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Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India.
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.
  • Syed-Abdul S; 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; International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical Univers
  • Babu AS; Department of ENT, Gandhi Medical College and Hospital, Musheerabad, Secunderabad, Telangana 500003, India. Electronic address: shobhanaachi@gmail.com.
  • Bellamkonda RS; School of Management Studies, University of Hyderabad, Hyderabad, Telangana, India.
  • Itumalla R; Department of Health Management, College of Public Health and Health Informatics, University of Hail, Hail, Saudi Arabia.
  • Acharyulu G; School of Management Studies, University of Hyderabad, Hyderabad, Telangana, India.
  • Krishnamurthy S; Data Scientist, iQGateway, Bengaluru, Karnataka, India.
  • Ramana YVS; Department of Hospital Administration, Gandhi Hospital, Secunderabad, Telangana, India.
  • Mogilicharla N; Department of ENT, Gandhi Medical College and Hospital, Musheerabad, Secunderabad, Telangana 500003, India.
  • Malwade S; International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
  • Li YC; 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; International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical Univers
J Infect ; 84(3): 351-354, 2022 03.
Article in English | MEDLINE | ID: covidwho-1587244
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Mucormycosis Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: Asia Language: English Journal: J Infect Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 / Mucormycosis Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: Asia Language: English Journal: J Infect Year: 2022 Document Type: Article