Machine Learning In Population Health
Listing Websites about Machine Learning In Population Health
Machine learning and algorithmic fairness in public and …
(Just Now) WEBAlgorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public
https://www.nature.com/articles/s42256-021-00373-4
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Machine learning in population health: Opportunities and threats
(8 days ago) WEBMachine learning (ML) has succeeded in complex tasks by trading experts and programmers for data and nonparametric statistical models. However, the …
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002702
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Predicting population health with machine learning: a scoping …
(1 days ago) WEBConclusions: Machine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, …
https://pubmed.ncbi.nlm.nih.gov/33109649/
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A population health perspective on artificial intelligence
(3 days ago) WEBThe sub-field of machine learning in particular is now being applied to many clinical problems, but machine learning has been applied less frequently to …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7323781/
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Machine learning in population health: Opportunities and threats
(1 days ago) WEBMachine learning in population health: Opportunities and threats PLoS Med. 2018 Nov 27;15(11):e1002702. doi: 10.1371/journal.pmed.1002702. eCollection …
https://pubmed.ncbi.nlm.nih.gov/30481173/
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Machine learning and algorithmic fairness in public and …
(2 days ago) WEBMachine learning in population and public health Building on the focus of public and population health, here we out-line a taxonomy to organize and illustrate machine …
https://par.nsf.gov/servlets/purl/10310717
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[2008.07278] Machine Learning in Population and Public Health
(2 days ago) WEBMachine Learning in Population and Public Health. Vishwali Mhasawade, Yuan Zhao, Rumi Chunara. Research in population and public health focuses on the …
https://arxiv.org/abs/2008.07278
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Machine learning and algorithmic fairness in public and …
(9 days ago) WEBabstract = "Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of …
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Using machine learning for healthcare challenges and …
(7 days ago) WEBUse of machine learning in Public Health. Population-level healthcare outcomes can be predicted by ML algorithms using large data sets [42]. ML algorithms …
https://www.sciencedirect.com/science/article/pii/S2352914822000739
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Machine Learning in Epidemiology and Health Outcomes Research
(7 days ago) WEBMachine learning approaches to modeling of epidemiologic data are becoming increasingly more prevalent in the literature. These methods have the potential to improve our …
https://www.annualreviews.org/content/journals/10.1146/annurev-publhealth-040119-094437
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Machine learning in population health: Opportunities and …
(6 days ago) WEBMachine learning (ML) has succeeded in complex tasks by trading experts and programmers for data and nonparametric statistical models. However, the applications …
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Big data, machine learning, and population health: predicting …
(1 days ago) WEBThe application of machine learning (ML) to address population health challenges has received much less attention than its application in the clinical setting. One such …
https://pubmed.ncbi.nlm.nih.gov/35681091/
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Machine learning and algorithmic fairness in public and …
(5 days ago) WEBThis Perspective aims to highlight potential opportunities and challenges for machine learning within a holistic view of health, and builds on research in population and …
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Prediction of disease comorbidity using explainable artificial
(7 days ago) WEB1.Introduction. Disease comorbidity occurs when an individual experiences two or more illnesses simultaneously, which can include physical and/or mental medical …
https://www.sciencedirect.com/science/article/pii/S1386505623001065
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Machine learning in population health: Opportunities and threats
(3 days ago) WEBMachine learning (ML) has succeeded in complex tasks by trading experts and programmers for data and nonparametric statistical models. However, the …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258474/
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Machine Learning in Population and Public Health DeepAI
(9 days ago) WEBMachine Learning in Population and Public Health. 07/21/2020. ∙. by Vishwali Mhasawade, et al. ∙. ∙. share. Research in population and public health …
https://deepai.org/publication/machine-learning-in-population-and-public-health
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Twenty-four-hour physical activity patterns - BMC Public Health
(7 days ago) WEBDepression is a mood disorder that causes persistent sadness and loss of interest in enjoyable activities [1,2,3,4].Depression is a prevalent mental health …
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18759-5
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Top 3 Data Analytics Trends in Healthcare 2024 - unislink.com
(6 days ago) WEBIntegration of artificial intelligence (AI) and machine learning Real-time data driving innovation and transforming patient care Utilization of big data for predictive …
https://unislink.com/rcm-best-practices-blog/top-3-data-analytics-trends-in-healthcare-for-2024/
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A Comprehensive Review on Machine Learning in Healthcare …
(4 days ago) WEBPopulation health management: Machine learning can be used to analyze data from large populations to identify trends and patterns that can inform the …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10180678/
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Machine Learning for Prediction of Childhood Mental Health …
(8 days ago) WEBWelsh population (15). The Adolescent Mental Health Data Platform (ADP) contains data relating to Chung J, Teo J. Mental Health Prediction Using Machine …
https://www.medrxiv.org/content/10.1101/2024.05.03.24306756.full.pdf
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Identification of Suicide Attempt Risk Factors in a National US …
(7 days ago) WEBThis study evaluates future suicide attempt risk factors in the general population using a data-driven machine learning approach that includes more than [Skip to Navigation]
https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2774348
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Interpretable machine learning framework to predict gout …
(4 days ago) WEBGout prediction is essential for the development of individualized prevention and treatment plans. Our objective was to develop an efficient and interpretable machine …
https://nutritionandmetabolism.biomedcentral.com/articles/10.1186/s12986-024-00802-2
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Association between breastfeeding duration and diabetes mellitus …
(5 days ago) WEBIn addition, machine-learning prediction models for DM and hemoglobin A1c (HbA1c) were developed to further evaluate this association. We used the Korean …
https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-024-00642-z
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Predicting population health with machine learning: a scoping …
(5 days ago) WEBObjective To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been …
https://bmjopen.bmj.com/content/10/10/e037860
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Scientific machine learning for predicting plasma concentrations in
(1 days ago) WEBA variety of classical machine learning approaches have been developed over the past ten years with the aim to individualize drug dosages based on measured …
https://www.medrxiv.org/content/10.1101/2024.05.06.24306555v1
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Big data, machine learning, and population health: predicting …
(4 days ago) WEBThe Author(s) 2022. The application of machine learning (ML) to address population health challenges has received much less attention than its application in the clinical …
https://www.nature.com/articles/s41390-022-02137-1.pdf
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Machine learning and algorithmic fairness in public and …
(4 days ago) WEBIn the following subsections, current gaps in machine learning and algorithmic fairness work with respect to health disparities are made explicit, and recommendations are …
https://www.nature.com/articles/s42256-021-00373-4.pdf
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Frontiers Interpretability-based machine learning for predicting …
(2 days ago) WEBObjective: The objective of this research was to create a machine learning predictive model that could be easily interpreted in order to precisely determine the risk …
https://www.frontiersin.org/articles/10.3389/fmed.2024.1399527
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Machine Learning for Maternal Health: OU Engineer Receives NSF …
(8 days ago) WEBTalayeh Razzaghi, Ph.D., an assistant professor in the Gallogly College of Engineering at the University of Oklahoma, has won a National Science Foundation …
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Big data, machine learning, and population health: predicting …
(3 days ago) WEBImpact. To date, the application of machine learning to address population health challenges in paediatrics lags behind other clinical applications. This review …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7614199/
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Agronomy Free Full-Text Analysis of the Effects of Population
(Just Now) WEBIn this study, we combined population structure, plant shape characteristics, environmental weather conditions, and management information data using a machine …
https://www.mdpi.com/2073-4395/14/5/1028
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Research on a machine learning-based adaptive and efficient …
(2 days ago) WEBCommunity correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological …
https://ui.adsabs.harvard.edu/abs/2024NatSR..14.9890X/abstract
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Agriculture Free Full-Text Prediction of Live Bulb Weight - MDPI
(9 days ago) WEB(1) Background: This challenge is exacerbated by the aging of the rural population, leading to a scarcity of available manpower. To address this issue, the …
https://www.mdpi.com/2077-0472/14/5/754
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Machine learning applications in stroke medicine: advancements
(1 days ago) WEBMachine learning promises to revolutionize stroke medicine by enabling precise diagnosis, tailored treatment selection, and improved prognostication. Continued research and …
https://pubmed.ncbi.nlm.nih.gov/37843210/
Category: Medicine Show Health
An AI revolution is brewing in medicine. What will it look like?
(Just Now) WEBShe and her colleagues identified thousands of medical-imaging studies that compared the diagnostic performance of deep-learning models with that of health-care …
https://www.nature.com/articles/d41586-023-03302-0
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