Devika Salunke


2025

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Towards conversational assistants for health applications: using ChatGPT to generate conversations about heart failure
Anuja Tayal | Devika Salunke | Barbara Di Eugenio | Paula Allen-Meares | Eulalia Puig Abril | Olga Garcia-Bedoya | Carolyn Dickens | Andrew Boyd
Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue

We explore the potential of ChatGPT to generate conversations focused on self-care strategies for African-American patients with heart failure, a domain with limited specialized datasets. To simulate patient-health educator dialogues, we employed four prompting strategies: aspects, African American Vernacular English, Social Determinants of Health (SDOH), and SDOH-informed reasoning. Conversations were generated across key self-care aspects— food, exercise, and fluid intake—with varying turn lengths and incorporated patient-specific SDOH attributes such as age, gender, neighborhood, and socioeconomic status. Our findings show that effective prompt design is essential. While incorporating SDOH and reasoning improves dialogue quality, ChatGPT still lacks the empathy and engagement needed for meaningful healthcare communication.

2024

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A Neuro-Symbolic Approach to Monitoring Salt Content in Food
Anuja Tayal | Barbara Di Eugenio | Devika Salunke | Andrew D. Boyd | Carolyn A. Dickens | Eulalia P. Abril | Olga Garcia-Bedoya | Paula G. Allen-Meares
Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024

We propose a dialogue system that enables heart failure patients to inquire about salt content in foods and help them monitor and reduce salt intake. Addressing the lack of specific datasets for food-based salt content inquiries, we develop a template-based conversational dataset. The dataset is structured to ask clarification questions to identify food items and their salt content. Our findings indicate that while fine-tuning transformer-based models on the dataset yields limited performance, the integration of Neuro-Symbolic Rules significantly enhances the system’s performance. Our experiments show that by integrating neuro-symbolic rules, our system achieves an improvement in joint goal accuracy of over 20% across different data sizes compared to naively fine-tuning transformer-based models.

2020

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Heart Failure Education of African American and Hispanic/Latino Patients: Data Collection and Analysis
Itika Gupta | Barbara Di Eugenio | Devika Salunke | Andrew Boyd | Paula Allen-Meares | Carolyn Dickens | Olga Garcia
Proceedings of the First Workshop on Natural Language Processing for Medical Conversations

Heart failure is a global epidemic with debilitating effects. People with heart failure need to actively participate in home self-care regimens to maintain good health. However, these regimens are not as effective as they could be and are influenced by a variety of factors. Patients from minority communities like African American (AA) and Hispanic/Latino (H/L), often have poor outcomes compared to the average Caucasian population. In this paper, we lay the groundwork to develop an interactive dialogue agent that can assist AA and H/L patients in a culturally sensitive and linguistically accurate manner with their heart health care needs. This will be achieved by extracting relevant educational concepts from the interactions between health educators and patients. Thus far we have recorded and transcribed 20 such interactions. In this paper, we describe our data collection process, thematic and initiative analysis of the interactions, and outline our future steps.