Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: A systematic literature review

OBJECTIVE: Alzheimer disease (AD) is the most common cause of dementia, a syndrome characterized by cognitive impairment severe enough to interfere with activities of daily life. We aimed to conduct a systematic literature review (SLR) of studies that applied machine learning (ML) methods to clinical data derived from electronic health records in order to model risk for progression of AD dementia. MATERIALS AND METHODS: We searched for articles published between January 1, 2010, and May 31, 2020, in PubMed, Scopus, ScienceDirect, IEEE Explore Digital Library, Association for Computing Machinery Digital Library, and arXiv. We used predefined criteria to select relevant articles and summarized them according to key components of ML analysis such as data characteristics, computational algorithms, and research focus. RESULTS: There has been a considerable rise over the past 5 years in the number of research papers using ML-based analysis for AD dementia modeling. We reviewed 64 relevant articles in our SLR. The results suggest that majority of existing research has focused on predicting progression of AD dementia using publicly available datasets containing both neuroimaging and clinical data (neurobehavioral status exam scores, patient demographics, neuroimaging data, and laboratory test values). DISCUSSION: Identifying individuals at risk for progression of AD dementia could potentially help to personalize disease management to plan future care. Clinical data consisting of both structured data tables and clinical notes can be effectively used in ML-based approaches to model risk for AD dementia progression. Data sharing and reproducibility of results can enhance the impact, adaptation, and generalizability of this research

Reducing preventable hospitalizations: A study of two models of transitional care

Purpose: Transitional care is an emerging model of health care designed to decrease preventable adverse events and associated utilization of healthcare through temporary follow-up after hospital discharge. This study describes the approach and outcomes of two transitional care programs: one is provided by masters-prepared clinical nurse specialists (CNS) with a chronic disease self-management focus, another by physicians specializing in palliative care (PPC). Existing research has shown that transitional care programs with intensive follow up reduce hospitalizations, emergency room visits, and costs. Few studies, however, have included side-by-side comparisons of the efficacy of transitional care programs varying by health care providers or program focus. Design: This is a retrospective cohort study comparing the number of Emergency Department (ED) visits and hospitalizations in the 120 days before and after the intervention for patients enrolled in each transitional care program. Each program included post-hospitalization home visits, but included difference in program focus (chronic disease vs. palliative), assessment and interventions, and population (rural vs. urban). Data from participants in the CNS program 9/2014 \u27 12/2014 were analyzed (n=98). The average age of participants was 69 and they were 65% female. Data was collected from patients from the PC program from 9/2014 to 4/2015 (n=71). Thirty participants died within 120 days after the intervention and were excluded, the remaining 41 were included in the analysis. Participants had an average age of 81 and were 63% female. Methods: For the CNS program, a secondary analysis of existing data was performed. For the PC program, a review of patient charts was done to collect encounters data. A Wilcoxon Matched-Pair Signed-Rank test was performed to test for significance. Findings: Patients in the CNS intervention had significantly fewer ED visits (p Conclusions: Both transitional programs have value in decreasing health care utilization. The CNS intervention had a more significant effect on ED visits for their target population than the PC program. Further study with randomized control trails is needed to allow for a better understanding of the healthcare workforce best fitted to enhance transitional care outcomes. Future study to examine the cost savings of each of the interventions is also needed

Novel perspectives from existing data on early Alzheimer’s disease pathology and dementia care use

Alzheimer’s disease (AD) has a long disease duration and a progressive course. To stop or slow down cognitive decline as early as possible, intervention studies are increasingly focusing on the earliest stage of the disease. To evaluate the effectiveness of these interventions, one ideally would want to track patients from the earliest preclinical stage, where amyloid pathology exists but cognition is still intact, to the prodromal stage, where cognitive functioning is impaired, to later and increasingly severe stages of dementia. An alternative strategy is to re-use and combine data that were previously collected. Combining different data sources can improve generalizability of findings, efficiency of future clinical trials, and identification of persons best suited for treatment at different disease stages. The aim of this thesis was to examine relevant outcomes and endpoints related to amyloid pathology in pre-dementia stages, and to examine the disease trajectory and care duration after a dementia diagnosis. In this thesis, we used different data sources and data types ranging from biomarker data to registry data to examine relevant outcomes and endpoints in AD. The relevant outcomes and endpoints in this thesis are important for the monitoring of treatment effects and for personalized predictions of whether and how a patient might advance on the AD disease spectrum. Part I focuses on preclinical and prodromal stages of AD, and Part II focuses on the disease trajectory and duration of different types of care after a dementia diagnosis

Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients.

In December 2017, the National Academy of Neuropsychology convened an interorganizational Summit on Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients in Denver, Colorado. The Summit brought together representatives of a broad range of stakeholders invested in the care of older adults to focus on the topic of cognitive health and aging. Summit participants specifically examined questions of who should be screened for cognitive impairment and how they should be screened in medical settings. This is important in the context of an acute illness given that the presence of cognitive impairment can have significant implications for care and for the management of concomitant diseases as well as pose a major risk factor for dementia. Participants arrived at general principles to guide future screening approaches in medical populations and identified knowledge gaps to direct future research. Key learning points of the summit included: recognizing the importance of educating patients and healthcare providers about the value of assessing current and baseline cognition;emphasizing that any screening tool must be appropriately normalized and validated in the population in which it is used to obtain accurate information, including considerations of language, cultural factors, and education; andrecognizing the great potential, with appropriate caveats, of electronic health records to augment cognitive screening and tracking of changes in cognitive health over time

Polymorphisms in the COMT and MAOA genes and their consequences for Clinical, Neuropsychological and Neuroimaging dimensions in a population at High Risk of Schizophrenia

Schizophrenia is a severe an enduring psychiatric condition occurring in around 1% of the general population. In addition to clinical symptoms, sufferers show neuropsychological deficits. Neuroimaging changes including deficits in frontal and temporal lobe structures can be seen in subjects with the condition. Of the many aetiological perspectives of Schizophrenia the heritability of the illness and the role of excess of the neurotransmitter dopamine are important. Dopamine is degraded by two enzymes COMT and MAOA. Thus mutations in the genes controlling the effectiveness of these enzymes may render subjects with a hyperdopaminergic state. This thesis will concentrate on two specific Single Nucleotide Polymorphisms in the MAOA and COMT genes and their consequences on the clinical, neuropsychological and neuroimaging phenotype. The study population for this thesis will be taken from the Edinburgh High Risk Study. This is a prospective cohort of individuals at high risk of schizophrenia due to having two or more relatives with the condition. It is in this population that the effects of the genes may be studied without the contaminating effects of psychotropic medication or other illness factors. The results from this thesis show that COMT genotype can be related to structural and functional neuroimaging changes. Additionally MAOA genotype appears to have a significant effect on affective symptoms and neuropsychological traits. These findings suggest a mechanism for how a hyperdopaminergic state may impact on the Schizophrenia Phenotype

Overlapping phenotypes - a clinical and magnetic resonance imaging investigation of schizotypy and pervasive developmental disorders in adolescents with cognitive impairment

Introduction: The neurobiological bases of pervasive developmental disorders (PDD) and schizotypy are not well established. In addition there are clinical overlaps between the two which can make diagnostic determination difficult. The primary aim of this thesis was to explore the relationship between PDD and schizotypy by examining their associated clinical and brain structural features in a group of cognitively impaired adolescents. Methods: 138 adolescents receiving special educational assistance and 62 typically developing controls were recruited. Schizotypal features were measured using the Structured Interview for Schizotypy (SIS) and PDD features were measured using the Social Communication Questionnaire (SCQ). Each participant also received a standardised clinical interview and a magnetic resonance imaging (MRI) scan. Whole brain volume, midsagittal corpus callosum area and prefrontal lobe volume and gyrification index (GI) were measured using automated, semi-automated and manual region of interest techniques. The subjects in special education were considered in different groupings in three main analyses. In the first, the SIS was used to divide the subjects into those with and without schizotypal features. In the second, the standard SCQ cut-offs were used to divide the subjects into those with autism, those with non-specific pervasive developmental disorder (PDD-NOS) and those with neither. Finally, both the SIS and the SCQ were used contemporaneously to divide the subjects into 6 groups: schizotypal; autistic; PDD-NOS; comorbid schizotypy and autism; comorbid schizotypy and PDD-NOS; and neither schizotypal nor autistic. In each analysis the groups were compared to each other and to the controls with respect to the clinical features and brain structural measures. Results: The schizotypal subjects showed an increase in right prefrontal volume and changes in the anterior and posterior corpus callosum relative to those without schizotypy and the controls. The autism group had reduced right prefrontal GI relative to the other groups as well as anterior callosal changes. The PDD-NOS group had the highest level of psychiatric symptomatology on the CIS, in particular those who were comorbid for PDD-NOS and schizotypy. This comorbid group, both clinically and structurally resembled the schizotypy group rather than the PDD-NOS group. Conclusions: Distinct neuroanatomical differences can be seen in educationally impaired adolescents with schizotypal features and in those with autistic features. These can be related to the observed clinical impairment and may help to distinguish these disorders in the future. It is possible that adolescents with features of both schizotypy and PDD-NOS suffer from an underlying schizophrenia spectrum disorder rather than an autistic spectrum disorder

Transactions of 2015 International Conference on Health Information Technology Advancement Vol.3, No. 1

The Third International Conference on Health Information Technology Advancement Kalamazoo, Michigan, October 30-31, 2015 Conference Chair Bernard Han, Ph.D., HIT Pro Department of Business Information Systems Haworth College of Business Western Michigan University Kalamazoo, MI 49008 Transactions Editor Dr. Huei Lee, Professor Department of Computer Information Systems Eastern Michigan University Ypsilanti, MI 48197 Volume 3, No. 1 Hosted by The Center for Health Information Technology Advancement, WM