Evidence map›Paper›PMID 41623846›Full record

ArticleBioinformation2025

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

Anshuman Srivastava, Shailesh Tripathi, Ravikant R, Parth Jani, Mukul Singh, Amrit Podder, Mohammed Mustafa, Mukesh Kumar Patwa

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Anshuman SrivastavaDepartment of General Medicine, Infinity Care Hospital, Varanasi, Uttar Pradesh, India.
Shailesh TripathiDepartment of Hospital Administration, RIMS Ranchi, Jharkhand, India.
Ravikant RDepartment of Microbiology, Nootan Medical College and Research Centre, Sankalchand Patel University, Visnagar, Gujarat, India.
Parth JaniDepartment of General Medicine, All India Institute of Medical Sciences, Rajkot, Gujarat, India.
Mukul SinghDepartment of General Surgery, All India Institute of Medical Sciences, Gorakhpur, Uttar Pradesh, India.
Amrit PodderDepartment of Physiology, Teerthanker Mahaveer Medical College & Research Centre, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India.
Mohammed MustafaDepartment of Conservative Dental Sciences, College of Dentistry, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Mukesh Kumar PatwaDepartment of Microbiology, ASMC Gonda, Uttar Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Antibiotic resistanceArtificial Intelligencehospital-based studymachine learningpredictive models

Identifiers

PMID41623846
PMCPMC12859369

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.