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

Man, Jianing; Zielinski, Martin D; Das, Devashish; Sir, Mustafa Y; Wutthisirisart, Phichet; Camazine, Maraya; Pasupathy, Kalyan S

Man, Jianing; Zielinski, Martin D; Das, Devashish; Sir, Mustafa Y; Wutthisirisart, Phichet; Camazine, Maraya; Pasupathy, Kalyan S.

Afiliação

Man J; School of Mechanical Engineering, Institute of Industrial and Intelligent System Engineering, Beijing Institute of Technology, Beijing, China. jmbitie@bit.edu.cn.
Zielinski MD; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA. jmbitie@bit.edu.cn.
Das D; Department of Surgery, Baylor College of Medicine, Houston, TX, USA.
Sir MY; Department of Industrial and Management Systems Engineering, University of South Florida, Tempa, FL, USA.
Wutthisirisart P; Amazon.Com, Inc, Seattle, WA, USA.
Camazine M; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA.
Pasupathy KS; Department of Surgery, Mayo Clinic, Rochester, MN, USA.

J Med Syst ; 46(11): 72, 2022 Sep 26.

Article em En | MEDLINE | ID: mdl-36156743

Assuntos

Hemoglobinas; Oximetria; Hemoglobinas/análise; Hemorragia; Humanos; Monitorização Fisiológica/métodos; Distribuição Normal

Palavras-chave

Functional principal component analysis; Functional regression method; Gaussian process; Missing data; Non-invasive hemoglobin measurement

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Hemoglobinas / Oximetria Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Limite: Humans Idioma: En Revista: J Med Syst Ano de publicação: 2022 Tipo de documento: Article

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