https://dspace.iiti.ac.in:8080/jspui/handle/123456789/12539 2026-05-12T17:10:11Z 2026-05-12T17:10:11Z Kalia, Himanshu https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17924 2026-04-28T12:12:52Z 2026-01-01T00:00:00Z

Title: Innovative approaches in treating paroxysmal sympathetic hyperactivity following traumatic brain injury: a comprehensive review Authors: Kalia, Himanshu Abstract: Introduction: Paroxysmal Sympathetic Hyperactivity (PSH) is a historically underrecognized yet increasingly acknowledged syndrome following traumatic brain injury (TBI), characterized by episodic surges in sympathetic nervous system activity. Despite increasing awareness, effective therapy remains unavailable due to diagnostic uncertainty and therapeutic heterogeneity. Areas covered: In this review, the authors synthesize the recent advances and emerging fronts in the treatment of PSH, encompassing mechanistic understanding, drug discovery, non-pharmacological treatment, and trials in progress. They also outline areas of knowledge deficit and offer suggestions for future research. Expert opinion: There are several ongoing challenges, including variability in diagnostic approaches and inconsistent outcome measures. There is also an absence of unified treatment protocols that limit clinical consistency and hamper research comparability. Improving alignment between acute ICU management and long-term rehabilitation is similarly important. Moving forward, precision medicine, predictive biomarker development, and individualized treatment modeling offer significant promise. There is optimism that identifying at-risk populations or individuals earlier could enable timely treatment and support the development of more targeted, mechanism-based management strategies that combine both pharmacologic and non-pharmacologic interventions. © 2026 Informa UK Limited, trading as Taylor & Francis Group.

2026-01-01T00:00:00Z Kale, Apoorwa Khandelwal, Yash Pandhare, Vibhor Ghosh, Atreyee Pathak, Nidhi Lad, Bhupesh Kumar https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17803 2026-04-28T12:12:34Z 2025-01-01T00:00:00Z

Title: A SCALABLE, LOW-COST FRAMEWORK FOR MULTILINGUAL INTELLIGENT DOCUMENT PROCESSING FOR CONTINUITY OF CARE Authors: Kale, Apoorwa; Khandelwal, Yash; Pandhare, Vibhor; Ghosh, Atreyee; Pathak, Nidhi; Lad, Bhupesh Kumar Abstract: Paper-based prescriptions and reports constitute a major part of the medical health records across the globe. Accordingly, paper-based manual data recording is a common practice in the Community Health Centers (CHCs) in India. These records result in poor handling, fragmented information, inefficient data retrieval, sharing, and storage of clinical data. To address this gap, we present Intelligent Document Processing Application (IDPA), a low-cost, scalable data digitization pipeline combining Optical Character Recognition (OCR) and Vision-Language Models (VLMs) for converting bilingual (Hindi-English), handwritten, and numerical medical records into structured digital formats. IDPA comprises a two-stage pipeline, where Stage 1 employs table cell segmentation using OpenCV and Stage 2 uses OCR extraction with PaliGemma VLM. As a proof-of-concept, the application was tested using a dataset of 150 patient records of the Indian population, which exhibited prevalent data input issues including overwritten texts, obscured columns, and the application of whiteners. PaliGemma, refined using over 650 labelled table cell images, attained 74% accuracy and a 13% Character Error Rate (CER), outperforming other open-source VLM models in extracting the medical records. The extracted data is organized into structured dataframes, served through a FastAPI endpoint, and accessible through a Progressive Web App (PWA). The interface supports secure user authentication via Clerk API and enables real-time image upload, editable tabular outputs, and data export in CSV/PDF formats. Together, these digital tools offer an affordable, user-centric approach to improve healthcare data management in low-resource settings. They hold strong potential for integration with national health systems, improvement of continuity of care, enablement of longitudinal monitoring, and expansion into predictive analytics for clinical decision support. © This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

2025-01-01T00:00:00Z Aarzoo Ghosh, Atreyee Pandhare, Vibhor Bhattacharjee, Soumyabrata Lad, Bhupesh Kumar https://dspace.iiti.ac.in:8080/jspui/handle/123456789/15605 2025-05-30T12:39:15Z 2024-01-01T00:00:00Z

Title: Expert-in-Loop Digital Twin-based Decision Support System for Early Detection of Ventilator-Induced Lung Injury Authors: Aarzoo; Ghosh, Atreyee; Pandhare, Vibhor; Bhattacharjee, Soumyabrata; Lad, Bhupesh Kumar Abstract: Mechanical ventilation has been a critical life support mechanism for patients with severe traumatic brain injuries (TBI) for decades. While lifesaving, mechanical ventilation has drawbacks, including the risk of complications such as Ventilator-Induced Lung Injury (VILI). This paper focuses on Ventilator-Associated Pneumonia (VAP), a significant complication that can adversely affect patient health and extend the duration of ventilation, thereby increasing the cost of critical care. To address this issue, we propose a novel Decision Support System (DSS) framework for the early detection of VAP, utilizing Digital Twin technology and the expertise of healthcare professionals. The framework is aligned with ISO-23247, with the addition of two new functional entities: a Learning Module and a Rule-Based System. Early detection of VILI through this DSS framework can significantly reduce weaning time, enhancing the affordability and accessibility of critical care. The framework offers real-time tracking and detection of patient conditions, enabling timely interventions and personalized treatment, which ultimately improves patient outcomes and optimizes resource utilization in critical care. This integration of Digital Twin technology and clinical expertise introduces a crucial advancement in critical care facilities in the context of today's AI-driven healthcare landscape. © 2024 The Authors.

2024-01-01T00:00:00Z Bhattacharjee, Soumyabrata Pandhare, Vibhor https://dspace.iiti.ac.in:8080/jspui/handle/123456789/14971 2025-05-30T12:38:54Z 2024-01-01T00:00:00Z

Title: Deriving inferences through natural language from structured datasets for asset lifecycle management Authors: Bhattacharjee, Soumyabrata; Pandhare, Vibhor Abstract: Industry 4.0 is expected to revolutionize the manufacturing world in terms of improved decision-making leading to increased value extraction from assets. Yet, since its inception, its adoption seems to have not scaled with time as expected. One of the bottlenecks can be due to the knowledge disparity between the Industry 4.0 technology developer and the domain expert/end user, which can lead to higher resources, cost, and time requirements for scalable adoption. Modern day Large Language Models (LLMs) have the ability to process large datasets and can serve as an intermediatory tool for domain end-users. Thus, in this paper, an Industrial-GPT tool is developed to translate natural language queries into meaningful inferences about asset-related data. The tool is evaluated using an exemplary dataset, representing a manufacturing industry with four production lines, multiple assets, readings and inferences from multiple sensors. The experimental results show that reasonable insight may be extracted from the dataset, using prompt engineering, while fine-tuning the model to adapt to business logic can help achieve better and faster inferences. Future research directions are proposed that can be addressed while developing Industrial-GPT-based tools and reducing the development cycle for Industry 4.0 technologies. Copyright © 2024 The Authors.

2024-01-01T00:00:00Z