Dina Albassam

Dina Albassam, Kexin Quan, Mengke Wu et al.

YouTube is widely used for informal learning, where learners explore lectures and tutorials without a predefined curriculum. However, learning across videos remains fragmented: learners must decide what to watch, how videos relate, and how knowledge builds. Existing tools provide partial support but treat planning and learning as separate activities, lacking a persistent interaction structure that connects them. Grounded in self-regulated learning theory (SRLT), we introduce YT-Pilot, a pathway-aware learning system that operationalizes the learning pathway as a persistent, user-facing interaction structure spanning planning and learning. The pathway coordinates goal setting, planning, navigation, progress tracking, and cross-video assistance. Through a within-subjects study ($N=20$), we show that YT-Pilot significantly improves perceived goal clarity, pathway coherence, and progress tracking, while shifting interaction toward pathway-level reasoning across multiple resources.

Trisha Das, Dina Albassam, Jimeng Sun

Medical dialogue systems (MDS) enhance patient-physician communication, improve healthcare accessibility, and reduce costs. However, acquiring suitable data to train these systems poses significant challenges. Privacy concerns prevent the use of real conversations, necessitating synthetic alternatives. Synthetic dialogue generation from publicly available clinical notes offers a promising solution to this issue, providing realistic data while safeguarding privacy. Our approach, SynDial, uses a single LLM iteratively with zero-shot prompting and a feedback loop to generate and refine high-quality synthetic dialogues. The feedback consists of weighted evaluation scores for similarity and extractiveness. The iterative process ensures dialogues meet predefined thresholds, achieving superior extractiveness as a result of the feedback loop. Additionally, evaluation shows that the generated dialogues excel in factuality metric compared to the baselines and has comparable diversity scores with GPT4.

Dina Albassam, Adam Cross, Chengxiang Zhai

Electronic Health Records (EHRs) often lack explicit links between medications and diagnoses, making clinical decision-making and research more difficult. Even when links exist, diagnosis lists may be incomplete, especially during early patient visits. Discharge summaries tend to provide more complete information, which can help infer accurate diagnoses, especially with the help of large language models (LLMs). This study investigates whether LLMs can predict implicitly mentioned diagnoses from clinical notes and link them to corresponding medications. We address two research questions: (1) Does majority voting across diverse LLM configurations outperform the best single configuration in diagnosis prediction? (2) How sensitive is majority voting accuracy to LLM hyperparameters such as temperature, top-p, and summary length? To evaluate, we created a new dataset of 240 expert-annotated medication-diagnosis pairs from 20 MIMIC-IV notes. Using GPT-3.5 Turbo, we ran 18 prompting configurations across short and long summary lengths, generating 8568 test cases. Results show that majority voting achieved 75 percent accuracy, outperforming the best single configuration at 66 percent. No single hyperparameter setting dominated, but combining deterministic, balanced, and exploratory strategies improved performance. Shorter summaries generally led to higher accuracy.In conclusion, ensemble-style majority voting with diverse LLM configurations improves diagnosis prediction in EHRs and offers a promising method to link medications and diagnoses in clinical texts.