Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases...European Federation for Medical Informatics (EFMI) Special Topic Conference (Virtual), November 22-24, 2021.
Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited time to discuss all cases. Complex breast cancer cas...
| Publicado en: | Studies in Health Technology & Informatics no. 287; pp. 144 - 149 |
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| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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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=153781194&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153781194 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2021 iid: 287 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 153781194 153781194 153781194 10.3233/SHTI210834 153781194 ppf: 144 ppct: 5 formats: tig: atl: Deep Learning, a Not so Magical Problem Solver: A Case Study with Predicting the Complexity of Breast Cancer Cases...European Federation for Medical Informatics (EFMI) Special Topic Conference (Virtual), November 22-24, 2021. aug: au: LE THIEN, My-Anh REDJDAL, Akram BOUAUD, Jacques SEROUSSI, Brigitte affil: Sorbonne Université, Université Sorbonne Paris Nord, Inserm, UMR S_1142, LIMICS, Paris, France sug: subj: Deep Learning Breast Neoplasms Decision Support Systems, Clinical Human Workflow Machine Learning Algorithms Multidisciplinary Care Team Neural Networks (Computer) Congresses and Conferences ab: Using guideline-based clinical decision support systems (CDSSs) has improved clinical practice, especially during multidisciplinary tumour boards (MTBs) in cancer patient management. However, MTBs have been reported to be overcrowded, with limited time to discuss all cases. Complex breast cancer cases that need further MTB discussions should have priority in the organization of MTBs. In order to optimize MTB workflow, we attempted to predict complex cases defined as non-compliant cases despite the use of the decision support system OncoDoc. After previously obtaining insufficient performance with machine learning algorithms, we tested Multi Layer Perceptron for classification, compared various samplers to compensate data imbalance combined with crossvalidation, and optimized all models with hyperparameter tuning and feature selection with no improvement and lacklustre results (F1-score: 31.4%). pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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