the XGBoost extreme gradient boosting platform to predict clinical diagnosis using different feature sets.
Measures of cognition were especially important predictive features of clinical diagnosis. Prediction accuracy was higher in a sample that had longer follow-up, better balance across diagnostic outcomes, and both self- and informant-report independent function measures.
Algorithmic diagnosis might be a meaningful substitute for clinical diagnosis in studies in which clinical evaluation and diagnosis are not feasible for all participants and may provide a standardized alternative when clinical diagnosis is available.
A machine learning algorithm was used to diagnose cognitive impairment and dementia. Measures of cognition were strongest predictive features for clinical diagnosis. Algorithm accuracy was improved by informant-report independent function measures. Algorithmic diagnosis might be an alternative if clinical diagnosis is not feasible. Standardization is an important advantage of algorithmic diagnosis.
临床诊断（正常认知、轻度认知障碍[MCI]、痴呆）对于理解认知障碍和痴呆至关重要，但由于需要复杂的临床判断，可能资源密集且存在不一致性。机器学习方法可能为传统临床诊断提供有意义的补充和/或替代方案。
研究样本由三个在人口统计学上不同的老年人纵向队列组成。我们使用XGBoost极端梯度提升平台，利用不同的特征集预测临床诊断。
认知测量是临床诊断尤为重要的预测特征。在随访时间更长、诊断结果之间平衡更好且同时有自我报告和知情者报告的独立功能测量的样本中，预测准确性更高。
在对所有参与者进行临床评估和诊断不可行的研究中，算法诊断可能是临床诊断的有意义替代方案，并且在可进行临床诊断时可能提供一种标准化的替代方法。
使用机器学习算法诊断认知障碍和痴呆。认知测量是临床诊断最强的预测特征。通过知情者报告的独立功能测量提高了算法准确性。如果临床诊断不可行，算法诊断可能是一种替代方案。标准化是算法诊断的一个重要优势。
