Fethi Jarray

Sabri Boughorbel, Fethi Jarray, Abdulaziz Al Homaid et al.

Language modeling have shown impressive progress in generating compelling text with good accuracy and high semantic coherence. An interesting research direction is to augment these powerful models for specific applications using contextual information. In this work, we explore multi-modal language modeling for healthcare applications. We are interested in outcome prediction and patient triage in hospital emergency department based on text information in chief complaints and vital signs recorded at triage. We adapt Perceiver - a modality-agnostic transformer-based model that has shown promising results in several applications. Since vital-sign modality is represented in tabular format, we modified Perceiver position encoding to ensure permutation invariance. We evaluated the multi-modal language model for the task of diagnosis code prediction using MIMIC-IV ED dataset on 120K visits. In the experimental analysis, we show that mutli-modality improves the prediction performance compared with models trained solely on text or vital signs. We identified disease categories for which multi-modality leads to performance improvement and show that for these categories, vital signs have added predictive power. By analyzing the cross-attention layer, we show how multi-modality contributes to model predictions. This work gives interesting insights on the development of multi-modal language models for healthcare applications.

Sabri Boughorbel, Fethi Jarray, Abdou Kadri

Patient no-shows is a major burden for health centers leading to loss of revenue, increased waiting time and deteriorated health outcome. Developing machine learning (ML) models for the prediction of no -shows could help addressing this important issue. It is crucial to consider fair ML models for no-show prediction in order to ensure equality of opportunity in accessing healthcare services. In this wo rk, we are interested in developing deep learning models for no-show prediction based on tabular data while ensuring fairness properties. Our baseline model, TabNet, uses on attentive feature transforme rs and has shown promising results for tabular data. We propose Fair-TabNet based on representation learning that disentangles predictive from sensitive components. The model is trained to jointly min imize loss functions on no-shows and sensitive variables while ensuring that the sensitive and prediction representations are orthogonal. In the experimental analysis, we used a hospital dataset of 210, 000 appointments collected in 2019. Our preliminary results show that the proposed Fair-TabNet improves the predictive, fairness performance and convergence speed over TabNet for the task of appointment no-show prediction. The comparison with the state-of-the art models for tabular data shows promising results and could be further improved by a better tuning of hyper-parameters.

Sabri Boughorbel, Fethi Jarray, Neethu Venugopal et al.

Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.

Bilel Ben Ali, Fethi Jarray

With the growing number of textual resources available, the ability to understand them becomes critical. An essential first step in understanding these sources is the ability to identify the part of speech in each sentence. Arabic is a morphologically rich language, wich presents a challenge for part of speech tagging. In this paper, our goal is to propose, improve and implement a part of speech tagger based on a genetic alorithm. The accuracy obtained with this method is comparable to that of other probabilistic approaches.