Population Health Machine Learning
Listing Websites about Population Health Machine Learning
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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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, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve …
https://pubmed.ncbi.nlm.nih.gov/33109649/
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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 applications for which ML has been successfully deployed in health and biomedicine remain limited [ 1 ]. These limits also apply in population health, in which we are concerned with the health
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002702
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Open access Original research Predicting population health …
(2 days ago) WEBmachine learning approaches for health applications. 15 16 Population health applications of prediction models are relatively new compared with clinical applications; correspondingly, the role of machine learning in these applications has been far less studied and discussed in the health literature. The goals of our review are to determine
https://bmjopen.bmj.com/content/bmjopen/10/10/e037860.full.pdf
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Human-in-the-loop machine learning with applications for …
(Just Now) WEBTherefore, as shown in Fig. 4, the basic vision of this work is to develop Human-in-the-loop Compressive Population Health (HCPH), a machine learning-based healthcare framework that reduces the effort for human expert (oracles) to perform traditional prevalence profiling while maintaining data reliability. Each disease will select certain
https://link.springer.com/article/10.1007/s42486-022-00115-4
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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 2018 Nov. Authors Abraham D Flaxman 1 , Theo Vos 1 Affiliation 1 Health Metrics Sciences, University of Washington, Seattle
https://pubmed.ncbi.nlm.nih.gov/30481173/
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Is Demography Destiny? Application of Machine …
(3 days ago) WEBA major challenge in monitoring population health is the regularity, timing and granularity of data available. If they are able to achieve sufficient precision, modelled estimates can play an important …
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0125602
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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 challenge is addressing disparities in early childhood cognitive development-a complex public health issue rooted in the social determinants of health, exacerbated by inequity
https://pubmed.ncbi.nlm.nih.gov/35681091/
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[PDF] Predicting population health with machine learning: a …
(3 days ago) WEBMachine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data, and important aspects of model development were under-reported. Objective To determine how machine learning has been applied to prediction applications in …
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Deep learning for prediction of population health costs - PMC
(3 days ago) WEBWe compared the deep learning model to three baseline models. (1) The average cost per year in the previous 6 years as prediction for the cost in the evaluation period. (2) The costs in the last year of the observation time as prediction for the cost in the evaluation period. (3) A two-stage approach where first, a multivariate ridge regression
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812208/
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Population modeling with machine learning can enhance …
(6 days ago) WEBPopulation modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations. In population studies of mental health, individual traits are captured via lengthy …
https://academic.oup.com/gigascience/article/10/10/giab071/6396189
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Population health management could see wins from AI, machine …
(1 days ago) WEBThe role of artificial intelligence in addressing population health management is explored. AI and machine learning can play a key role in population health in the areas of disease risk and
https://www.modernhealthcare.com/indepth/how-ai-plays-role-in-population-health-management/
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Strategic Analysis of Population Health Management using …
(6 days ago) WEBThe purpose of this research is to create a disease prediction system that can make precise inferences from user input using machine learning algorithms. The interface of the system will be carefully crafted according to the user requirements and workflow. Decision Tree, Random Forest, Naive Bayes, and K-Nearest Neighbors are among the efficient …
https://ieeexplore.ieee.org/document/10193171/
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Machine Learning and Feature Selection for Population Health
(2 days ago) WEBenough to build machine learning models. AI insights are needed to build the simplest machine learning models possible, and the models need to be understood and trusted by clinicians if we want them to use the output from the models. Terri Steinberg, MD, MBA Chief Health Information Officer and Vice President for Population Health Informatics
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A population health perspective on artificial intelligence
(1 days ago) WEBPopulation Health*. Public Health. The burgeoning field of Artificial Intelligence (AI) has the potential to profoundly impact the public's health. Yet, to make the most of this opportunity, decision-makers must understand AI concepts. In this article, we describe approaches and fields within AI and illustrate through examples how th ….
https://pubmed.ncbi.nlm.nih.gov/31106580/
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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 conditions [1].The prevalence of comorbidity is expected to increase significantly in the coming years, with 17% of the UK population projected to have four or more chronic …
https://www.sciencedirect.com/science/article/pii/S1386505623001065
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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 studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines. Design A scoping review.
https://bmjopen.bmj.com/content/10/10/e037860
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Twenty-four-hour physical activity patterns - BMC Public Health
(7 days ago) WEBWiemken TL, Kelley RR. Machine Learning in Epidemiology and Health Outcomes Research. Annu Rev Public Health. 2020;41:21–36. Article PubMed Google Scholar Rose S. Intersections of machine learning and epidemiological methods for health services research. Int J Epidemiol. 2021;49:1763–70.
https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-024-18759-5
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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 setting. One such
https://www.nature.com/articles/s41390-022-02137-1.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] Schaffer A, Sinyor M, Kurdyak P, et al. Population-based analysis of health care contacts among suicide decedents: identifying opportunities for more targeted
https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2774348
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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 applications for which ML has been successfully deployed in health and biomedicine remain limited [ 1 ]. These limits also apply in population health, in which we are concerned with the health
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6258474/
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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 learning (ML) model using the SHapley Additive exPlanation (SHAP) to link dietary fiber and triglyceride-glucose (TyG) index to predict gout. Using datasets from the National …
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 National Health and Nutrition Examination Surveys database, a nationwide and population-based health survey from 2010 to 2020.
https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-024-00642-z
Category: Nutrition Show Health
Is demography destiny? Application of machine learning
(1 days ago) WEBApplication of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset PLoS One. 2015 May 4;10(5): e0125602. from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two …
https://pubmed.ncbi.nlm.nih.gov/25938675/
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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 plasma concentrations. However, the interpretability of these models is challenging as they do not incorporate information on pharmacokinetic (PK) drug disposition. In this work we …
https://www.medrxiv.org/content/10.1101/2024.05.06.24306555v1
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Predicting population health with machine learning: a scoping …
(2 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 studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines.
https://europepmc.org/article/MED/33109649
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