Deep neural network models for identifying incident dementia using claims and EHR datasets

Abstract

This study investigates the use of deep learning methods to improve the accuracy of a predictive model for dementia, and compares the performance to a traditional machine learning model. With sufficient accuracy the model can be deployed as a first round screening tool for clinical follow-up including neurological examination, neuropsychological testing, imaging and recruitment to clinical trials. Seven cohorts with two years of data, three to eight years prior to index date, and an incident cohort were created. Four trained models for each cohort, boosted trees, feed forward network, recurrent neural network and recurrent neural network with pre-trained weights, were constructed and their performance compared using validation and test data. The incident model had an AUC of 94.4% and F1 score of 54.1%. Eight years removed from index date the AUC and F1 scores were 80.7% and 25.6%, respectively. The results for the remaining cohorts were between these ranges. Deep learning models can result in significant improvement in performance but come at a cost in terms of run times and hardware requirements. The results of the model at index date indicate that this modeling can be effective at stratifying patients at risk of dementia. At this time, the inability to sustain this quality at longer lead times is more an issue of data availability and quality rather than one of algorithm choices.

A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

“…Deep learning can also help to identify features that are important for disease progression or serve as markers for clinical trials (Ithapu et al, 2015 ). In addition to harvesting brain imaging data [especially the multimodality imaging data from the public ADNI database ( http://adni.loni.usc.edu/ )], deep learning has been applied to biospecimens (Lee et al, 2019b ; Lin et al, 2020 ), electronic health records (Landi et al, 2020 ; Nori et al, 2020 ), speech (Lopez-de-Ipina et al, 2018 ), neuropsychological data (Choi et al, 2018 ; Kang et al, 2019 ), and a combination of MRI and neuropsychological data (Qiu et al, 2018 ; Duc et al, 2020 ). By contrast, few studies have applied deep learning to cognitive task data, which – by design – is supposed to be more sensitive to detect early and mild neurocognitive impairment (Locascio et al, 1995 ; Perry and Hodges, 1999 ).…”

Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

“…Different outcomes were considered by different studies while incorporating ML or AI techniques. Major neurocognitive disorder, dementia, and Alzheimer's disease were the most common conditions among the articles included in our study and were reported in 11 studies ( 7 , 15 , 16 , 24 , 25 , 27 , 28 , 30 , 31 , 33 , 34 ). Among the other outcomes, a geriatric syndrome (falls, malnutrition, dementia, severe urinary control issues, absence of fecal control, visual impairment, walking difficulty, pressure ulcers, lack of social support, and weight loss) in 3 studies ( 18 , 21 , 22 ), delirium in 2 studies ( 20 , 32 ), mild cognitive impairment ( 33 ), cognitive disorder ( 26 ), multimorbidity pattern ( 23 ), mortality ( 29 ), hospital admission ( 17 ), and themes/topics mentioned in care providers' notes ( 19 ) were considered once.…”

“…Support vector machine ( 15 , 31 ) and topic modeling ( 19 , 24 ) were used by two studies each. Finally, lasso ( 33 ), naïve Bayes ( 28 ), multivariate information-based inductive causation (MIIC) network learning algorithm ( 26 ), fuzzy c-means cluster analysis ( 23 ), conditional restricted Boltzmann machine ( 27 ), WEKA ( 15 ), and gradient boosted trees ( 33 ) were applied by one study each.…”

“…After removing duplicates, 363 titles and abstracts were screened for eligibility, and from there, 68 articles were selected for fulltext screening. After assessing full-texts, 21 articles were finally selected for the systematic review (7,(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34). The detailed study selection process is summarized in Figure 1.…”

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

“…In [62] researchers used longitudinal electronic health records from 2007 to 2017 including many features such as subject's age, background and clinical test results. Three models were trained on these data to predict MCI and AD within three to eight years using recurrent neural network (RNN), RNN with trained weights of another model, and a feed forward network.…”