تقرير
Dialogue-Contextualized Re-ranking for Medical History-Taking
| العنوان: | Dialogue-Contextualized Re-ranking for Medical History-Taking |
|---|---|
| المؤلفون: | Zhu, Jian, Valmianski, Ilya, Kannan, Anitha |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Information Retrieval |
| الوصف: | AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extremely varied, machine learning models require a significant amount of data to train. To overcome this challenge, existing systems are developed using indirect data or expert knowledge. This leads to a training-inference gap as models are trained on different kinds of data than what they observe at inference time. In this work, we present a two-stage re-ranking approach that helps close the training-inference gap by re-ranking the first-stage question candidates using a dialogue-contextualized model. For this, we propose a new model, global re-ranker, which cross-encodes the dialogue with all questions simultaneously, and compare it with several existing neural baselines. We test both transformer and S4-based language model backbones. We find that relative to the expert system, the best performance is achieved by our proposed global re-ranker with a transformer backbone, resulting in a 30% higher normalized discount cumulative gain (nDCG) and a 77% higher mean average precision (mAP). Comment: Code and pre-trained S4 checkpoints will be available after publication |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2304.01974 |
| رقم الأكسشن: | edsarx.2304.01974 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |