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One-Third of COVID-19 Survivors May Develop a Neuropsychiatric Disorder Within Months of Infection
One-third of individuals diagnosed with COVID-19 developed a psychiatric or neurological problem within six months of their diagnosis, according to astudy published Tuesday inThe Lancet Psychiatry. The prevalence of a post-COVID neurologic or psychiatric diagnosis was even greater among individuals with severe illness who had required hospitalization.“Given the size of the pandemic and the chronicity of many of the diagnoses and their consequences (for example, dementia, stroke, and intracranial hemorrhage), substantial effects on health and social care systems are likely to occur,” wrote Maxime Taque, Ph.D., of the Un...
Source: Psychiatr News - April 7, 2021 Category: Psychiatry Tags: anxiety COVID-19 electronic health records hospitalizations mood disorders neuropsychiatric disorders The Lancet Psychiatry Source Type: research

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Source: Frontiers in Neurology - March 31, 2021 Category: Neurology Source Type: research

Sensors, Vol. 21, Pages 460: Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction
wan We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (...
Source: Sensors - January 11, 2021 Category: Biotechnology Authors: Yun-Hsuan Chen Mohamad Sawan Tags: Review Source Type: research

Multivariable Risk Prediction of Dysphagia in Hospitalized Patients Using Machine Learning.
CONCLUSION: The developed models outperformed previously published models predicting dysphagia. In future, an implementation in the clinical workflow is needed to determine the clinical benefit. PMID: 32578538 [PubMed - in process]
Source: Studies in Health Technology and Informatics - June 26, 2020 Category: Information Technology Tags: Stud Health Technol Inform Source Type: research

Consider the Promises and Challenges of Medical Image Analyses Using Machine Learning
Medical imaging saves millions of lives each year, helping doctors detect and diagnose a wide range of diseases, from cancer and appendicitis to stroke and heart disease. Because non-invasive early disease detection saves so many lives, scientific investment continues to increase. Artifical intelligence (AI) has the potential to revolutionize the medical imaging industry by sifting through mountains of scans quickly and offering providers and patients with life-changing insights into a variety of diseases, injuries, and conditions that may be hard to detect without the supplemental technology. Images are the largest source...
Source: MDDI - June 2, 2020 Category: Medical Devices Authors: Partha S. Anbil and Michael T. Ricci Tags: Imaging Source Type: news

Deep learning approach for diabetes prediction using PIMA Indian dataset
ConclusionThe outcome of the study confirms that DL provides the best results with the most promising extracted features. DL achieves the accuracy of 98.07% which can be used for further development of the automatic prognosis tool. The accuracy of the DL approach can further be enhanced by including the omics data for prediction of the onset of the disease.
Source: Journal of Diabetes and Metabolic Disorders - April 13, 2020 Category: Endocrinology Source Type: research

Assessing stroke severity using electronic health record data: a machine learning approach
Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text i...
Source: BMC Medical Informatics and Decision Making - January 8, 2020 Category: Information Technology Authors: Emily Kogan, Kathryn Twyman, Jesse Heap, Dejan Milentijevic, Jennifer H. Lin and Mark Alberts Tags: Research article Source Type: research

Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event
Conclusion: The clinical risk scores that currently exist for predicting short-term and long-term risk of recurrent cerebral ischemia are limited in their performance and clinical utilities. There is a need for a better predictive tool which can overcome the limitations of current predictive models. Application of machine learning methods in combination with electronic health records may provide platform for development of new-generation predictive tools.
Source: Frontiers in Neurology - November 11, 2019 Category: Neurology Source Type: research

Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing
This study sought to use natural language processing of electronic health records (EHR) combined with machine learning methods to automate IS subtyping. Methods: Among IS patients from an observational registry with TOAST subtyping adjudicated by board-certified vascular neurologists, we analyzed unstructured text-based EHR data including neurology progress notes and neuroradiology reports using natural language processing.
Source: Journal of Stroke and Cerebrovascular Diseases - May 14, 2019 Category: Neurology Authors: Ravi Garg, Elissa Oh, Andrew Naidech, Konrad Kording, Shyam Prabhakaran Source Type: research

More Research Is Needed on Lifestyle Behaviors That Influence Progression of Parkinson's Disease
This article highlights some of these challenges in the design of lifestyle studies in PD, and suggests a more coordinated international effort is required, including ongoing longitudinal observational studies. In combination with pharmaceutical treatments, healthy lifestyle behaviors may slow the progression of PD, empower patients, and reduce disease burden. For optimal care of people with PD, it is important to close this gap in current knowledge and discover whether such associations exist. Introduction Parkinson's disease (PD) is an age-related complex progressive neurodegenerative disorder, with key p...
Source: Frontiers in Neurology - April 29, 2019 Category: Neurology Source Type: research

A Novel Deep Neural Network Model for Multi-Label Chronic Disease Prediction
Conclusions concludes this work along with future work. Dataset and Data Preprocessing In the work, we mainly focus on multiple chronic disease classification. It can be formulated into a multi-label classification problem. There are three common chronic diseases are selected from the physical examination records: hypertension (H), diabetes (D), and fatty liver (FL). In the experiments, the physical examination datasets are collected from a local medical center, which contain 110,300 physical examination records from about 80,000 anonymous patients (Li et al., 2017a,b). Sixty-two feature items are selected from over 100...
Source: Frontiers in Genetics - April 23, 2019 Category: Genetics & Stem Cells Source Type: research

Novel electronic health record (EHR) education intervention in large healthcare organization improves quality, efficiency, time, and impact on burnout
A novel approach to advanced electronic health record (EHR) skills training was developed in a large healthcare organization to improve high-quality EHR documentation, while reducing stressors linked to physician burnout. The 3-day intensive EHR education intervention covered best practices in EHR documentation and physician well-being. The specialty physician faculty used interactive teaching including demonstration, facilitation, and individual coaching. Laptops were provided for hands-on practice. Mixed-method evaluation included real-time feedback, daily surveys, and post-activity surveys to measure participant learni...
Source: Medicine - September 1, 2018 Category: Internal Medicine Tags: Research Article: Quality Improvement Study Source Type: research

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning
Conclusions: With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.
Source: Journal of Medical Internet Research - January 30, 2018 Category: General Medicine Authors: Chengyin Ye Tianyun Fu Shiying Hao Yan Zhang Oliver Wang Bo Jin Minjie Xia Modi Liu Xin Zhou Qian Wu Yanting Guo Chunqing Zhu Yu-Ming Li Devore S Culver Shaun T Alfreds Frank Stearns Karl G Sylvester Eric Widen Doff McElhinney Xuefeng Ling Source Type: research

Developing a New Score: How Machine Learning Improves Risk Prediction
Composite risk scores have been used for decades to identify disease risk and health status in the general population. However, current approaches often fail to identify people who would benefit from intervention or recommend unnecessary intervention. Machine learning promises to improve accuracy, ensuring targeted treatment for patients that need it and reducing unnecessary intervention. Framingham Risk Score, the gold standard for predicting the likelihood of heart disease, predicts hospitalizations with about 56% accuracy. It uses factors such as age, gender, smoking, cholesterol levels, and systolic blood pressure to...
Source: MDDI - November 17, 2017 Category: Medical Devices Authors: Heather R. Johnson Tags: R & D Source Type: news

Validity of Cardiovascular Data From Electronic Sources:The Multi-Ethnic Study of Atherosclerosis and HealthLNK.
Conclusions -These findings illustrate the limitations and strengths of electronic data repositories compared with information collected by traditional standardized epidemiologic approaches for the ascertainment of CVD risk factors and events. PMID: 28687707 [PubMed - as supplied by publisher]
Source: Circulation - July 7, 2017 Category: Cardiology Authors: Ahmad FS, Chan C, Rosenman MB, Post WS, Fort DG, Greenland P, Liu KJ, Kho A, Allen NB Tags: Circulation Source Type: research

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