Online Clinical Consensus Diagnosis of Dementia: Development and Validation

Lee, Jinkook; Ganguli, Mary; Weerman, Albert; Chien, Sandy; Lee, Dong Hoon; Varghese, Mathew; Dey, Aparajit Ballav

doi:10.1111/jgs.16736

Cited by 14 publications

(23 citation statements)

References 11 publications

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“…One way to reduce the cost is to replace the in-person consensus conference with a web-based consensus diagnosis approach. This web-based method was implemented first in the Monongahela-Youghiogheny Healthy Aging Team Project [8] and then in the LASI-DAD [9], which developed an online clinical consensus diagnosis platform that provided the detailed information necessary for a clinical assessment [9] and obtained the Clinical Dementia Rating (CDR) for a subsample of the LASI-DAD participants (n=2528).…”

Section: Introductionmentioning

confidence: 99%

Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study

Jin¹,

Chien²,

Meijer³

et al. 2021

JMIR Ment Health

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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.

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Section: Introductionmentioning

confidence: 99%

Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study

Jin¹,

Chien²,

Meijer³

et al. 2021

JMIR Ment Health

Self Cite

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“…The LASI-DAD study drew a subsample aged 60 and older ( N = 4,096) to conduct in-depth assessments of cognition and dementia and informant interviews of their cognitive and health conditions [15]. About 60% of LASI-DAD participants received clinical consensus diagnoses of their dementia status [16]. A machine learning model has been developed and validated to expand the clinical consensus diagnosis of dementia to all LASI-DAD participants [17].…”

Section: Methodsmentioning

confidence: 99%

Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach

et al. 2023

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Introduction. Accurate estimation of dementia prevalence is essential for making effective public and social care policy to support individuals and families suffering from the disease. The purpose of this paper is to estimate the prevalence of dementia in India using a semi-supervised machine learning approach based on a large nationally representative sample. Methods. The sample of this study is adults 60 years or older in the wave 1 (2017-2019) of the Longitudinal Aging Study in India (LASI). A subsample in LASI received extensive cognitive assessment and clinical consensus ratings and therefore have diagnoses of dementia. A semi-supervised machine learning model was developed to predict the status of dementia for LASI participants without diagnoses. After obtaining the predictions, sampling weights and age standardization to the World Health Organization (WHO) standard population were applied to generate the estimate for prevalence of dementia in India. Results. The prevalence of dementia for those aged 60 years and older in India was 8.44% (95% CI: 7.89%~9.01%). The age-standardized prevalence was estimated to be 8.94% (95% CI: 8.36%~9.55%). The prevalence of dementia was greater for those who were older, were females, received no education, and lived in rural areas. Discussion/Conclusion. The prevalence of dementia in India may be higher than prior estimates derived from local studies. These prevalence estimates provide the information necessary for making long-term planning of public and social care policy. The semi-supervised machine learning approach adopted in this paper may also be useful for other large population ageing studies that have a similar data structure.

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“…With the aim of bringing together expert clinical judgement for the diagnosis of dementia, we developed an online clinical consensus panel approach for our study. The online platform provides standardized data necessary for clinicians to rate respondents on the Clinical Dementia Rating (CDR ® ) 16 , and its validity was demonstrated by its comparison to in-person clinical assessment and consensus diagnosis 17 . This approach is a cost-effective alternative to the typical in-person clinical diagnosis consensus conference.…”

Section: Venous Blood Samplementioning

confidence: 99%

“…Clinicians who evaluated the case initially were not always present at the consensus call, and all participating clinicians in the call review and discuss each case. Moderators were asked to mark whether a consensus was reached through discussion (see Lee et al 26 for further details on the online clinical consensus diagnosis). Finally, for Wave 1 of LASI-DAD, clinical consensus ratings were obtained for cases in Phases 2 and 3 only.…”

Section: Venous Blood Samplementioning

confidence: 99%

Deep phenotyping and genomic data from a nationally representative study on dementia in India

et al. 2023

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The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is a nationally representative in-depth study of cognitive aging and dementia. We present a publicly available dataset of harmonized cognitive measures of 4,096 adults 60 years of age and older in India, collected across 18 states and union territories. Blood samples were obtained to carry out whole blood and serum-based assays. Results are included in a venous blood specimen datafile that can be linked to the Harmonized LASI-DAD dataset. A global screening array of 960 LASI-DAD respondents is also publicly available for download, in addition to neuroimaging data on 137 LASI-DAD participants. Altogether, these datasets provide comprehensive information on older adults in India that allow researchers to further understand risk factors associated with cognitive impairment and dementia.

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Online Clinical Consensus Diagnosis of Dementia: Development and Validation

Cited by 14 publications

References 11 publications

Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study

Learning From Clinical Consensus Diagnosis in India to Facilitate Automatic Classification of Dementia: Machine Learning Study

Estimating the Prevalence of Dementia in India Using a Semi-Supervised Machine Learning Approach

Deep phenotyping and genomic data from a nationally representative study on dementia in India

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