Adelson Robert P, Garikipati Anurag, Maharjan Jenish, Ciobanu Madalina, Barnes Gina, Singh Navan Preet, Dinenno Frank A, Mao Qingqing, Das Ritankar
Montera, Inc. dba Forta, 548 Market St, PMB 89605, San Francisco, CA 94104-5401, USA.
Diagnostics (Basel). 2023 Dec 20;14(1):13. doi: 10.3390/diagnostics14010013.
Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer's disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55-88 years old ( = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24-48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24-48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD.
轻度认知障碍（MCI）是一种认知衰退，可能预示着未来患阿尔茨海默病（AD）的风险。我们基于梯度提升树集成方法开发并验证了一种机器学习算法（MLA），用于分析55至88岁（n = 493）被诊断为MCI的个体的表型数据。在多个预测窗口内对数据进行分析，并取平均值以预测24至48个月内进展为AD的情况。在大多数指标上，MLA在所有预测窗口均优于简易精神状态检查表（MMSE）和三个比较模型。例外情况包括18个月时的灵敏度（MLA和MMSE均达到0.600）；以及30和42个月时的灵敏度（MMSE略优）。对于所有预测窗口，MLA的曲线下面积（AUROC）≥ 0.857，阴性预测值（NPV）≥ 0.800。在24至48个月的前瞻性时间范围内的平均数据上，MLA在所有指标上均优于MMSE。本研究表明，机器学习可能比标准护理提供更准确的风险评估。这可能有助于护理协调，降低医疗保健支出，并维持有从MCI进展为AD风险的患者的生活质量。
