Trisha Das

Pritom Saha Akash, Trisha Das, Kevin Chen-Chuan Chang

Topic models are popular statistical tools for detecting latent semantic topics in a text corpus. They have been utilized in various applications across different fields. However, traditional topic models have some limitations, including insensitivity to user guidance, sensitivity to the amount and quality of data, and the inability to adapt learned topics from one corpus to another. To address these challenges, this paper proposes a neural topic model, TopicAdapt, that can adapt relevant topics from a related source corpus and also discover new topics in a target corpus that are absent in the source corpus. The proposed model offers a promising approach to improve topic modeling performance in practical scenarios. Experiments over multiple datasets from diverse domains show the superiority of the proposed model against the state-of-the-art topic models.

Trisha Das, Zifeng Wang, Afrah Shafquat et al.

Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging due to privacy concerns and federal regulations. To help mitigate privacy concerns, researchers have proposed methods for generating synthetic patient data. However, existing approaches for generating synthetic clinical trial data disregard the usage requirements of these data, including maintaining specific properties of clinical outcomes, and only use post hoc assessments that are not coupled with the data generation process. In this paper, we propose SynRL which leverages reinforcement learning to improve the performance of patient data generators by customizing the generated data to meet the user-specified requirements for synthetic data outcomes and endpoints. Our method includes a data value critic function to evaluate the quality of the generated data and uses reinforcement learning to align the data generator with the users' needs based on the critic's feedback. We performed experiments on four clinical trial datasets and demonstrated the advantages of SynRL in improving the quality of the generated synthetic data while keeping the privacy risks low. We also show that SynRL can be utilized as a general framework that can customize data generation of multiple types of synthetic data generators. Our code is available at https://anonymous.4open.science/r/SynRL-DB0F/.

Chufan Gao, Jathurshan Pradeepkumar, Trisha Das et al.

Background The cost of drug discovery and development is substantial, with clinical trial outcomes playing a critical role in regulatory approval and patient care. However, access to large-scale, high-quality clinical trial outcome data remains limited, hindering advancements in predictive modeling and evidence-based decision-making. Methods We present the Clinical Trial Outcome (CTO) benchmark, a fully reproducible, large-scale repository encompassing approximately 125,000 drug and biologics trials. CTO integrates large language model (LLM) interpretations of publications, trial phase progression tracking, sentiment analysis from news sources, stock price movements of trial sponsors, and additional trial-related metrics. Furthermore, we manually annotated a dataset of clinical trials conducted between 2020 and 2024 to enhance the quality and reliability of outcome labels. Results The trial outcome labels in the CTO benchmark agree strongly with expert annotations, achieving an F1 score of 94 for Phase 3 trials and 91 across all phases. Additionally, benchmarking standard machine learning models on our manually annotated dataset revealed distribution shifts in recent trials, underscoring the necessity of continuously updated labeling approaches. Conclusions By analyzing CTO's performance on recent clinical trials, we demonstrate the ongoing need for high-quality, up-to-date trial outcome labels. We publicly release the CTO knowledge base and annotated labels at https://chufangao.github.io/CTOD, with regular updates to support research on clinical trial outcomes and inform data-driven improvements in drug development.