MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Prediction
Clinical trials are the gold standard for assessing the effectiveness and safety of drugs for treating diseases. Given the vast design space of drug molecules, elevated financial cost, and multi-year timeline of these trials, research on clinical trial outcome prediction has gained immense traction. Accurate predictions must leverage data of diverse modes such as drug molecules, target diseases, and eligibility criteria to infer successes and failures. Previous Deep Learning approaches for this task, such as HINT, often require wet lab data from synthesized molecules and/or rely on prior knowledge to encode interactions as part of the model architecture. To address these limitations, we propose a light-weight attention-based model, MEXA-CTP, to integrate readily-available multi-modal data and generate effective representations via specialized modules dubbed "mode experts", while avoiding human biases in model design. We optimize MEXA-CTP with the Cauchy loss to capture relevant interactions across modes. Our experiments on the Trial Outcome Prediction (TOP) benchmark demonstrate that MEXA-CTP improves upon existing approaches by, respectively, up to 11.3% in F1 score, 12.2% in PR-AUC, and 2.5% in ROC-AUC, compared to HINT. Ablation studies are provided to quantify the effectiveness of each component in our proposed method.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive da…
BenchmarkingMultiple-choiceQuestion AnsweringBenchmarking Large Language Models on CMExam - A comprehensive Chinese Medical Exam Dataset
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive da…
MEXA: Multilingual Evaluation of English-Centric LLMs via Cross-Lingual Alignment
English-centric large language models (LLMs) often show strong multilingual capabilities. However, the multilingual performance of these models remains unclear and is not thoroughly evaluated for many languages. Most ben…
ARCBelebeleMMLUCLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs
Many existing models for clinical trial outcome prediction are optimized using task-specific loss functions on trial phase-specific data. While this scheme may boost prediction for common diseases and drugs, it can hinde…
Large Language Modelparameter-efficient fine-tuningPredictionSparseDoctor: Towards Efficient Chat Doctor with Mixture of Experts Enhanced Large Language Models
Large language models (LLMs) have achieved great success in medical question answering and clinical decision-making, promoting the efficiency and popularization of the personalized virtual doctor in society. However, the…
Reinforcement LearningContrastive LearningQuestion Answering