Tushar Mungle

Interconnected Challenges in Dementia Caregiving: A Co-occurrence Network Analysis of Burden, Unmet Needs, and System Failures Among Caregivers

Yeon Mi Hwang, Tushar Mungle, Audrey Angel Kwan, Malvika Pillai et autres

Abstract Background Alzheimer’s Disease and Related Dementias (ADRD) is a growing global public health challenge, and caregivers experience high rates of burden, unmet needs, and system failures. These challenges vary by caregiver role and relationship to the care recipient, reflecting the heterogeneous …

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Fine-Grained Emotional Characterization of Dementia Caregivers in Online Support Communities Using Large Language Models

Tushar Mungle, Audrey Angel Kwan, Yeon Mi Hwang, Malvika Pillai et autres

Abstract Background Dementia caregiving carries substantial emotional and psychological consequences, but most evidence comes from structured surveys and interviews that are resource-intensive and may incompletely capture spontaneous, contextual experience. Scalable methods to characterize caregiver experience from real-world narratives are lacking. Objective To …

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Inflammatory bowel disease outcomes among hospitalized homeless individuals

Tushar Mungle, Vivek Charu, Philip N. Okafor

BACKGROUND: There is a paucity of outcome data among homeless individuals with inflammatory bowel diseases (IBD). We reported rates of homelessness among inpatients with Crohn's disease (CD) and ulcerative colitis (UC) and examined inpatient outcomes. METHODS: The Healthcare Utilization Project (HCUP) State …

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Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability

Hannah Lee, Rebecca Handler, Tushar Mungle, Tina Hernandez-Boussard

BACKGROUND: Generative artificial intelligence (AI) chatbots built on large language models are rapidly entering mental-health care, offering human-like support without meeting evidentiary standards for safety or effectiveness. OBJECTIVE: To examine the risks, and outline a governance framework capable of supporting safe, accountable, …

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Beyond CLIP: Knowledge-Enhanced Multimodal Transformers for Cross-Modal Alignment in Diabetic Retinopathy Diagnosis

Argha Kamal Samanta, Harshika Goyal, Vasudha Joshi, Tushar Mungle et autres

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, demanding accurate automated diagnostic systems. While general-domain vision-language models like Contrastive Language-Image Pre-Training (CLIP) perform well on natural image tasks, they struggle in medical domain applications, particularly in cross-modal retrieval for …

Beyond CLIP: Knowledge-Enhanced Multimodal Transformers for Cross-Modal Alignment in Diabetic Retinopathy Diagnosis

Argha Kamal Samanta, Harshika Goyal, Vasudha Joshi, Tushar Mungle et autres

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide, demanding accurate automated diagnostic systems. While general-domain vision-language models like Contrastive Language-Image Pre-Training (CLIP) perform well on natural image tasks, they struggle in medical domain applications, particularly in cross-modal retrieval for …

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To Evaluate the Efficacy of Zero-Shot Prompting Using Large Language Models in the Extraction of Microbial Keratitis Descriptors

Lokeshwari Aruljyothi, Tushar Mungle, Maria A. Woodward, Rudy M.M.A. Nuijts et autres

PURPOSE: To extract microbial keratitis (MK) descriptors from clinician notes in electronic health records using Large Language Models (LLMs) with a zero-shot prompting approach and compare the descriptors with those identified by expert human annotators. METHODS: Two hundred fifteen patients with culture-proven …

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Ensemble learning to enhance accurate identification of patients with glaucoma using electronic health records

Tushar Mungle, Behzad Naderalvojoud, Chris Andrews, Hong Su An et autres

Abstract Objectives Existing ophthalmology studies for clinical phenotypes identification in real-world datasets (RWD) rely exclusively on structured data elements (SDE). We evaluated the performance, generalizability, and fairness of multimodal ensemble models that integrate real-world SDE and free-text data compared to SDE-only models …

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Enhanced Phenotype Identification of Common Ocular Diseases in Real-World Datasets

Joshua D. Stein, Hyonggin An, Chris Andrews, Suzann Pershing et autres

Objective: For studies using real-world data, accurately identifying patients with phenotypes of interest is challenging. To identify cohorts of interest, most studies exclusively use the International Classification of Diseases (ICD) billing codes, which can be limiting. We developed a method to accurately …

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Analysis toolkit for evaluation of drug titration practice in acute lymphoblastic leukemia maintenance

Tushar Mungle, Ananya Mahadevan, Jayanta Mukhopadhyay, Sangeeta Bhattacharya et autres

Objective: During the 2-year maintenance treatment phase (MT) of acute lymphoblastic leukemia (ALL), personalized patient-specified titration of oral antimetabolite drug doses is required to ensure maximum tolerated systemic drug exposure. Drug titration is difficult to implement in practice and insufficient systemic drug …

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Sa1024 THE IMPACT OF HOMELESSNESS ON ENDOSCOPY UTILIZATION, SURGICAL TREATMENT, AND POST-OPERATIVE COMPLICATIONS AMONG HOSPITALIZED PATIENTS WITH ULCERATIVE COLITIS

Chukwunonso Ezeani, Tushar Mungle, Vivek Charu, Philip N. Okafor

A118 THE IMPACT OF HOMELESSNESS ON ENDOSCOPY UTILIZATION, SURGICAL TREATMENT, AND POST-OPERATIVE COMPLICATIONS AMONG HOSPITALIZED PATIENTS WITH CROHN’S DISEASE

Michael Youssef, Tushar Mungle, Vivek Charu, Philip N. Okafor

Abstract Background Homelessness is an important public health issue with a prevalence around 4.2% in the United States. Homelessness has been associated with lack of access to healthcare services, poor quality of care, and negative health outcomes. However, there is limited data …

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