Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement.
A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts' reasoning abilities remain critical in identifying potential improvements and ensuring safety. We propose a thick data analytics (T...
| Publicado en: | American Statistician Vol. 78; no. 4; pp. 456 - 465 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Nov2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=180359694&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 180359694 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: Y pubinfo: dt: Nov2024 vid: 78 iid: 4 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 180359694 10.1080/00031305.2024.2327535 ppf: 456 ppct: 9 formats: tig: atl: Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement. aug: au: Nguyen, Minh Eulalio, Tiffany Marafino, Ben J. Rose, Christian Chen, Jonathan H. Baiocchi, Michael affil: Department of Biomedical Data Science, Stanford University, Stanford, CA Department of Emergency Medicine, Stanford University, Stanford, CA Center for Biomedical Informatics Research, Stanford University, Stanford, CA Department of Epidemiology and Population Health, Stanford University, Stanford, CA su: Decision making Information resources Data analytics Sampling (Process) Prediction models sug: subj: Decision making Information resources Data analytics Sampling (Process) Prediction models keyword: Algorithmic audit Machine learning Mixed methods Thick description Algorithmic audit Machine learning Mixed methods Thick description ab: A gap remains between developing risk prediction models and deploying models to support real-world decision making, especially in high-stakes situations. Human-experts' reasoning abilities remain critical in identifying potential improvements and ensuring safety. We propose a thick data analytics (TDA) framework for eliciting and combining expert-human insight into the evaluation of models. The insight is 3-fold: (a) statistical methods are limited to using joint distributions of observable quantities for predictions but often there is more information available in a real-world than what is usable for algorithms, (b) domain experts can access more information (e.g., patient files) than an algorithm and bring additional knowledge into their assessments through leveraging insights and experiences, and (c) experts can re-frame and re-evaluate prediction problems to suit real-world situations. Here, we revisit an example of predicting temporal risk for intensive care admission within 24 hr of hospitalization. We propose a sampling procedure for identifying informative cases for deeper inspection. Expert feedback is used to understand sources of information to improve model development and deployment. We recommend model assessment based on objective evaluation metrics derived from subjective evaluations of the problem formulation. TDA insights facilitate iterative model development toward safer, actionable, and acceptable risk predictions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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