Improving pre-bariatric surgery diagnosis of hiatal hernia using machine learning models.
Bariatric patients have a high prevalence of hiatal hernia (HH). HH imposes various difficulties in performing laparoscopic bariatric surgery. Preoperative evaluation is generally inaccurate, establishing the need for better preoperative assessment. To utilize machine learning ability to improve pre...
| Publicado en: | Minimally Invasive Therapy & Allied Technologies Vol. 31; no. 5; pp. 760 - 768 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2022
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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=157509038&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157509038 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13645706 J4S jtl: Minimally Invasive Therapy & Allied Technologies issn: 13645706 maglogo: Y pubinfo: dt: Jun2022 vid: 31 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 157509038 149506400 157509038 157509038 10.1080/13645706.2021.1901120 157509038 ppf: 760 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving pre-bariatric surgery diagnosis of hiatal hernia using machine learning models. aug: au: Assaf, Dan Rayman, Shlomi Segev, Lior Neuman, Yair Zippel, Douglas Goitein, David affil: Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel sug: subj: Bariatric Surgery Hernia, Diaphragmatic Diagnosis Machine Learning Utilization Preoperative Period Length of Stay Human Male Female Adult Middle Age Israel Anthropometry Decision Trees Hospitalization Retrospective Design Data Analysis Software ROC Curve Sensitivity and Specificity Descriptive Statistics Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Bariatric patients have a high prevalence of hiatal hernia (HH). HH imposes various difficulties in performing laparoscopic bariatric surgery. Preoperative evaluation is generally inaccurate, establishing the need for better preoperative assessment. To utilize machine learning ability to improve preoperative diagnosis of HH. Machine learning (ML) prediction models were utilized to predict preoperative HH diagnosis using data from a prospectively maintained database of bariatric procedures performed in a high-volume bariatric surgical center between 2012 and 2015. We utilized three optional ML models to improve preoperative contrast swallow study (SS) prediction, automatic feature selection was performed using patients' features. The prediction efficacy of the models was compared to SS. During the study period, 2482 patients underwent bariatric surgery. All underwent preoperative SS, considered the baseline diagnostic modality, which identified 236 (9.5%) patients with presumed HH. Achieving 38.5% sensitivity and 92.9% specificity. ML models increased sensitivity up to 60.2%, creating three optional models utilizing data and patient selection process for this purpose. Implementing machine learning derived prediction models enabled an increase of up to 1.5 times of the baseline diagnostic sensitivity. By harnessing this ability, we can improve traditional medical diagnosis, increasing the sensitivity of preoperative diagnostic workout. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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