Dataset Information

Machine learning for scattering data: strategies, perspectives and applications to surface scattering.

ABSTRACT: Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.

SUBMITTER: Hinderhofer A

PROVIDER: S-EPMC9901926 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Machine learning for scattering data: strategies, perspectives and applications to surface scattering.

Hinderhofer Alexander A Greco Alessandro A Starostin Vladimir V Munteanu Valentin V Pithan Linus L Gerlach Alexander A Schreiber Frank F

Journal of applied crystallography 20230201 Pt 1

Machine learning (ML) has received enormous attention in science and beyond. Discussed here are the status, opportunities, challenges and limitations of ML as applied to X-ray and neutron scattering techniques, with an emphasis on surface scattering. Typical strategies are outlined, as well as possible pitfalls. Applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for ML applications, such as ...[more]

PMID: 36777139

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Project description:BackgroundEbola virus disease (EVD) plagues low-resource and difficult-to-access settings. Machine learning prognostic models and mHealth tools could improve the understanding and use of evidence-based care guidelines in such settings. However, data incompleteness and lack of interoperability limit model generalizability. This study harmonizes diverse datasets from the 2014-16 EVD epidemic and generates several prognostic models incorporated into the novel Ebola Care Guidelines app that provides informed access to recommended evidence-based guidelines.MethodsMultivariate logistic regression was applied to investigate survival outcomes in 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone at various timepoints during 2014-16. We generated a parsimonious model (viral load, age, temperature, bleeding, jaundice, dyspnea, dysphagia, and time-to-presentation) and several fallback models for when these variables are unavailable. All were externally validated against two independent datasets and compared to further models including expert observational wellness assessments. Models were incorporated into an app highlighting the signs/symptoms with the largest contribution to prognosis.FindingsThe parsimonious model approached the predictive power of observational assessments by experienced clinicians (Area-Under-the-Curve, AUC = 0.70-0.79, accuracy = 0.64-0.74) and maintained its performance across subcohorts with different healthcare seeking behaviors. Age and viral load contributed > 5-fold the weighting of other features and including them in a minimal model had a similar AUC, albeit at the cost of specificity.InterpretationClinically guided prognostic models can recapitulate clinical expertise and be useful when such expertise is unavailable. Incorporating these models into mHealth tools may facilitate their interpretation and provide informed access to comprehensive clinical guidelines.FundingHoward Hughes Medical Institute, US National Institutes of Health, Bill & Melinda Gates Foundation, International Medical Corps, UK Department for International Development, and GOAL Global.

Dataset Information

Machine learning for scattering data: strategies, perspectives and applications to surface scattering.

Publications

Machine learning for scattering data: strategies, perspectives and applications to surface scattering.

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