Use of patterns of service utilization and hierarchical survival analysis in planning and providing care for overdose patients and predicting the time-to-second overdose

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dc.contributor.authorBambi, Jonas
dc.contributor.authorOlobatuyi, Kehinde
dc.contributor.authorSantoso, Yudi
dc.contributor.authorSadri, Hanieh
dc.contributor.authorMoselle, Ken
dc.contributor.authorRudnick, Abraham
dc.contributor.authorDong, Gracia Yunruo
dc.contributor.authorChang, Ernie
dc.contributor.authorKuo, Alex
dc.date.accessioned2024-10-10T17:23:08Z
dc.date.available2024-10-10T17:23:08Z
dc.date.issued2024
dc.description.abstractIndividuals from a variety of backgrounds are affected by the opioid crisis. To provide optimal care for individuals at risk of opioid overdose and prevent subsequent overdoses, a more targeted response that goes beyond the traditional taxonomical diagnosis approach to care management needs to be adopted. In previous works, Graph Machine Learning and Natural Language Processing methods were used to model the products for planning and evaluating the treatment of patients with complex issues. This study proposes a methodology of partitioning patients in the opioid overdose cohort into various communities based on their patterns of service utilization (PSUs) across the continuum of care using graph community detection and applying survival analysis to predict time-to-second overdose for each of the communities. The results demonstrated that the overdose cohort is not homogeneous with respect to the determinants of risk. Moreover, the risk for subsequent overdose was quantified: there is a 51% higher chance of experiencing a second overdose for a high-risk community compared to a low-risk community. The proposed method can inform a more efficient treatment heterogeneity approach for a cohort made of diverse individuals, such as the opioid overdose cohort. It can also guide targeted support for patients at risk of subsequent overdoses.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.identifier.citationBambi, J., Olobatuyi, K., Santoso, Y., Sadri, H., Moselle, K., Rudnick, A., Dong, G. Y., Chang, E., & Kuo, A. (2024). Use of patterns of service utilization and hierarchical survival analysis in planning and providing care for overdose patients and predicting the time-to-second overdose. Knowledge, 4(3), Article 3. https://doi.org/10.3390/knowledge4030024
dc.identifier.urihttps://doi.org/10.3390/knowledge4030024
dc.identifier.urihttps://hdl.handle.net/1828/20513
dc.language.isoen
dc.publisherKnowledge
dc.rightsAttribution CC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectclinical pathways
dc.subjectclustering algorithms
dc.subjectdecision support
dc.subjectgraph community detection
dc.subjecthealth information management
dc.subjecthealth service system
dc.subjectmachine learning algorithms
dc.subjectopioid crisis
dc.subjectopioid overdose
dc.subjectsurvival analysis
dc.subject.departmentSchool of Health Information Science
dc.subject.departmentDepartment of Mathematics and Statistics
dc.subject.departmentDepartment of Computer Science
dc.subject.departmentDepartment of Psychology
dc.titleUse of patterns of service utilization and hierarchical survival analysis in planning and providing care for overdose patients and predicting the time-to-second overdose
dc.typeArticle

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