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Outcome Prediction in Clinical Treatment Processes

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

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.

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Acknowledgement

This work was supported by the National Nature Science Foundation of China under Grant No 81101126 and 61562088, and the Fundamental Research Funds for the Central Universities under Grant No 2014QNA5014. The author would like to give special thanks to all experts who cooperate in the evaluation of the proposed method.

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Authors and Affiliations

  1. College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhejiang, China

    Zhengxing Huang & Huilong Duan

  2. Cardiology department of Chinese, PLA General Hospital, Beijing, China

    Wei Dong

  3. IT department of Chinese, PLA General Hospital, Beijing, China

    Lei Ji

Authors
  1. Zhengxing Huang
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  2. Wei Dong
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  3. Lei Ji
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  4. Huilong Duan
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Corresponding author

Correspondence to Zhengxing Huang.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Huang, Z., Dong, W., Ji, L. et al. Outcome Prediction in Clinical Treatment Processes. J Med Syst 40, 8 (2016). https://doi.org/10.1007/s10916-015-0380-6

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  • DOI: https://doi.org/10.1007/s10916-015-0380-6

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