Akas Gajanan Prabhune

@iihmrbangalore.edu.in

Assistant Professor Department of Health IT
Institute of Health Management Research Bangalore

Akas Gajanan Prabhune


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https://researchid.co/prabhunesky

EDUCATION

Bachelor of Dental Surgery, Master of Clinical Resrarch

RESEARCH INTERESTS

Dr. Akash is a Health researcher with 5+ years of experience in Health Systems Strengthening, Digital Health and AI/ML applications in healthcare management. He has worked with various state Governments, Indian Council of Medical Research, several national and international funding agencies
14

Scopus Publications

132

Scholar Citations

6

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Development and Validation of a Predictive AI Framework for Diabetic Foot Ulcer Monitoring and Severity Assessment: A Step towards Self-monitoring and Primary Care Integration
    Subodh S. Satheesh, Akhila Rayampalli, Akash G. Prabhune, Vinay R. Sri Hari
    Healthcare Informatics Research, 2026
    Objectives: Diabetic foot ulcer (DFU) is a critical complication of diabetes that can lead to severe outcomes such as infection, amputation, and increased mortality if left untreated. Early detection and continuous monitoring are essential but remain challenging, especially in resource-limited settings such as India. This study developed and validated a deep learning algorithm to classify diabetic foot images into severity grades based on the International Working Group on the Diabetic Foot classification: grade 0 (healthy), grade 1 (mild), grade 2 (moderate), and grade 3 (severe).Methods: A dataset of 407 clinical images was collected from open-source platforms and clinics in South India and expanded to 612 images through data augmentation. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. Multiple machine learning models were tested, including MobileNet_V2, EfficientNet-b0, DenseNet121, ResNet_50, VGG16, and ViT_b_16.Results: Among the evaluated models, MobileNet_V2 demonstrated the highest validation accuracy (82%) and achieved an F1-score of 79% on the test set. Although the model showed strong training accuracy, minor overfitting was observed, particularly in distinguishing adjacent severity grades. To address this, dropout, batch normalization, and early stopping were employed. Overall, the model generalized well, showing high accuracy in detecting healthy cases and acceptable performance across ulcer severity grades.Conclusions: This study underscores the potential of machine learning-based tools to support frontline healthcare workers and facilitate patient self-monitoring in low-resource environments. Future work will focus on refining the model and integrating it into user-friendly applications.
  • A web-based platform for optimizing healthcare resource allocation and workload management using agile methodology and WISN theory
    Akash Gajanan Prabhune, P S Karpaga Priya, Rohit Chandra, Ankur Thakur, Viany R Srihari, Sachin S Bhat
    BMC Health Services Research, 2025
    BACKGROUND: Effective healthcare workforce management is critical for ensuring quality care delivery, particularly in resource-constrained settings. The World Health Organization's (WHO) Workload Indicators of Staffing Need (WISN) methodology provides an evidence-based framework for optimizing staffing levels. However, manual implementation of the WISN methodology is labour-intensive, error-prone, and time-consuming. To address these challenges, the Platform for Resource Allocation and Optimization for Healthcare Facilities (PRAYOJN) platform was developed as a web-based tool to automate WISN calculations, streamline data analysis, and improve workforce planning. OBJECTIVE: To develop and validate a web-based system that automates the WISN methodology for healthcare workforce planning. METHODS: The PRAYOJN platform was developed using an agile methodology, structured over five iterative sprints. These sprints incorporated stakeholder feedback to refine system functionalities, ensuring adaptability to real-world healthcare needs. The platform integrates data for principal, supporting, and ancillary tasks to calculate staffing requirements. Key functionalities include automated computation of Available Work Time (AWT), Standard Workload (SW), Category Allowance Factor (CAF), and Individual Allowance Factor (IAF). Alpha testing validated usability and accuracy, while beta testing in a clinical phlebotomy department assessed real-world performance. RESULTS: The platform calculated an ideal staffing requirement of 15.53 Full-Time Equivalent (FTE) for the phlebotomy department, aligning closely with the current staff strength of 15 FTE. Agile development ensured iterative improvements, enhancing user interface (UI) and user experience (UX). Feedback highlighted the platform's user-friendly design, with dynamic visualizations such as pie charts and bar graphs aiding workload interpretation. Users praised its efficiency, adaptability, and role in reducing calculation complexity. CONCLUSION: PRAYOJN modernizes and enhances WISN-based workforce planning by automating workload calculations, improving data visualization, and supporting real-time decision-making. Its scalability and intuitive interface position it as a valuable tool for optimizing staffing efficiency across diverse healthcare environments.
  • Development and validation of an AI-based application for early detection and risk stratification of oral potentially malignant disorders
    Akash Gajanan Prabhune, Vinay R. Srihari, Shreya Shree, Manish Katiyar, Vipin Thampi
    Journal of Oral Biology and Craniofacial Research, 2025
    Background: Oral Potentially Malignant Disorders (OPMDs) are early indicators of oral cancer, and timely detection is essential for improving patient outcomes. However, diagnosis often relies on expert clinical evaluation, which may not be available in low-resource settings. Objective: This study presents the development and validation of PRAYAAS, an AI-based mobile application for early detection and risk stratification of OPMDs using intraoral images. Methods: A total of 794 intraoral images were classified into three categories: (1) Normal mucosa/inflammatory conditions, (2) Premalignant conditions, and (3) Oral carcinoma. Images were split into training (70 %), validation (18 %), and test (12 %) datasets while maintaining class balance. Preprocessing involved resizing to 224 × 224 pixels, contrast enhancement, and normalization. A U-Net-based model segmented lesion regions, followed by classification using a fine-tuned DenseNet201 model. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrices. Results: The DenseNet201 classifier achieved 94 % accuracy on the test set. For normal/inflammatory lesions, precision and recall were 1.00. For premalignant lesions, precision was 0.87 and recall was 1.00. For carcinoma, precision was 1.00 and recall was 0.80. The integrated segmentation module improved lesion focus and reduced background noise. The app provides class-wise risk scores and a user-friendly interface for clinical support. Conclusion: PRAYAAS offers a robust, mobile-enabled solution for early OPMD screening. By integrating segmentation and classification into a single platform, the tool holds promise for enhancing community-based oral cancer detection and referral.
  • Virtual Reality for Procedural Skill Training in Medical Education
    Akash Gajanan Prabhune, Ragaviveka Gopalan, V.N. Chaithanya, Adith Chinnaswami
    Augmented and Virtual Reality in Immersive Healthcare, 2025
    This chapter explores the utilization of virtual reality (VR) technology for procedural skill training in medical education. Procedural skill training is a key component of medical education. However, while effective, traditional training methods often lack patient safety in clinical environments, offer insufficient practicum exposure, and lack engagement in non-patient learning environments. This study aimed to explore the effectiveness of VR in improving ECG competency among medical students. Thirty second-year medical students underwent VR training on ECG performance, with knowledge and skill acquisition assessed pre- and post-training. Overall, the students demonstrated significant improvement in procedural knowledge and skills for ECG post-training. VR was rated highly for usability, spatial presence, and realism, and participants reported feeling high levels of satisfaction. Thematic analyses revealed four themes: experience with VR, learning process, engagement and motivation, and application in the real world. Participants found VR engaging and fun, experienced a transition from initial difficulty to comfort, and reported increased confidence and preparedness for real-world applications. These results suggest that VR can significantly improve ECG training outcomes, providing an effective, immersive learning experience.
  • A Hybrid RoBERTa—Rule-based Aspect-based Sentiment Analysis Model for Hospital Patient Reviews: Development, Validation and Case Application in India
    Akash Gajanan Prabhune, Karpaga Priya P S, Vinay R Sri Hari, Akhila Rayampalli, Ankur S Thakur, Ebin M Yohannan
    Journal of Health Research, 2025
    Background: Patient Online Reviews (PORs) provide real-time, unsolicited feedback that complements traditional satisfaction surveys. However, existing sentiment analysis tools often handle only monolingual data, broad sentiment categories, or lack review authenticity checks. This study develops a hybrid Aspect-Based Sentiment Analysis (ABSA) model combining RoBERTa with a rule-based aspect detection layer to extract fine-grained sentiment insights from Indian hospital reviews. Methods: We curated and manually annotated 4,862 Google Maps reviews from 25 Indian hospitals across six aspects—Doctor Behavior, Staff Behavior, Cleanliness, Facility Experience, Cost, and Waiting Time. Though the scheme included positive/negative/neutral labels, the final model used binary polarity (positive vs. negative) for reliability given sparse neutral data. The hybrid pipeline integrated RoBERTa fine-tuning with a keyword dictionary to enhance recall on low-frequency or implicit mentions. Review authenticity was verified using Type-Token Ratio, Jaccard similarity, and bigram overlap. External validation used 670 independent reviews. Results: The model achieved an average F1 score of 0.923 and 92.67% accuracy. Sensitivity and specificity averaged 0.905, with PPV 94.27% and NPV 87.82%. Comparative testing showed superior performance over lexicon-based and monolingual transformer models. A case study revealed strong satisfaction with clinical care but concerns over waiting time. Conclusions: The hybrid ABSA framework converts unstructured patient feedback into structured, actionable insights for hospital dashboards, audits, and digital health systems, supporting quality improvement in resource-limited, multilingual settings. Keywords: Sentiment analysis, Healthcare, Patient experience, Hybrid model, India
  • Agile fusion: developing 'Eat at Right Place' sentiment analysis tool
    Akash Prabhune, Vinay R Srihari, Neeraj Kumar Sethiya, Mansi Gauniyal
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.
  • Optimizing Medication Access in Public Healthcare Centers: A Machine Learning Stochastic Model for Inventory Management and Demand Forecasting in Primary Health Services
    Sachin S Bhat, Vinay R Srihari, Akash Prabhune, Subodh S Satheesh, Ananya Biswas Bidrohi
    Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing Electrical and Electronics Iciitcee 2024, 2024
    This article discusses the critical issue of inefficient inventory management and demand forecasting in public healthcare systems, highlighting the significant impact of this problem on patient care and healthcare costs. It introduces a novel approach that leverages machine learning and advanced forecasting techniques to address this challenge. The methodology involves mapping the supply chain, data collection, preprocessing, regression analysis, and the creation of three forecasting models: Projective, Causal, and Stochastic. The results of regression analysis identify key factors affecting stockouts, while the forecasting models predict drug demand and supply requirements. The integration of inventory cost optimization models further enhances the precision of stock requirement forecasts. These models offer a promising solution to improve medication access, reduce stockouts, and optimize resource allocation in primary healthcare services, ultimately leading to more efficient and equitable healthcare delivery in the public sector.
  • Improving Manpower Allocation at Primary Healthcare Facilities: Development and Validation of a Machine Learning Quadratic Model to Strengthen Public Health Service Availability
    Sachin S Bhat, Vinay R Srihari, Akash Prabhune, Aishwarya Mallawaram, Ananya Biswas Bidrohi
    Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing Electrical and Electronics Iciitcee 2024, 2024
    The paper addresses the critical issue of human resource shortages in healthcare, particularly in developing countries like India. It emphasizes the importance of adequate and well-distributed health professionals for achieving desirable health outcomes. The focus is on quadratic modelling as a tool for optimal planning in healthcare systems, considering the composition and distribution of health personnel.The methodology involves creating a comprehensive unit optimization framework using a Demand Index, Supply Index, and Distance Matrix to derive a Site Suitability Index. The Demand Index integrates demographic, mortality, and footfall data, while the Supply Index considers medical, para-medical, and non-medical staff weights. A Gravity model is employed for accessibility scoring, and a three-by-three matrix is used for rule-based classification. The quadratic optimization model aims to maximize human resource allocation based on physical accessibility, minimizing the cost of deviation from desired healthcare standards. Data mining and cleaning involve secondary data from various sources, subjected to screening using geographical and population data.Results include a Gravity model-derived Baseline Accessibility and Burden Index, categorizing villages and PHCs. The quadratic optimization model allocates resources according to IPHS standards, using a weekly timetable for staff sharing to address shortages. The model successfully optimizes human resource allocation based on demand burden and accessibility, presenting a systematic approach to address healthcare workforce challenges.
  • Application of Scrum framework and Low Code No Code platform for development and implementation of In-patient electronic visitor management system to optimise hospital operations
    Kalaivani S, Arun Senthilkumar, Akash Prabhune, Mathan Babu Durairaj, Sachin S Bhat
    Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing Electrical and Electronics Iciitcee 2024, 2024
    This study focuses on the development of an Electronic Visitor Management System for small-scale hospitals, utilizing an Agile-based Scrum Framework and a Low No-Code (Low Code No Code) platform. The primary aim is to address the challenges of managing crowds and footfalls in hospital in-patient departments (IPD) efficiently. The methodology employed for this project involved the use of the mHealth agile framework, combining clinical product development stages with agile development. A Scrum team was formed, and a comprehensive project timeline was established, with a series of sprints, each with its specific goals and associated product backlog items. The development tools utilized included the Zoho Low Code No Code platform, SQL database, and Android Studio for publishing the application. Results from each sprint are discussed, ranging from defining the project scope to releasing the successful application. The study emphasizes the importance of user consultation for enhancing the User Interface (UI) and User Experience (UX) of the application. Feedback from stakeholders and end-users played a crucial role in refining the product. Overall, this study showcases the successful development of an Electronic Visitor Management System for hospitals, highlighting the role of technology and healthcare professionals in digital health initiatives, reducing development time and costs.
  • Enhancing Accessibility to Primary Healthcare Centres through the Development and Validation of a Machine Learning-based Gravity Model: Strengthening Public Health Coverage
    Akash Prabhune, Vinay R Srihari, Ananya Biswas Bidrohi, Ashitha Reddy, Aishwarya Mallawaram
    Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing Electrical and Electronics Iciitcee 2024, 2024
    This paper introduces a novel approach, a Machine Learning-based Gravity Model, to address this issue by predicting and mitigating disparities in healthcare access based on geographical location, population distribution, transportation infrastructure, and healthcare workforce availability. The research focuses on the Chikkaballapur district in Karnataka, Southern India, as a proof of concept.The methodology involves developing an optimization model for healthcare facility locations by analyzing the geospatial and demographic characteristics of the district. The goal is to maximize accessibility to primary health units while minimizing the burden on these healthcare facilities. To achieve this, the study considers various factors from the demand side (village population, literacy rates, agricultural and non-agricultural workers, OPD and IPD data, among others), supply side (healthcare staff), and distance and travel (including travel friction). Composite indexes are constructed to represent demand, supply, and travel factors, contributing to the overall accessibility and burden calculations.The study classifies villages based on their accessibility scores, separating them into categories of poor, average, and high accessibility. Similarly, Primary Health Centers (PHCs) are classified as underutilized, average, or overutilized based on their burden scores. Using these classifications, the paper presents case scenarios and a rule-based algorithm to recommend actions, such as upgrading or downgrading PHCs and building new ones, aimed at improving accessibility and addressing burden disparities.In conclusion, the Gravity-Based Optimization Model offers a promising solution to the complex challenge of healthcare accessibility and facility optimization in India.
  • Provision of rehabilitation and assistive technology services in a low resource setting during the COVID-19 pandemic and introduction of telehealth: Service users’ and providers’ perspectives
    Ritu Ghosh, Aoife Healy, Akash Prabhune, Aishwarya Mallavaram, Sama Raju, Nachiappan Chockalingam
    Assistive Technology, 2024
  • Adaptation of a BERT Model to the India Restaurant data using Rule-based Approach for Aspect-based Sentiment Analysis
    Akash Gajanan Prabhune, Vinay R Shri Hari, Neeraj Kumar Sethiya
    2023 2nd International Conference on Smart Technologies for Smart Nation Smarttechcon 2023, 2023
  • A research framework for passive surveillance for food safety from social media: Identification and evaluation of customer reviews for regulatory use and case study of 30 restaurants
    Akash Gajanan Prabhune, Neeraj Kumar Sethiya, Heemanshu Arora
    Indian Journal of Forensic and Community Medicine, 2022
  • Assessment of healthcare utilization in a community-centric model of primary healthcare for rural populations
    Akash Prabhune, Aparna Manoharan
    Indian Journal of Public Health Research and Development, 2017

RECENT SCHOLAR PUBLICATIONS

  • Designing Effective Surgical Messaging on Instagram: Evidence From a Robotic Nipple-Sparing Mastectomy Campaign
    SS Chikhalikar, AS Chandrasekaran, AG Prabhune
    Journal of Health and Allied Sciences NU , 2026
    2026
  • Development and Validation of a Predictive AI Framework for Diabetic Foot Ulcer Monitoring and Severity Assessment: A Step towards Self-monitoring and Primary Care Integration
    SS Satheesh, A Rayampalli, AG Prabhune, VRS Hari
    Healthcare Informatics Research 32 (1), 69-76 , 2026
    2026
  • Comparative Evaluation of YOLOv12 and SAHI for Medication Identification in Hospital Pharmacies
    B Malepati, S Nandamury, U Manjunath, D Rajan, AG Prabhune
    2026 International Conference on Intelligent and Innovative Technologies in … , 2026
    2026
  • Exploring the Potential of Virtual Reality-Based Procedural Skill Training in Reducing Medical Errors: A Pilot Study
    AG Prabhune, R Gopalan, SS Bhat, A Chinnaswami
    2026 International Conference on Intelligent and Innovative Technologies in … , 2026
    2026
  • Improving surgical antibiotic prophylaxis compliance: A tertiary hospital quality improvement study
    AG Prabhune, RT Poojary
    Medicine Research and Clinical Practice 3 (1), 8-13 , 2026
    2026
  • Virtual Reality for Procedural Skill Training in Medical Education: An Exploratory Study
    AG Prabhune, R Gopalan, VN Chaithanya, A Chinnaswami
    Augmented and Virtual Reality in Immersive Healthcare, 353-379 , 2025
    2025
  • Development and validation of an AI-based application for early detection and risk stratification of oral potentially malignant disorders
    AG Prabhune, VR Srihari, S Shree, M Katiyar, V Thampi
    Journal of Oral Biology and Craniofacial Research 15 (6), 1806-1812 , 2025
    2025
    Citations: 3
  • A web-based platform for optimizing healthcare resource allocation and workload management using agile methodology and WISN theory
    AG Prabhune, PSK Priya, R Chandra, A Thakur, VR Srihari, SS Bhat
    BMC Health Services Research 25 (1), 400 , 2025
    2025
    Citations: 8
  • Bridging gaps in tuberculosis control: a culturally competent approach for tribal populations in India
    AG Prabhune, P Dadha, A Prabhune
    Cureus 17 (3) , 2025
    2025
    Citations: 5
  • Utilising consumer reviews for passive surveillance of foodborne illnesses: insights and challenges from the Indian restaurant
    A Prabhune, VS Hari, NK Sethiya, M Gauniyal
    International Journal of Public Health 14 (1), 479-492 , 2025
    2025
    Citations: 1
  • A Hybrid RoBERTa–Rule-Based Aspect-Based Sentiment Analysis Model for Hospital Patient Reviews: Development, Validation, and Case Application in India
    AG Prabhune, KP PS, VR Hari, A Rayampalli, AS Thakur, EM Yohannan
    Journal of Health Research 39 (6), 6 , 2025
    2025
    Citations: 1
  • Provision of rehabilitation and assistive technology services in a low resource setting during the COVID-19 pandemic and introduction of telehealth: service users’ and …
    R Ghosh, A Healy, A Prabhune, A Mallavaram, S Raju, N Chockalingam
    Assistive Technology 36 (6), 405-411 , 2024
    2024
    Citations: 6
  • Internet-driven self-medication practices in Urban India: A cross-sectional study across Bengaluru, Amritsar, and Chennai
    AG Prabhune, S Shriraam, VD Patil, PSK Priya
    Journal of Public Health and Community Medicine 1 (3), 109-109 , 2024
    2024
  • Developing Causality and Severity Assessment Frameworks for Food Safety Signals Using Social Media Reviews: A Technical Report Based on Data From an Urban Indian Suburb
    A Prabhune, VS Hari, NK Sethiya, M Gauniyal, VR Srihari
    Cureus 16 (7) , 2024
    2024
    Citations: 2
  • Agile fusion: developing eat at right place sentiment analysis tool
    A Prabhune, VR Srihari, NK Sethiya, M Gauniyal
    IJEECS 34 (1), 602 , 2024
    2024
    Citations: 4
  • Enhancing Accessibility to Primary Healthcare Centres through the Development and Validation of a Machine Learning-based Gravity Model: Strengthening Public Health Coverage
    A Prabhune, VR Srihari, AB Bidrohi, A Reddy, A Mallawaram
    2024 International Conference on Intelligent and Innovative Technologies in … , 2024
    2024
    Citations: 1
  • Application of Scrum framework and Low Code No Code platform for development and implementation of In-patient electronic visitor management system to optimise hospital operations
    S Kalaivani, A Senthilkumar, A Prabhune, MB Durairaj, SS Bhat
    2024 International Conference on Intelligent and Innovative Technologies in … , 2024
    2024
    Citations: 7
  • Improving manpower allocation at primary healthcare facilities: development and validation of a machine learning quadratic model to strengthen public health service availability
    SS Bhat, VR Srihari, A Prabhune, A Mallawaram, AB Bidrohi
    2024 International Conference on Intelligent and Innovative Technologies in … , 2024
    2024
    Citations: 5
  • Optimizing medication access in public healthcare centers: a machine learning stochastic model for inventory management and demand forecasting in primary health services
    SS Bhat, VR Srihari, A Prabhune, SS Satheesh, AB Bidrohi
    2024 International Conference on Intelligent and Innovative Technologies in … , 2024
    2024
    Citations: 20
  • Transforming hospital crowd management: a case study on Pravesh electronic visitor management system
    PSK Priya, AG Prabhune
    Artificial Intelligence 3 (1), 40-44 , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • Abelmoschus esculentus (Okra) potential natural compound for prevention and management of Diabetes and diabetic induced hyperglycemia. Tamil Nadu
    A Prabhune, M Sharma, B Ojha
    International Journal of Herbal Medicine 5 (2), 66-68 , 2017
    2017
    Citations: 32
  • Optimizing medication access in public healthcare centers: a machine learning stochastic model for inventory management and demand forecasting in primary health services
    SS Bhat, VR Srihari, A Prabhune, SS Satheesh, AB Bidrohi
    2024 International Conference on Intelligent and Innovative Technologies in … , 2024
    2024
    Citations: 20
  • A web-based platform for optimizing healthcare resource allocation and workload management using agile methodology and WISN theory
    AG Prabhune, PSK Priya, R Chandra, A Thakur, VR Srihari, SS Bhat
    BMC Health Services Research 25 (1), 400 , 2025
    2025
    Citations: 8
  • Application of Scrum framework and Low Code No Code platform for development and implementation of In-patient electronic visitor management system to optimise hospital operations
    S Kalaivani, A Senthilkumar, A Prabhune, MB Durairaj, SS Bhat
    2024 International Conference on Intelligent and Innovative Technologies in … , 2024
    2024
    Citations: 7
  • Assessment of healthcare utilization in a community-centric model of primary healthcare for rural populations
    A Prabhune, A Manoharan
    Indian Journal of Public Health Research & Development 8 (4) , 2017
    2017
    Citations: 7
  • Provision of rehabilitation and assistive technology services in a low resource setting during the COVID-19 pandemic and introduction of telehealth: service users’ and …
    R Ghosh, A Healy, A Prabhune, A Mallavaram, S Raju, N Chockalingam
    Assistive Technology 36 (6), 405-411 , 2024
    2024
    Citations: 6
  • Adaptation of a BERT model to the India restaurant data using rule-based approach for aspect-based sentiment analysis
    AG Prabhune, VRS Hari, NK Sethiya
    2023 Second International Conference On Smart Technologies For Smart Nation … , 2023
    2023
    Citations: 6
  • Bridging gaps in tuberculosis control: a culturally competent approach for tribal populations in India
    AG Prabhune, P Dadha, A Prabhune
    Cureus 17 (3) , 2025
    2025
    Citations: 5
  • Improving manpower allocation at primary healthcare facilities: development and validation of a machine learning quadratic model to strengthen public health service availability
    SS Bhat, VR Srihari, A Prabhune, A Mallawaram, AB Bidrohi
    2024 International Conference on Intelligent and Innovative Technologies in … , 2024
    2024
    Citations: 5
  • Effectiveness of Oral hygiene with chlorhexidine mouthwash with 0.12% and 0.2% concentration on incidence of ventilator associated pneumonia (VAP) in intubated patients–a …
    N Vyas, P Mathur, S Jhawar, A Prabhune, P Vimal
    Ann Int Med Dent Res 7 (3), 6 , 2021
    2021
    Citations: 5
  • Systematic review of prevalence, attitude, and practices of pubic hair removal activities
    AG Prabhune, D Nagrath, P Vimal
    Journal of Behavioral Health 8 (4), 170-176 , 2019
    2019
    Citations: 5
  • Agile fusion: developing eat at right place sentiment analysis tool
    A Prabhune, VR Srihari, NK Sethiya, M Gauniyal
    IJEECS 34 (1), 602 , 2024
    2024
    Citations: 4
  • Development and validation of an AI-based application for early detection and risk stratification of oral potentially malignant disorders
    AG Prabhune, VR Srihari, S Shree, M Katiyar, V Thampi
    Journal of Oral Biology and Craniofacial Research 15 (6), 1806-1812 , 2025
    2025
    Citations: 3
  • Past, present and future of breast cancer in Nepal: A review
    AP Biwesh Ojha, Randeep Kumar
    Global Journal of Medicine and Public Health 7 (No1:2018) , 2018
    2018
    Citations: 3
  • Developing Causality and Severity Assessment Frameworks for Food Safety Signals Using Social Media Reviews: A Technical Report Based on Data From an Urban Indian Suburb
    A Prabhune, VS Hari, NK Sethiya, M Gauniyal, VR Srihari
    Cureus 16 (7) , 2024
    2024
    Citations: 2
  • Evaluation of COVID-19 Vaccination Websites Using the DISCERN Tool and QUality Evaluation Scoring Tool
    PA Gajanan, M Aishwarya, B Sachin, P Samridhi, R Ashitha, HV Sri
    Osmania Journal of Medical Research 1 (1), 15-24 , 2024
    2024
    Citations: 2
  • A literature review on perceptions and practices related to healthcare and nutrition amongst the residents of urban slums across India
    AG Prabhune, U Manjunath, SS Satheesh, AGG Prabhune
    Cureus 15 (3) , 2023
    2023
    Citations: 2
  • A research framework for passive surveillance for food safety from social media: Identification and evaluation of customer reviews for regulatory use and case study of 30 …
    P Akash Gajanan, S Neeraj Kumar, A Heemanshu
    Indian Journal of Forensic and Community Medicine 9 (4), 146-152 , 2022
    2022
    Citations: 2
  • Literature review–Understanding the patient’s perception about chronic diseases and conditions, Barrier for non-adherence and poor follow-up
    A Prabhune
    InIKP Centre for Technologies in Public Health (ICTPH), India. IKP Centre … , 2020
    2020
    Citations: 2
  • Prevalence of diabetes and prediabetes among rural South Indian population
    AG Prabhune, B Ojha, A Manoharan
    International Journal of Community Medicine and Public Health 6 (1), 320 , 2019
    2019
    Citations: 2