Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313261005> ?p ?o ?g. }
- W4313261005 endingPage "23" @default.
- W4313261005 startingPage "23" @default.
- W4313261005 abstract "Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data. We used the Medical Information Mart for Intensive Care III database as our source of data and obtained more than 20 ECG records from each included patient during a single hospital admission. We focused on lead II recordings, along with corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine algorithm to predict dysglycemia. The model was able to predict dysglycemia using a single heartbeat with an AUC of 0.92 ± 0.09, a sensitivity of 0.92 ± 0.10, and specificity of 0.84 ± 0.04. After applying 10 s majority voting, the AUC of the model's dysglycemia prediction increased to 0.97 ± 0.06. This study showed that a personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG." @default.
- W4313261005 created "2023-01-06" @default.
- W4313261005 creator A5010639568 @default.
- W4313261005 creator A5015921213 @default.
- W4313261005 creator A5020752696 @default.
- W4313261005 creator A5021306957 @default.
- W4313261005 creator A5051467869 @default.
- W4313261005 creator A5076879023 @default.
- W4313261005 date "2022-12-25" @default.
- W4313261005 modified "2023-10-01" @default.
- W4313261005 title "Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring" @default.
- W4313261005 cites W1744326916 @default.
- W4313261005 cites W1849745725 @default.
- W4313261005 cites W1992731073 @default.
- W4313261005 cites W2000758355 @default.
- W4313261005 cites W2002934220 @default.
- W4313261005 cites W2007188141 @default.
- W4313261005 cites W2007852978 @default.
- W4313261005 cites W2018354995 @default.
- W4313261005 cites W2025416505 @default.
- W4313261005 cites W2032196423 @default.
- W4313261005 cites W2039183257 @default.
- W4313261005 cites W2047092076 @default.
- W4313261005 cites W2068585084 @default.
- W4313261005 cites W2077017015 @default.
- W4313261005 cites W2085526526 @default.
- W4313261005 cites W2092980628 @default.
- W4313261005 cites W2107545755 @default.
- W4313261005 cites W2115282607 @default.
- W4313261005 cites W2125163474 @default.
- W4313261005 cites W2132237453 @default.
- W4313261005 cites W2158009484 @default.
- W4313261005 cites W2396881363 @default.
- W4313261005 cites W2418768274 @default.
- W4313261005 cites W2515852816 @default.
- W4313261005 cites W2532385626 @default.
- W4313261005 cites W2577350032 @default.
- W4313261005 cites W2607704626 @default.
- W4313261005 cites W2902644322 @default.
- W4313261005 cites W2922249064 @default.
- W4313261005 cites W2928542791 @default.
- W4313261005 cites W2954095853 @default.
- W4313261005 cites W2964434942 @default.
- W4313261005 cites W2965520043 @default.
- W4313261005 cites W2988890139 @default.
- W4313261005 cites W2996720771 @default.
- W4313261005 cites W3000630830 @default.
- W4313261005 cites W3120565934 @default.
- W4313261005 cites W3123700022 @default.
- W4313261005 cites W3189486095 @default.
- W4313261005 cites W3201628826 @default.
- W4313261005 cites W4220719668 @default.
- W4313261005 cites W4223585514 @default.
- W4313261005 cites W972738447 @default.
- W4313261005 doi "https://doi.org/10.3390/bios13010023" @default.
- W4313261005 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36671857" @default.
- W4313261005 hasPublicationYear "2022" @default.
- W4313261005 type Work @default.
- W4313261005 citedByCount "1" @default.
- W4313261005 countsByYear W43132610052023 @default.
- W4313261005 crossrefType "journal-article" @default.
- W4313261005 hasAuthorship W4313261005A5010639568 @default.
- W4313261005 hasAuthorship W4313261005A5015921213 @default.
- W4313261005 hasAuthorship W4313261005A5020752696 @default.
- W4313261005 hasAuthorship W4313261005A5021306957 @default.
- W4313261005 hasAuthorship W4313261005A5051467869 @default.
- W4313261005 hasAuthorship W4313261005A5076879023 @default.
- W4313261005 hasBestOaLocation W43132610051 @default.
- W4313261005 hasConcept C119857082 @default.
- W4313261005 hasConcept C126322002 @default.
- W4313261005 hasConcept C13852961 @default.
- W4313261005 hasConcept C154945302 @default.
- W4313261005 hasConcept C38652104 @default.
- W4313261005 hasConcept C41008148 @default.
- W4313261005 hasConcept C71924100 @default.
- W4313261005 hasConceptScore W4313261005C119857082 @default.
- W4313261005 hasConceptScore W4313261005C126322002 @default.
- W4313261005 hasConceptScore W4313261005C13852961 @default.
- W4313261005 hasConceptScore W4313261005C154945302 @default.
- W4313261005 hasConceptScore W4313261005C38652104 @default.
- W4313261005 hasConceptScore W4313261005C41008148 @default.
- W4313261005 hasConceptScore W4313261005C71924100 @default.
- W4313261005 hasFunder F4320322767 @default.
- W4313261005 hasIssue "1" @default.
- W4313261005 hasLocation W43132610051 @default.
- W4313261005 hasLocation W43132610052 @default.
- W4313261005 hasLocation W43132610053 @default.
- W4313261005 hasLocation W43132610054 @default.
- W4313261005 hasOpenAccess W4313261005 @default.
- W4313261005 hasPrimaryLocation W43132610051 @default.
- W4313261005 hasRelatedWork W2748952813 @default.
- W4313261005 hasRelatedWork W2899084033 @default.
- W4313261005 hasRelatedWork W2961085424 @default.
- W4313261005 hasRelatedWork W2973489423 @default.
- W4313261005 hasRelatedWork W3046775127 @default.
- W4313261005 hasRelatedWork W4285260836 @default.
- W4313261005 hasRelatedWork W4286629047 @default.
- W4313261005 hasRelatedWork W4306321456 @default.