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data extraction from optum ehr  (Optum Inc)

 
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    Optum Inc data extraction from optum ehr
    Dataset creation for the ambulatory electrocardiogram-convolutional neural network model development. Schematic indicates the strategy to obtain robust and reliable dataset for model development. To avoid cross-contamination, no patient data are repeated among training, validation, and testing datasets. *Patient count ( n = 5829) selected based on several inclusion, exclusion criteria to ensure the quality <t>of</t> <t>LVEF</t> data as they are obtained from Optum ® <t>EHR</t> and the dataset was captured in Optum ® EHR via natural language processing of procedure/diagnostic notes and prone to natural language processing errors.
    Data Extraction From Optum Ehr, supplied by Optum Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/data extraction from optum ehr/product/Optum Inc
    Average 90 stars, based on 1 article reviews
    data extraction from optum ehr - by Bioz Stars, 2026-04
    90/100 stars

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    1) Product Images from "Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram"

    Article Title: Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram

    Journal: European Heart Journal. Digital Health

    doi: 10.1093/ehjdh/ztae035

    Dataset creation for the ambulatory electrocardiogram-convolutional neural network model development. Schematic indicates the strategy to obtain robust and reliable dataset for model development. To avoid cross-contamination, no patient data are repeated among training, validation, and testing datasets. *Patient count ( n = 5829) selected based on several inclusion, exclusion criteria to ensure the quality of LVEF data as they are obtained from Optum ® EHR and the dataset was captured in Optum ® EHR via natural language processing of procedure/diagnostic notes and prone to natural language processing errors.
    Figure Legend Snippet: Dataset creation for the ambulatory electrocardiogram-convolutional neural network model development. Schematic indicates the strategy to obtain robust and reliable dataset for model development. To avoid cross-contamination, no patient data are repeated among training, validation, and testing datasets. *Patient count ( n = 5829) selected based on several inclusion, exclusion criteria to ensure the quality of LVEF data as they are obtained from Optum ® EHR and the dataset was captured in Optum ® EHR via natural language processing of procedure/diagnostic notes and prone to natural language processing errors.

    Techniques Used: Biomarker Discovery, Diagnostic Assay



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    data extraction from optum ehr  (Optum Inc)
    90
    Optum Inc data extraction from optum ehr
    Dataset creation for the ambulatory electrocardiogram-convolutional neural network model development. Schematic indicates the strategy to obtain robust and reliable dataset for model development. To avoid cross-contamination, no patient data are repeated among training, validation, and testing datasets. *Patient count ( n = 5829) selected based on several inclusion, exclusion criteria to ensure the quality <t>of</t> <t>LVEF</t> data as they are obtained from Optum ® <t>EHR</t> and the dataset was captured in Optum ® EHR via natural language processing of procedure/diagnostic notes and prone to natural language processing errors.
    Data Extraction From Optum Ehr, supplied by Optum Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/data extraction from optum ehr/product/Optum Inc
    Average 90 stars, based on 1 article reviews
    data extraction from optum ehr - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Dataset creation for the ambulatory electrocardiogram-convolutional neural network model development. Schematic indicates the strategy to obtain robust and reliable dataset for model development. To avoid cross-contamination, no patient data are repeated among training, validation, and testing datasets. *Patient count ( n = 5829) selected based on several inclusion, exclusion criteria to ensure the quality of LVEF data as they are obtained from Optum ® EHR and the dataset was captured in Optum ® EHR via natural language processing of procedure/diagnostic notes and prone to natural language processing errors.

    Journal: European Heart Journal. Digital Health

    Article Title: Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram

    doi: 10.1093/ehjdh/ztae035

    Figure Lengend Snippet: Dataset creation for the ambulatory electrocardiogram-convolutional neural network model development. Schematic indicates the strategy to obtain robust and reliable dataset for model development. To avoid cross-contamination, no patient data are repeated among training, validation, and testing datasets. *Patient count ( n = 5829) selected based on several inclusion, exclusion criteria to ensure the quality of LVEF data as they are obtained from Optum ® EHR and the dataset was captured in Optum ® EHR via natural language processing of procedure/diagnostic notes and prone to natural language processing errors.

    Article Snippet: To ensure robust and reliable LVEF information and minimize errors introduced through automated data extraction from Optum ® EHR , we only included patients with prior HF diagnosis to create the low ejection fraction (EF) data (LVEF ≤ 40%).

    Techniques: Biomarker Discovery, Diagnostic Assay