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Non-invasive Hemoglobin Measurement Predictive Analytics with Missing Data and Accuracy Improvement Using Gaussian Process and Functional Regression Model

  • Mobile & Wireless Health
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Abstract

Recent use of noninvasive and continuous hemoglobin (SpHb) concentration monitor has emerged as an alternative to invasive laboratory-based hematological analysis. Unlike delayed laboratory based measures of hemoglobin (HgB), SpHb monitors can provide real-time information about the HgB levels. Real-time SpHb measurements will offer healthcare providers with warnings and early detections of abnormal health status, e.g., hemorrhagic shock, anemia, and thus support therapeutic decision-making, as well as help save lives. However, the finger-worn CO-Oximeter sensors used in SpHb monitors often get detached or have to be removed, which causes missing data in the continuous SpHb measurements. Missing data among SpHb measurements reduce the trust in the accuracy of the device, influence the effectiveness of hemorrhage interventions and future HgB predictions. A model with imputation and prediction method is investigated to deal with missing values and improve prediction accuracy. The Gaussian process and functional regression methods are proposed to impute missing SpHb data and make predictions on laboratory-based HgB measurements. Within the proposed method, multiple choices of sub-models are considered. The proposed method shows a significant improvement in accuracy based on a real-data study. Proposed method shows superior performance with the real data, within the proposed framework, different choices of sub-models are discussed and the usage recommendation is provided accordingly. The modeling framework can be extended to other application scenarios with missing values.

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Acknowledgements

This work was partially supported by the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA, 55905.

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Authors and Affiliations

  1. School of Mechanical Engineering, Institute of Industrial and Intelligent System Engineering, Beijing Institute of Technology, Beijing, China

    Jianing Man

  2. Department of Surgery, Baylor College of Medicine, Houston, TX, USA

    Martin D. Zielinski

  3. Department of Industrial and Management Systems Engineering, University of South Florida, Tempa, FL, USA

    Devashish Das

  4. Amazon.Com, Inc, Seattle, WA, USA

    Mustafa Y. Sir

  5. Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA

    Jianing Man, Phichet Wutthisirisart & Kalyan S. Pasupathy

  6. Department of Surgery, Mayo Clinic, Rochester, MN, USA

    Maraya Camazine

  7. Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, USA

    Kalyan S. Pasupathy

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  1. Jianing Man
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  2. Martin D. Zielinski
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Correspondence to Jianing Man or Kalyan S. Pasupathy.

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Man, J., Zielinski, M.D., Das, D. et al. Non-invasive Hemoglobin Measurement Predictive Analytics with Missing Data and Accuracy Improvement Using Gaussian Process and Functional Regression Model. J Med Syst 46, 72 (2022). https://doi.org/10.1007/s10916-022-01854-8

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