Outcome Prediction in Clinical Treatment Processes.
Clinical outcome prediction, as strong implications for health service delivery of clinical treatment processes (CTPs), is important for both patients and healthcare providers. Prior studies typically use a priori knowledge, such as demographics or patient physical factors, to estimate clinical outc...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2016
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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=115925234&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925234 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2016 vid: 40 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925234 115925234 115925234 10.1007/s10916-015-0380-6 115925234 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Outcome Prediction in Clinical Treatment Processes. aug: au: Huang, Zhengxing Dong, Wei Ji, Lei Duan, Huilong affil: College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhejiang China sug: subj: Outcomes (Health Care) Angina, Unstable Therapy Treatment Outcomes Probability Electronic Health Records Therapeutics Trends Length of Stay Readmission Patient Discharge China Descriptive Statistics Validity Benchmarking Algorithms Quality of Health Care Physicians Human Aged Middle Age Male Female Comorbidity Funding Source Aged: 65+ years Middle Aged: 45-64 years Male Female ab: Clinical outcome prediction, as strong implications for health service delivery of clinical treatment processes (CTPs), is important for both patients and healthcare providers. Prior studies typically use a priori knowledge, such as demographics or patient physical factors, to estimate clinical outcomes at early stages of CTPs (e.g., admission). They lack the ability to deal with temporal evolution of CTPs. In addition, most of the existing studies employ data mining or machine learning methods to generate a prediction model for a specific type of clinical outcome, however, a mathematical model that predicts multiple clinical outcomes simultaneously, has not yet been established. In this study, a hybrid approach is proposed to provide a continuous predictive monitoring service on multiple clinical outcomes. More specifically, a probabilistic topic model is applied to discover underlying treatment patterns of CTPs from electronic medical records. Then, the learned treatment patterns, as low-dimensional features of CTPs, are exploited for clinical outcome prediction across various stages of CTPs based on multi-label classification. The proposal is evaluated to predict three typical classes of clinical outcomes, i.e., length of stay, readmission time, and the type of discharge, using 3492 pieces of patients' medical records of the unstable angina CTP, extracted from a Chinese hospital. The stable model was characterized by 84.9% accuracy and 6.4% hamming-loss with 3 latent treatment patterns discovered from data, which outperforms the benchmark multi-label classification algorithms for clinical outcome prediction. Our study indicates the proposed approach can potentially improve the quality of clinical outcome prediction, and assist physicians to understand the patient conditions, treatment inventions, and clinical outcomes in an integrated view. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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