Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois.
Objectives.To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. Methods. We used administrative data for 6457 women collected by the Illinois Department of H...
| Publicado en: | American Journal of Public Health Vol. 107; no. 6; pp. 938 - 945 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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American Public Health Association
Jun2017
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| 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=123016470&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 123016470 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00900036 APH jtl: American Journal of Public Health issn: 00900036 maglogo: N pubinfo: dt: Jun2017 vid: 107 iid: 6 pid: 44 pub: American Public Health Association artinfo: ui: 123016470 10.2105/AJPH.2017.303711 ppf: 938 ppct: 7 formats: tig: atl: Machine Learning for Social Services: A Study of Prenatal Case Management in Illinois. aug: au: Pan, Ian Nolan, Laura B. Brown, Rashida R. Khan, Romana van der Boor, Paul Harris, Daniel G. Ghani, Rayid affil: Department of Biostatistics, School of Public Health, Brown University, Providence, RI Population Research Center, School of Social Work, Columbia University, New York, NY Division of Epidemiology, School of Public Health, University of California, Berkeley Kellogg School of Management, Northwestern University, Evanston, IL Center for Data Science and Public Policy, University of Chicago, Chicago, IL Department of Human Services, Illinois State Government, Chicago su: United States Illinois Prenatal care Social services Artificial intelligence Forecasting Health care rationing Social impact assessment Pregnancy Machine learning Medical case management Labor complications (Obstetrics) Medical forecasting Algorithm research Illinois. Dept. of Human Services Diagnosis Risk assessment Predictive tests High-risk pregnancy Algorithms Descriptive statistics Computer-aided diagnosis sug: subj: Prenatal care Social services Artificial intelligence Forecasting Health care rationing Social impact assessment Pregnancy United States Illinois Other Individual and Family Services All Other Miscellaneous Ambulatory Health Care Services All other ambulatory health care services Machine learning Medical case management Labor complications (Obstetrics) Medical forecasting Algorithm research Illinois. Dept. of Human Services Diagnosis Risk assessment Predictive tests High-risk pregnancy Algorithms Descriptive statistics Computer-aided diagnosis ab: Objectives.To evaluate the positive predictive value of machine learning algorithms for early assessment of adverse birth risk among pregnant women as a means of improving the allocation of social services. Methods. We used administrative data for 6457 women collected by the Illinois Department of Human Services from July 2014 to May 2015 to develop a machine learning model for adverse birth prediction and improve upon the existing paper-based risk assessment. We compared different models and determined the strongest predictors of adverse birth outcomes using positive predictive value as the metric for selection. Results. Machine learning algorithms performed similarly, outperforming the current paper-based risk assessment by up to 36%; a refined paper-based assessment outperformed the current assessment by up to 22%. We estimate that these improvements will allow 100 to 170 additional high-risk pregnant women screened for program eligibility each year to receive services that would have otherwise been unobtainable. Conclusions. Our analysis exhibits the potential for machine learning to move government agencies toward a more data-informed approach to evaluating risk and providing social services. Overall, such efforts will improve the efficiency of allocating resource-intensive interventions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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