Rough set theory based prognostic classification models for hospice referral.
Background: This paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respec...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 15; pp. 1 - 19 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
BioMed Central
11/25/2015
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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=ccm&AN=111298684&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111298684 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 11/25/2015 vid: 15 pid: 24147 pub: BioMed Central artinfo: ui: 111298684 111298684 NLM26606986 111298684 10.1186/s12911-015-0216-9 NLM26606986 PMC4659220 111298684 ppf: 1 ppct: 18 formats: tig: atl: Rough set theory based prognostic classification models for hospice referral. aug: au: Gil-Herrera, Eleazar Aden-Buie, Garrick Yalcin, Ali Tsalatsanis, Athanasios Barnes, Laura E. Djulbegovic, Benjamin affil: Threshold Tuning Optimization Team Citigroup, 3800 Citibank Center, Tampa, 33610 FL, USA sug: subj: Prognosis Referral and Consultation Statistics and Numerical Data Models, Theoretical Terminally Ill Patients Statistics and Numerical Data Hospices Statistics and Numerical Data Female Male Middle Age Aged Classification Human Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Background: This paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respect to two factors related to clinical credibility: accuracy and accessibility. Accessibility refers to the ability of the model to provide traceable, interpretable results and use data that is relevant and simple to collect.Methods: We utilize retrospective data from 9,103 terminally ill patients to demonstrate the design and implementation RST- based models to identify potential hospice candidates. The classical rough set approach (CRSA) provides methods for knowledge acquisition, founded on the relational indiscernibility of objects in a decision table, to describe required conditions for membership in a concept class. On the other hand, the dominance-based rough set approach (DRSA) analyzes information based on the monotonic relationships between condition attributes values and their assignment to the decision class. CRSA decision rules for six-month patient survival classification were induced using the MODLEM algorithm. Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm.Results: The RST-based classifiers are compared with other predictive and rule based decision modeling techniques, namely logistic regression, support vector machines, random forests and C4.5. The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5.Conclusions: This paper contributes to the growing body of research in RST-based prognostic models. RST and its extensions posses features that enhance the accessibility of clinical decision support models. While the non-rule-based methods-logistic regression, support vector machines and random forests-were found to achieve higher AUC, the performance differential may be outweighed by the benefits of the rule-based methods, particularly in the case of VC-DomLEM. Developing prognostic models for hospice referrals is a challenging problem resulting in substandard performance for all of the evaluated classification methods. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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