Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India.
Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at...
| Publicado en: | Journal of Infection Vol. 84; no. 3; pp. 351 - 355 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
W B Saunders
Mar2022
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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=155490073&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155490073 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01634453 8IL jtl: Journal of Infection issn: 01634453 maglogo: N pubinfo: dt: Mar2022 vid: 84 iid: 3 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 155490073 155490073 NLM34953910 10.1016/j.jinf.2021.12.016 NLM34953910 155490073 ppf: 351 ppct: 4 formats: tig: atl: Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India. aug: au: Syed-Abdul, Shabbir Babu, A. Shoban Bellamkonda, Raja Shekhar Itumalla, Ramaiah Acharyulu, GVRK Krishnamurthy, Surya Ramana, Y. Venkat Santosh Mogilicharla, Naresh Malwade, Shwetambara Li, Yu-Chuan Li, Yu-Chuan Jack affil: Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 5F., No. 172-1, Sec. 2, Keelung Rd. Da'an, Taipei 106, Taiwan sug: ab: Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital.Methods: The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error.Results: The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis.Conclusion: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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