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A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement

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Abstract

Venous thromboembolism (VTE) is a well-recognized complication that is prevalent in patients undergoing major orthopedic surgery (e.g., total hip arthroplasty and total knee arthroplasty). For years, to identify patients at high risk of developing VTE, physicians have relied on traditional risk scoring systems, which are too simplistic to capture the risk level accurately. In this paper, we propose a data-driven machine learning framework to identify such high-risk patients before they undergo a major hip or knee surgery. Using electronic health records of more than 392,000 patients who undergone a major orthopedic surgery, and following a guided feature selection using the genetic algorithm, we trained a fully connected deep neural network model to predict high-risk patients for developing VTE. We identified several risk factors for VTE that were not previously recognized. The best FCDNN model trained using the selected features yielded an area under the ROC curve (AUC) of 0.873, which was remarkably higher than the best AUC obtained by including only risk factors previously known in the medical literature. Our findings suggest several interesting and important insights. The traditional risk scoring tables that are being widely used by physicians to identify high-risk patients are not considering a comprehensive set of risk factors, nor are they as powerful as cutting-edge machine learning methods in distinguishing low- from high-risk patients.

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Notes

  1. It should be noted that the Cerner Corporation had not migrated from ICD-9 to ICD-10 yet by October 2015. Hence, all the records used for this study captured diagnoses, medications, and operations using ICD-9. In addition, while some EHR systems use both ICD and CPT coding systems to record procedures, Cerner used solely ICD-9 for procedures.

  2. The standard thresholds categorize each patient as low, moderate, high, and very high risk. To make a one-to-one match to the binary classification ML models, we merged the first two and the last two categories and labeled them as “low risk” and “high risk,” respectively.

  3. We tested 50, 100, and 150 as the feature set sizes in separate runs of the GA algorithm. No remarkable improvement in the model's performance was made beyond 100 added features, so we decided to use 100 as the feature set size.

  4. We also pursued a similar grid search approach for all other ML algorithms mentioned in Table 5 to ensure that their best possible results are used for model comparison.

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Acknowledgements

This work was conducted with data obtained from the Cerner Health Facts data warehouse of EHRs, provided by Oklahoma State University (OSU), Center for Health Systems Innovation (CHSI). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Cerner Corporation, OSU, or CHSI. The authors would also like to thank Ms. Elvena Fong, health data analytics program manager at CHSI, for her support in extracting and understanding the healthcare data.

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Authors and Affiliations

  1. University of Wisconsin-Milwaukee, Milwaukee, WI, USA

    Elham Rasouli Dezfouli & Huimin Zhao

  2. Oklahoma State University, Stillwater, OK, USA

    Dursun Delen

  3. Istinye University, Istanbul, Turkey

    Dursun Delen

  4. University of Wisconsin-Whitewater, Whitewater, WI, USA

    Behrooz Davazdahemami

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Rasouli Dezfouli, E., Delen, D., Zhao, H. et al. A Machine Learning Framework for Assessing the Risk of Venous Thromboembolism in Patients Undergoing Hip or Knee Replacement. J Healthc Inform Res 6, 423–441 (2022). https://doi.org/10.1007/s41666-022-00121-2

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