Non-invasive Hemoglobin Measurement Predictive Analytics with Missing Data and Accuracy Improvement Using Gaussian Process and Functional Regression Model.
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...
| Publicado en: | Journal of Medical Systems Vol. 46; no. 11; pp. 1 - 11 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Nov2022
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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=159925822&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159925822 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2022 vid: 46 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159925822 159925822 159925822 10.1007/s10916-022-01854-8 159925822 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Non-invasive Hemoglobin Measurement Predictive Analytics with Missing Data and Accuracy Improvement Using Gaussian Process and Functional Regression Model. aug: au: Man, Jianing Zielinski, Martin D. Das, Devashish Sir, Mustafa Y. Wutthisirisart, Phichet Camazine, Maraya Pasupathy, Kalyan S. affil: School of Mechanical Engineering, Institute of Industrial and Intelligent System Engineering, Beijing Institute of Technology, Beijing, China sug: subj: Hemoglobins Analysis Monitoring, Physiologic Methods Monitoring, Physiologic Equipment and Supplies Prediction Models Models, Statistical Validity Human Minnesota Female Male Adult Middle Age Aged Retrospective Design Oximetry Equipment and Supplies Hemoglobinometry Hemorrhage Factor Analysis Regression Correlation Coefficient Descriptive Statistics Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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