International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 2
March-April 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Predictive Modeling of Antibiotic Prescription Patterns Using Machine Learning in Outpatient Settings

Author(s) Samee Mohammed, Sindhu Kommareddy, Holmes Walter
Country United States
Abstract Antibiotic resistance is currently a critical global health issue driven by improper and excessive use of antibiotics, particularly in outpatient settings. To mitigate this, machine learning (ML) techniques hold promise by leveraging vast clinical data to predict prescription patterns and optimize the utilization of antibiotics. This study takes into account the application of ML models for predicting antibiotic prescriptions based on patient features, history, physician prescribing patterns, and diagnosing variables.
Using an outpatient clinic electronic health records (EHRs) dataset, we developed and validated a range of ML models, from basic logistic regression and decision trees to random forests, support vector machines, and deep learning-based architectures. Feature selection techniques were employed to identify the most important drivers of prescription decision, such as previous use of antibiotics, comorbidities, and local antimicrobial resistance patterns. Model performance was quantified in terms of accuracy, precision, recall, and F1-score and indicated that ensemble learning methods and deep learning models offered the highest predictive accuracy.
The results identify the importance of ML-based predictive analytics in supporting antibiotic stewardship programs by identifying hazardous prescription patterns and providing real-time recommendations. With such models integrated into clinical decision support systems (CDSS), clinicians can prevent improper use of antibiotics, enhance patient outcomes, and minimize antimicrobial resistance. However, issues in integration are heterogeneity of data, interpretability of predictions by ML, and ethical dilemmas to be addressed in advancing responsible implementation in healthcare settings.
This research highlights the potential of ML to revolutionize antibiotic prescribing practices and impelling its future potential to enhance precision medicine and public health interventions. Improving the ML models with larger, mixed datasets and streaming real-time data needs to be considered for future research aimed at improving predictability and applicability to clinical decision-making.
Keywords Antibiotic prescription patterns, Machine learning, Predictive modeling, Outpatient settings, Electronic health records (EHR), Clinical decision support systems (CDSS), Antimicrobial resistance, Deep learning, Logistic regression, Random forests, Healthcare analytics, Data-driven prescribing, Feature selection, Precision medicine, Artificial intelligence in healthcare
Field Medical / Pharmacy
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-04-04
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.40879
Short DOI https://doi.org/g9dg38
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com