arXiv subjects

Cesar Caraballo

Publications and source records attributed to Cesar Caraballo.

3 recordsLinked to original sources

arXiv2026

HealMed: Multilingual Evaluation of Large Language Models in Medicine

We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine. HealMed contains 1,000 examples in each of nine languages, drawn from nine datasets and covering three task formats: MCQA, NLI and open-ended QA. The benchmark was developed over two years by 23 physicians and medical experts based across nine countries and regions. Each translation was evaluated and revised by two experts fluent in English and the corresponding target language. On HealMed, performance declined most in low-resource languages, although the size of the gap varied markedly across languages and models. The strongest proprietary models were the most stable across languages, whereas many open-source and medically specialized models showed larger and less consistent gaps. Medical specialization alone did not ensure multilingual robustness. Furthermore, expert revision could either raise or lower measured performance, indicating that translation quality materially affects cross-language evaluation results.

cs.CL↗

arXiv2020

Learning to Ask Medical Questions using Reinforcement Learning

We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection. Given a masked vector of input features, a reinforcement learning agent iteratively selects certain features to be unmasked, and uses them to predict an outcome when it is sufficiently confident. The algorithm makes use of a novel environment setting, corresponding to a non-stationary Markov Decision Process. A key component of our approach is a guesser network, trained to predict the outcome from the selected features and parametrizing the reward function. Applying our method to a national survey dataset, we show that it not only outperforms strong baselines when requiring the prediction to be made based on a small number of input features, but is also highly more interpretable. Our code is publicly available at \url{https://github.com/ushaham/adaptiveFS}.

cs.LG↗

arXiv2019

A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation

Automated analysis of clinical notes is attracting increasing attention. However, there has not been much work on medical term abbreviation disambiguation. Such abbreviations are abundant, and highly ambiguous, in clinical documents. One of the main obstacles is the lack of large scale, balance labeled data sets. To address the issue, we propose a few-shot learning approach to take advantage of limited labeled data. Specifically, a neural topic-attention model is applied to learn improved contextualized sentence representations for medical term abbreviation disambiguation. Another vital issue is that the existing scarce annotations are noisy and missing. We re-examine and correct an existing dataset for training and collect a test set to evaluate the models fairly especially for rare senses. We train our model on the training set which contains 30 abbreviation terms as categories (on average, 479 samples and 3.24 classes in each term) selected from a public abbreviation disambiguation dataset, and then test on a manually-created balanced dataset (each class in each term has 15 samples). We show that enhancing the sentence representation with topic information improves the performance on small-scale unbalanced training datasets by a large margin, compared to a number of baseline models.

cs.CL↗