Electronic Health Records Machine Learning
Listing Websites about Electronic Health Records Machine Learning
Implementing Machine Learning in the Electronic Health …
(6 days ago) WebMachine learning (ML) holds significant promise for improving clinical care.1 To facilitate their appropriate and effective use, it is important that clinical guidance based on these ML models is provided automatically to end users as a part of routine clinical care …
https://www.mayoclinicproceedings.org/article/S0025-6196(23)00020-4/fulltext
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Deep representation learning of electronic health records …
(9 days ago) WebDeriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. In Proc. Machine Learning for Healthcare, Vol. 56 (eds Doshi-Velez, F. et al
https://www.nature.com/articles/s41746-020-0301-z
Category: Medicine Show Health
Machine Learning for Multimodal Electronic Health …
(2 days ago) WebThis paper reviews studies that use structured and unstructured data from EHRs as input for ML or DL models. It discusses the advantages and limitations of different fusion methods and future directions for multimodal EHR research.
https://arxiv.org/abs/2111.04898
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Using machine learning to identify health outcomes from …
(1 days ago) WebPurpose of review: Electronic health records (EHRs) contain valuable data for identifying health outcomes, but these data also present numerous challenges when creating computable phenotyping algorithms. Machine learning methods could help with some of …
https://pubmed.ncbi.nlm.nih.gov/30555773/
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Using AI to Improve Electronic Health Records - Harvard …
(9 days ago) WebUltimately, AI should help doctors tailor EHRs to their specific needs and work styles making them easier to use and more …
https://hbr.org/2018/12/using-ai-to-improve-electronic-health-records
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Scalable and accurate deep learning with electronic …
(9 days ago) WebPredictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. In Proceedings of the 1st Machine Learning for
https://www.nature.com/articles/s41746-018-0029-1
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Machine learning approaches for electronic health …
(5 days ago) WebAccurate and rapid phenotyping is a prerequisite to leveraging electronic health records for biomedical research. While early phenotyping relied on rule-based algorithms curated by experts, machine learning (ML) approaches have emerged as an …
https://academic.oup.com/jamia/article/30/2/367/6839857
Category: Medical Show Health
Machine learning approaches for electronic health …
(1 days ago) WebAccurate and rapid methods for phenotyping are a prerequisite to realizing the potential of electronic health records (EHRs) data for clinical and translational research. This study reviews the literature on machine learning (ML) approaches for …
https://www.medrxiv.org/content/10.1101/2022.04.23.22274218v1
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Learning from Longitudinal Data in Electronic Health …
(Just Now) WebIn this study, we applied machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR) and genetic data. Our study cohort …
https://www.nature.com/articles/s41598-018-36745-x
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A Survey of Deep Learning for Electronic Health Records
(2 days ago) WebMedical data is an important part of modern medicine. However, with the rapid increase in the amount of data, it has become hard to use this data effectively. The development of machine learning, such …
https://www.mdpi.com/2076-3417/12/22/11709
Category: Medical, Medicine Show Health
Electronic Health Records as Source of Research Data
(5 days ago) WebElectronic health records (EHRs) are the collection of all digitalized information regarding individual’s health. EHRs are not only the base for storing clinical information for archival purposes, but they are …
https://www.ncbi.nlm.nih.gov/books/NBK597466/
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Machine Learning in Healthcare - PMC - National Center for
(3 days ago) WebIn large medical organizations, machine learning-based approaches have also been implemented to achieve increased efficiency in the organization of electronic health records , identification of irregularities in the blood samples , organs [6-8], and …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8822225/
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Implementing Machine Learning in the Electronic Health Record
(1 days ago) Web1 Department of Biomedical Informatics, University of Utah, Salt Lake City, UT. Electronic address: [email protected]. 2 Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY. 3 Department of …
https://pubmed.ncbi.nlm.nih.gov/36868743/
Category: Medical, Medicine Show Health
A novel electronic health record-based, machine-learning model …
(8 days ago) WebA novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study. Mai Shi, Aimin Yang, Eric S. H. Lau, Andrea O. Y. Luk, …
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004369
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Electronic Medical Records and Machine Learning in - IntechOpen
(1 days ago) WebElectronic medical records (EMRs) were primarily introduced as a digital health tool in hospitals to improve patient care, but over the past decade, research works have implemented EMR data in clinical trials and omics studies to increase translational …
https://www.intechopen.com/chapters/72352
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Using Machine Learning to Identify Health Outcomes from …
(9 days ago) WebPurpose of Review Electronic health records (EHRs) contain valuable data for identifying health outcomes, but these data also present numerous challenges when creating computable phenotyping algorithms. Machine learning methods could help with …
https://link.springer.com/article/10.1007/s40471-018-0165-9
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Transforming Patient Monitoring with Machine Learning
(Just Now) WebTransforming Patient Monitoring with Machine Learning. David Kim, MD, PhD and his team are developing software that synthesizes data from electronic health records and physiologic monitors in real-time to provide more specific and accurate information about …
https://emed.stanford.edu/stories/diagnostics.html
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Machine Learning for Multimodal Electronic Health Records-Based
(2 days ago) WebA machine learning algorithm for identifying atopic dermatitis in adults from electronic health records. In: Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017, pp. 83–90 (2017).
https://link.springer.com/chapter/10.1007/978-981-19-9865-2_10
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Machine learning model to predict mental health crises from …
(Just Now) WebMachine learning applied on electronic health records can predict mental health crises 28 days in advance and become a clinically valuable tool for managing caseloads and mitigating the risk of
https://www.nature.com/articles/s41591-022-01811-5
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Machine learning applied to electronic health record data in home
(7 days ago) WebIn recent years, there has been growing evidence that machine learning algorithms can predict risk of deterioration in patients by analyzing electronic health record (EHR) documentation [10]. HHC agencies typically have their own EHR systems which …
https://www.sciencedirect.com/science/article/pii/S1386505622002921
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Integrating Longitudinal Electronic Health Records with Machine
(6 days ago) WebThis study proposes an innovative approach to personalized cancer management by integrating Longitudinal Electronic Health Records (LEHR) with ensemble machine learning algorithms. The method encompasses data preprocessing, feature engineering, …
https://ieeexplore.ieee.org/document/10507968/
Category: Cancer Show Health
Records and Machine Learning Precision Rehabilitation for …
(2 days ago) WebPrecision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning Fengyi Gao, MS , BS , DPT Bayan M. Aldhahwani, PT, MS ,5 isweswaran, MD, PhD ,6 yan Shi, PhD and demographic information recorded …
https://arxiv.org/pdf/2405.05993
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Using machine learning to identify health outcomes from …
(3 days ago) WebPurpose of review: Electronic health records (EHRs) contain valuable data for identifying health outcomes, but these data also present numerous challenges when creating computable phenotyping algorithms. Machine learning methods could help with some of …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6289196/
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Machine Learning and Electronic Health Records: A Paradigm Shift
(7 days ago) WebIn this issue of the Journal, Barack-Corren et al. use machine learning methods to build a highly predictive model of suicidal behavior using longitudinal electronic health records (EHRs).They do so using a well-established probability-based machine …
https://ajp.psychiatryonline.org/doi/10.1176/appi.ajp.2016.16101169
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A machine learning model identifies patients in need of - Nature
(Just Now) WebHere, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584
https://www.nature.com/articles/s41467-023-37996-7
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Predicting polycystic ovary syndrome with machine learning …
(7 days ago) WebWe built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis. METHODS: This is …
https://open.bu.edu/handle/2144/48731
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BDCC Free Full-Text International Classification of Diseases
(7 days ago) WebThe International Classification of Diseases (ICD) serves as a widely employed framework for assigning diagnosis codes to electronic health records of patients. These codes facilitate the encapsulation of diagnoses and procedures …
https://www.mdpi.com/2504-2289/8/5/47
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