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Management: Electronic Health Records (EHR)

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Total 33 results found since Jan 2013.

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

Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Source: Frontiers in Neurology - September 27, 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

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

Children with perinatal stroke are at increased risk for autism spectrum disorder: Prevalence and co-occurring conditions within a clinically followed sample
DISCUSSION: Children with perinatal stroke have an increased prevalence of ASD (11.4%) than in the general population. ASD concerns arise at a similar age as the general population, yet ASD is diagnosed almost two years later than the general population and 3.60 years after first concerns present. Co-occurring neurological conditions are common. Clinicians must be aware of increased prevalence and implement screening as part of routine care for all pediatric patients with perinatal stroke.PMID:34308766 | DOI:10.1080/13854046.2021.1955150
Source: The Clinical Neuropsychologist - July 26, 2021 Category: Psychiatry & Psychology Authors: Taralee Hamner Evelyn Shih Rebecca Ichord Lauren Krivitzky Source Type: research

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing
Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.
Source: Frontiers in Neurology - July 13, 2021 Category: Neurology Source Type: research

A Learning Health System Infrastructure for Precision Rehabilitation After Stroke
We describe the creation of a Precision Rehabilitation Data Repository that facilitates access to systematically collected data from the electronic health record as part of a learning health system to drive precision rehabilitation. Specifically, we describe the process of (1) standardizing the documentation of functional assessments, (2) obtaining regulatory approval, (3) defining the patient cohort, and (4) extracting data for the Precision Rehabilitation Data Repository. The development of similar infrastructures at other institutions can help generate large, heterogeneous data sets to drive poststroke care toward preci...
Source: Health Physics - January 12, 2023 Category: Physics Authors: Margaret A French Kelly Daley Annette Lavezza Ryan T Roemmich Stephen T Wegener Preeti Raghavan Pablo Celnik 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

Common clinical blood and urine biomarkers for ischemic stroke: an Estonian Electronic Health Records database study
ConclusionsWe conclude that the EHR database and the risk factors uncovered are valuable resources in screening the population for risk of IS as well as constructing disease risk scores and refining prediction models for IS by ML.
Source: European Journal of Medical Research - March 25, 2023 Category: Research 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

Prediction of incident atrial fibrillation in community-based electronic health records: a systematic review with meta-analysis
Conclusions Models externally validated for prediction of incident AF in community-based EHR demonstrate moderate predictive ability and high risk of bias. Novel methods may provide stronger discriminative performance. Systematic review registration PROSPERO CRD42021245093.
Source: Heart - June 10, 2022 Category: Cardiology Authors: Nadarajah, R., Alsaeed, E., Hurdus, B., Aktaa, S., Hogg, D., Bates, M. G. D., Cowan, C., Wu, J., Gale, C. P. Tags: Open access Arrhythmias and sudden death Source Type: research

Predicting patient-level new-onset atrial fibrillation from population-based nationwide electronic health records: protocol of FIND-AF for developing a precision medicine prediction model using artificial intelligence
Introduction Atrial fibrillation (AF) is a major cardiovascular health problem: it is common, chronic and incurs substantial healthcare expenditure because of stroke. Oral anticoagulation reduces the risk of thromboembolic stroke in those at higher risk; but for a number of patients, stroke is the first manifestation of undetected AF. There is a rationale for the early diagnosis of AF, before the first complication occurs, but population-based screening is not recommended. Previous prediction models have been limited by their data sources and methodologies. An accurate model that uses existing routinely collected data is n...
Source: BMJ Open - November 2, 2021 Category: General Medicine Authors: Nadarajah, R., Wu, J., Frangi, A. F., Hogg, D., Cowan, C., Gale, C. Tags: Open access, Cardiovascular medicine 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