Background: The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents. Objective: This study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status. Methods: Clinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status. Results: Among the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%. Conclusions: The selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.

中文翻译：

---

学习印度的临床共识诊断以促进痴呆症的自动分类：机器学习研究


背景：印度纵向老龄化研究痴呆症统一诊断评估 (LASI-DAD) 是印度第一个也是唯一一个关于晚年认知和痴呆症的全国代表性研究 (n=4096)。 LASI-DAD 获得了 2528 名受访者子样本的痴呆症临床共识诊断。目的：本研究开发了一种机器学习模型，使用 LASI-DAD 临床共识诊断的数据来支持痴呆状态的分类。方法：向临床医生提供从 LASI-DAD 收集的大量数据，包括受访者的社会人口统计信息和健康史、认知状态筛查测试的结果以及从知情者访谈中获得的信息。基于临床痴呆评分（CDR）并使用在线平台，临床医生对每个病例​​进行单独评估，然后达成共识诊断。采用两步程序来训练多个候选机器学习模型，并使用单独的测试集进行评估以进行预测准确性测量，包括受试者工作曲线下面积 (AUROC)、准确性、灵敏度、特异性、精密度、F1 分数、和 kappa 统计量。最终模型是根据 kappa 测量的总体一致性来选择的。我们进一步检查了所选机器学习模型和参与临床共识诊断过程的个体临床医生之间的总体准确性和最终共识诊断的一致性。最后，我们将所选模型应用于未获得临床共识诊断的 LASI-DAD 参与者亚组，以预测他们的痴呆状态。 结果：在接受临床共识诊断的 2528 人中，192 人（调整抽样权重后为 6.7%）被诊断为痴呆症。所有候选机器学习模型都实现了出色的判别能力，如 AUROC>.90 所示，并且具有相似的准确性和特异性（均在 0.95 左右）。支持向量机模型以最高的灵敏度（0.81）、F1分数（0.72）和kappa（0.70，表明基本一致）和第二高的精度（0.65）优于其他模型。因此，选择支持向量机作为最终模型。进一步的检查表明，所选模型和个别临床医生之间的总体准确性和一致性相似。将预测模型应用于 1568 名没有临床共识诊断的个体，将 127 名个体归类为痴呆症患者。应用抽样权重后，我们可以估计人口中痴呆症的患病率为 7.4%。结论：所选的机器学习模型具有出色的判别能力，与痴呆症的临床共识诊断基本一致。该模型可以作为临床共识诊断过程中编码的临床知识和经验的计算机模型，并具有许多潜在的应用，包括预测漏诊的痴呆症诊断以及作为临床决策支持工具或虚拟评估器来协助痴呆症的诊断。