Passive Digital Signature for Early Identification of Alzheimer's Disease and Related Dementia

Boustani, Malaz; Perkins, Anthony J.; Khandker, Rezaul Karim; Duong, Stephen; Dexter, Paul; Lipton, Richard B.; Black, Christopher M.; Chandrasekaran, Vasu; Solid, Craig A.; Monahan, Patrick O.

doi:10.1111/jgs.16218

Cited by 15 publications

(28 citation statements)

References 24 publications

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“…Missing cognitive data, particularly when heightened among demographic subsets, affects not only quality of patient care, but also quality of EHR databases increasingly used for research and clinical decision‐making algorithms. Researchers have developed models to identify patients with high risk of AD using various EHR data including the presence of cognitive symptoms in EHR 19–21,37 . Suboptimal data inputs reduce the accuracy and generalizability of EHR‐based models.…”

Section: Discussionmentioning

confidence: 99%

“…We further examined whether the likelihood of having a documented cognitive measure differed by patient age, sex, race, comorbidities, and health‐care use. These results are also informative of the quality and missingness in EHR data, which is increasingly used to develop algorithms to predict incidence of dementia and conduct embedded pragmatic clinical trials 19–22 …”

Section: Introductionmentioning

confidence: 99%

“…These results are also informative of the quality and missingness in EHR data, which is increasingly used to develop algorithms to predict incidence of dementia and conduct embedded pragmatic clinical trials. 19 , 20 , 21 , 22 …”

Section: Introductionmentioning

confidence: 99%

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Cognitive measures lacking in EHR prior to dementia or Alzheimer's disease diagnosis

Maserejian

Krzywy

Eaton

et al. 2021

Alzheimer's & Dementia

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Introduction The extent that cognitive measures are documented in electronic health records (EHR) is important for quality care and addressing disparities in timely diagnosis of dementia or Alzheimer's disease (AD). Methods Analysis of U.S. EHR data to describe the frequency and factors associated with cognitive measures prior to diagnosis of dementia (N = 111,125) or AD (N = 30,203). Results Only 11% of dementia patients and 24% of AD patients had a cognitive measure documented in the 5 years prior to diagnosis. Black race, older age, non‐commercial health insurance, lower mean neighborhood income, greater in‐patient stays, and fewer out‐patient visits were associated with lacking cognitive measures. Discussion Extensive missing cognitive data and differences in the availability of cognitive measures by race, age, and socioeconomic factors hinder patient care and limit utility of EHR for dementia research. Structured fields and prompts for cognitive data inputs at the point of care may help address these gaps.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cognitive measures lacking in EHR prior to dementia or Alzheimer's disease diagnosis

Maserejian

Krzywy

Eaton

et al. 2021

Alzheimer's & Dementia

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“…As a consequence, the incidence of AD has been reported to be higher in those individuals with lower education (16). Previous studies showed that digital health data, cognitive performance such as memory, and neuropsychiatric symptoms can help identify those with dementia from normal subjects (17)(18)(19)(20)(21). Some research groups (19) have suggested that a diagnosis of dementia can be made from health recording data.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of Support-Vector Machines for Diagnosis of Alzheimer's Disease, Using Volume of Brain Obtained by Structural MRI at Siriraj Hospital

et al. 2021

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Background: The determination of brain volumes using visual ratings is associated with an inherently low accuracy for the diagnosis of Alzheimer's disease (AD). A support-vector machine (SVM) is one of the machine learning techniques, which may be utilized as a classifier for various classification problems. This study exploratorily investigated the accuracy of SVM classification models for AD subjects using brain volume and various clinical data as features.Methods: The study was designed as a retrospective chart review. A total of 201 eligible subjects were recruited from the Memory Clinic at Siriraj Hospital, Thailand. Eighteen cases were excluded due to incomplete MRI data. Subjects were randomly assigned to a training group (AD = 46, normal = 46) and testing group (AD = 45, normal = 46) for SVM modeling and validation, respectively. The results in terms of accuracy and a receiver operating characteristic curve analysis are reported.Results: The highest accuracy for brain volumetry (62.64%) was found using the hippocampus as a single feature. A combination of clinical parameters as features provided accuracy ranging between 83 and 90%. However, a combination of brain volumetry and clinical parameters as features to the SVM models did not improve the accuracy of the result.Conclusions: In our study, the use of brain volumetry as SVM features provided low classification accuracy with the highest accuracy of 62.64% using the hippocampus volume alone. In contrast, the use of clinical parameters [Thai mental state examination score, controlled oral word association tests (animals; and letters K, S, and P), learning memory, clock-drawing test, and construction-praxis] as features for SVM models provided good accuracy between 83 and 90%.

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“…In [45], Jammeh et al also evaluated different ML algorithms for AD screening using clinical data and diagnosis records. Boustani et al used electronic medical record data to develop a passive digital signature for early identification of AD through logistic regression [46]. In these studies, no variable selection was performed.…”

Section: Machine Learning and Alzheimer's Diseasementioning

confidence: 99%

Analysis of Risk Factors in Dementia Through Machine Learning

Balea-Fernández

Martinez-Vega

Ortega

et al. 2021

JAD

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Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

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Passive Digital Signature for Early Identification of Alzheimer's Disease and Related Dementia

Cited by 15 publications

References 24 publications

Cognitive measures lacking in EHR prior to dementia or Alzheimer's disease diagnosis

Cognitive measures lacking in EHR prior to dementia or Alzheimer's disease diagnosis

Accuracy of Support-Vector Machines for Diagnosis of Alzheimer's Disease, Using Volume of Brain Obtained by Structural MRI at Siriraj Hospital

Analysis of Risk Factors in Dementia Through Machine Learning

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