Title

Implementation of AI for predicting antibiotic resistance patterns: A hospital-based study

Authors

Anshuman Srivastava¹*, Shailesh Tripathi², R Ravikant³, Parth Jani⁴, Mukul Singh⁵, Amrit Podder⁶, Mohammed Mustafa⁷ & Mukesh Kumar Patwa⁸

Affiliation

¹Department of General Medicine, Infinity Care Hospital, Varanasi, Uttar Pradesh, India; ²Department of Hospital Administration, RIMS Ranchi, Jharkhand, India; ³Department of Microbiology, Nootan Medical College and Research Centre, Sankalchand Patel University, Visnagar, Gujarat, India; ⁴Department of General Medicine, All India Institute of Medical Sciences, Rajkot, Gujarat, India; ⁵Department of General Surgery, All India Institute of Medical Sciences, Gorakhpur, Uttar Pradesh, India; ⁶Department of Physiology, Teerthanker Mahaveer Medical College & Research Centre, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India; ⁷Department of Conservative Dental Sciences, College of Dentistry, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia; ⁸Department of Microbiology, ASMC Gonda, Uttar Pradesh, India; *Corresponding author

Email

Anshuman Srivastava - E-mail: anshumansrivastava2503@gmail.com
Shailesh Tripathi - E-mail: shaileshtripathi1971@gmail.com
R Ravikant - E-mail: ravikantbscity@gmail.com
Parth Jani - E-mail: parthjani13@gmail.com
Mukul Singh - E-mail: singhmukul3911@gmail.com
Amrit Podder - E-mail: amritpodder0@gmail.com
Mohammed Mustafa - E-mail: ma.mustafa@psau.edu.sa
Mukesh Kumar Patwa - E-mail: kumar.mukesh.patwa@gmail.com

Article Type

Research Article

Date

Received October 1, 2025; Revised October 31, 2025; Accepted October 31, 2025, Published October 31, 2025

Abstract

The use of Artificial Intelligence (AI) to predict antibiotic resistance patterns in a hospital setting is of interest. By leveraging machine learning (ML) models, including Random Forest, Logistic Regression and Support Vector Machines, the study aimed to predict resistance based on patient demographics, microbial species and clinical data. The Random Forest model outperformed other models in terms of accuracy, precision and recall. Data shows the importance of integrating AI-driven tools into clinical workflows for improved antibiotic stewardship and patient outcomes. Despite challenges, AI presents a promising approach for combating antibiotic resistance in healthcare.

Keywords

Antibiotic resistance, Artificial Intelligence, hospital-based study, machine learning, predictive models

Citation

Srivastava et al. Bioinformation 21(10): 3635-3639 (2025)

Edited by

Ritik Kashwani

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.