FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3215809611> ?p ?o ?g. }

• W3215809611 endingPage "e34201" @default.
• W3215809611 startingPage "e34201" @default.
• W3215809611 abstract "Background There is a growing demand globally for emergency department (ED) services. An increase in ED visits has resulted in overcrowding and longer waiting times. The triage process plays a crucial role in assessing and stratifying patients’ risks and ensuring that the critically ill promptly receive appropriate priority and emergency treatment. A substantial amount of research has been conducted on the use of machine learning tools to construct triage and risk prediction models; however, the black box nature of these models has limited their clinical application and interpretation. Objective In this study, we plan to develop an innovative, dynamic, and interpretable System for Emergency Risk Triage (SERT) for risk stratification in the ED by leveraging large-scale electronic health records (EHRs) and machine learning. Methods To achieve this objective, we will conduct a retrospective, single-center study based on a large, longitudinal data set obtained from the EHRs of the largest tertiary hospital in Singapore. Study outcomes include adverse events experienced by patients, such as the need for an intensive care unit and inpatient death. With preidentified candidate variables drawn from expert opinions and relevant literature, we will apply an interpretable machine learning–based AutoScore to develop 3 SERT scores. These 3 scores can be used at different times in the ED, that is, on arrival, during ED stay, and at admission. Furthermore, we will compare our novel SERT scores with established clinical scores and previously described black box machine learning models as baselines. Receiver operating characteristic analysis will be conducted on the testing cohorts for performance evaluation. Results The study is currently being conducted. The extracted data indicate approximately 1.8 million ED visits by over 810,000 unique patients. Modelling results are expected to be published in 2022. Conclusions The SERT scoring system proposed in this study will be unique and innovative because of its dynamic nature and modelling transparency. If successfully validated, our proposed solution will establish a standard for data processing and modelling by taking advantage of large-scale EHRs and interpretable machine learning tools. International Registered Report Identifier (IRRID) DERR1-10.2196/34201" @default.
• W3215809611 created "2021-12-06" @default.
• W3215809611 creator A5003848511 @default.
• W3215809611 creator A5015716999 @default.
• W3215809611 creator A5024459585 @default.
• W3215809611 creator A5030967805 @default.
• W3215809611 creator A5036008511 @default.
• W3215809611 creator A5049356493 @default.
• W3215809611 creator A5070074839 @default.
• W3215809611 creator A5072669248 @default.
• W3215809611 date "2022-03-25" @default.
• W3215809611 modified "2023-10-18" @default.
• W3215809611 title "Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation" @default.
• W3215809611 cites W1553511670 @default.
• W3215809611 cites W1709704784 @default.
• W3215809611 cites W1887152358 @default.
• W3215809611 cites W1975630033 @default.
• W3215809611 cites W1992803797 @default.
• W3215809611 cites W2023015132 @default.
• W3215809611 cites W2024784120 @default.
• W3215809611 cites W2035043769 @default.
• W3215809611 cites W2045553720 @default.
• W3215809611 cites W204697558 @default.
• W3215809611 cites W2055905393 @default.
• W3215809611 cites W2058747453 @default.
• W3215809611 cites W2068227175 @default.
• W3215809611 cites W2071435448 @default.
• W3215809611 cites W2091085232 @default.
• W3215809611 cites W2096367109 @default.
• W3215809611 cites W2098745922 @default.
• W3215809611 cites W2110342300 @default.
• W3215809611 cites W2120308908 @default.
• W3215809611 cites W2135046866 @default.
• W3215809611 cites W2147808390 @default.
• W3215809611 cites W2153531573 @default.
• W3215809611 cites W2164878629 @default.
• W3215809611 cites W2169167455 @default.
• W3215809611 cites W2280491322 @default.
• W3215809611 cites W2345073978 @default.
• W3215809611 cites W2743731382 @default.
• W3215809611 cites W2752349109 @default.
• W3215809611 cites W2771761382 @default.
• W3215809611 cites W2796424707 @default.
• W3215809611 cites W2799930623 @default.
• W3215809611 cites W2805089815 @default.
• W3215809611 cites W2807518139 @default.
• W3215809611 cites W2885195348 @default.
• W3215809611 cites W2898892062 @default.
• W3215809611 cites W2911964244 @default.
• W3215809611 cites W2919115771 @default.
• W3215809611 cites W2929110666 @default.
• W3215809611 cites W2934238135 @default.
• W3215809611 cites W2945806301 @default.
• W3215809611 cites W2945976633 @default.
• W3215809611 cites W2975676887 @default.
• W3215809611 cites W2984942011 @default.
• W3215809611 cites W3006913750 @default.
• W3215809611 cites W3024621094 @default.
• W3215809611 cites W3039720325 @default.
• W3215809611 cites W3045837041 @default.
• W3215809611 cites W3046231755 @default.
• W3215809611 cites W3098949126 @default.
• W3215809611 cites W3129498525 @default.
• W3215809611 cites W3160401648 @default.
• W3215809611 cites W3194068789 @default.
• W3215809611 cites W3201312739 @default.
• W3215809611 doi "https://doi.org/10.2196/34201" @default.
• W3215809611 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35333179" @default.
• W3215809611 hasPublicationYear "2022" @default.
• W3215809611 type Work @default.
• W3215809611 sameAs 3215809611 @default.
• W3215809611 citedByCount "6" @default.
• W3215809611 countsByYear W32158096112022 @default.
• W3215809611 countsByYear W32158096112023 @default.
• W3215809611 crossrefType "journal-article" @default.
• W3215809611 hasAuthorship W3215809611A5003848511 @default.
• W3215809611 hasAuthorship W3215809611A5015716999 @default.
• W3215809611 hasAuthorship W3215809611A5024459585 @default.
• W3215809611 hasAuthorship W3215809611A5030967805 @default.
• W3215809611 hasAuthorship W3215809611A5036008511 @default.
• W3215809611 hasAuthorship W3215809611A5049356493 @default.
• W3215809611 hasAuthorship W3215809611A5070074839 @default.
• W3215809611 hasAuthorship W3215809611A5072669248 @default.
• W3215809611 hasBestOaLocation W32158096111 @default.
• W3215809611 hasConcept C107327155 @default.
• W3215809611 hasConcept C119857082 @default.
• W3215809611 hasConcept C142724271 @default.
• W3215809611 hasConcept C154945302 @default.
• W3215809611 hasConcept C159110408 @default.
• W3215809611 hasConcept C162324750 @default.
• W3215809611 hasConcept C204787440 @default.
• W3215809611 hasConcept C2777120189 @default.
• W3215809611 hasConcept C2778872837 @default.
• W3215809611 hasConcept C2780385302 @default.
• W3215809611 hasConcept C2780724011 @default.
• W3215809611 hasConcept C41008148 @default.
• W3215809611 hasConcept C50522688 @default.
• W3215809611 hasConcept C545542383 @default.

• next