Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records

Wang, Liqin; Laurentiev, John; Yang, Jie; Lo, Ying-Chih; Amariglio, Rebecca E.; Blacker, Deborah; Sperling, Reisa A.; Marshall, Gad A.; Zhou, Li

doi:10.1001/jamanetworkopen.2021.35174

Cited by 18 publications

(42 citation statements)

References 26 publications

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“…0.9. 18,[30][31][32] In contrast, our study is the first to focus on predicting impaired performance of daily living activities, utilizing only predictors available in structured patient data from the EHR, making data abstraction and analysis more feasible, reliable, timely, and lower cost compared to also using unstructured data. Despite this difference, our results are comparable to those of previous models.…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning (ML) offers an exciting methodological approach for harnessing EHR data 15,16 to estimate an individual's level of functional impairment. Although recent studies have employed deep learning models to predict cognitive impairment and identify patient subgroups using EHR data, [17][18][19][20][21] there is a significant research gap in developing machine learning models for detecting functional impairment, a crucial clinical outcome for older adults. Machine learning, a type of artificial intelligence that leverages data to enhance classification performance, has the potential to identify functional impairments or predict them by detecting patterns in the data.…”

Section: Why Does This Paper Matter?mentioning

confidence: 99%

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Machine learning functional impairment classification with electronic health record data

Pavon

Previll

Woo

et al. 2023

J American Geriatrics Society

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BackgroundPoor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment.MethodsWe identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t‐distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome.ResultsMedian age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states.ConclusionA machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population‐based strategy for identifying patients with poor functional status who need additional health resources.

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Section: Discussionmentioning

confidence: 99%

Section: Why Does This Paper Matter?mentioning

confidence: 99%

Machine learning functional impairment classification with electronic health record data

Pavon

Previll

Woo

et al. 2023

J American Geriatrics Society

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“…[29][30][31][32][33] Six studies provided complete information on the race and ethnicity of the population studied. 20,22,23,25,28,34 In 12 studies, [21][22][23][24]26,29,32,[34][35][36][37][38] the PDMs were developed through retrospective data extraction and analysis. Eight studies performed retrospective data analysis using a case control design.…”

Section: Studies Characteristicsmentioning

confidence: 99%

“…20,22,23,27,28,30,31,37 One study 33 developed the PDM on retrospective data analysis and evaluated it prospectively in a pilot study prior to a randomized clinical trial. To develop the PDM model, data was extracted from healthcare system's enterprise data warehouses, 23,27,28,33,35,36,38 from state-wide health care data repositories-(Indiana network for patient care or OneFlorida network) 20,22,30 or from specific research cohorts that were merged with EHR. [24][25][26]29,31,32 Except for three studies performed in United Kingdom 26,27,37 and one in Brazil, 32 all other studies took place in the United States.…”

Section: Studies Characteristicsmentioning

confidence: 99%

Passive digital markers for Alzheimer's disease and other related dementias: A systematic evidence review

Taylor

Barboi

Boustani

2023

J American Geriatrics Society

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BackgroundThe timely detection of Alzheimer's disease and other related dementias (ADRD) is suboptimal. Digital data already stored in electronic health records (EHR) offer opportunities for enhancing the timely detection of ADRD by facilitating the development of passive digital markers (PDMs). We conducted a systematic evidence review to identify studies that describe the development, performance, and validity of EHR‐based PDMs for ADRD.MethodsWe searched the literature published from January 2000 to August 2022 and reviewed cross‐sectional, retrospective, or prospective observational studies with a patient population of 18 years or older, published in English that collected and interpreted original data, included EHR as a source of digital data, and had the primary purpose of supporting ADRD care. We extracted relevant data from the included studies with guidance from the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist and used the US Preventive Services Task Force criteria to appraise each study.ResultsWe included and appraised 19 studies. Four studies were considered to have a fair quality, and none was considered to have a good quality. The functionality of the PDMs varied from detecting mild cognitive impairment, Alzheimer's disease or ADRD, to forecasting stages of ADRD. Only seven studies used a valid reference diagnostic method. Nine PDMs used only structured EHR data, and five studies provided complete information on the race and ethnicity of its population. The number of features included in the PDMs ranges from 10 to 853, and the PMDs used a variety of statistical and machine learning algorithms with various time‐at‐risk windows. The area under the curve (AUC) for the PDMs varied from 0.67 to 0.97.ConclusionAlthough we noted heterogeneity in the PDMs development and performance, there is evidence that these PDMs have the potential to detect ADRD at earlier stages.

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“…The ability to detect cognitive decline at earlier stages may necessitate changes to incorporate regular use of brief cognitive assessments by primary care personnel (physicians, nurse practitioners), in other settings (pharmacy, ophthalmology), and/or through community outreach programs. Application of algorithms in electronic health records may potentially identify cognitive decline earlier for those at risk and in need of longitudinal assessment, 28 and such an approach may be acceptable to many patients and caregivers. 29 The second node in the healthcare system patient flow category is timely and accurate diagnosis.…”

Section: Healthcare System Patient Flowmentioning

confidence: 99%

A framework for addressing Alzheimer's disease: Without a frame, the work has no aim

Ball¹,

Mattke

Frank

et al. 2022

Alzheimer's & Dementia

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Confronting Alzheimer's disease (AD) involves patients, healthcare professionals, supportive services, caregivers, and government agencies interacting along a continuum from initial awareness to diagnosis, treatment, support, and care. This complex scope presents a challenge for health system transformation supporting individuals at risk for, or diagnosed with, AD. The AD systems preparedness framework was developed to help health systems identify specific opportunities to implement and evaluate focused improvement programs. The framework is purposely flexible to permit local adaptation across different health systems and countries. Health systems can develop solutions tailored to system-specific priorities considered within the context of the overall framework. Example metric concepts and initiatives are provided for each of ten areas of focus. Examples of funded projects focusing on screening and early detection are provided. It is our hope that stakeholders utilize the common framework to generate and share additional implementation evidence to benefit individuals with AD.

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Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records

Cited by 18 publications

References 26 publications

Machine learning functional impairment classification with electronic health record data

Machine learning functional impairment classification with electronic health record data

Passive digital markers for Alzheimer's disease and other related dementias: A systematic evidence review

A framework for addressing Alzheimer's disease: Without a frame, the work has no aim

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