Using Meta-Transformers for Multimodal Clinical Decision Support and Evidence-Based Medicine
Notice bibliographique
Résumé
Abstract The advancements in computer vision and natural language processing are keys to thriving modern healthcare systems and its applications. Nonetheless, they have been researched and used as separate technical entities without integrating their predictive knowledge discovery when they are combined. Such integration will benefit every clinical/medical problem as they are inherently multimodal - they involve several distinct forms of data, such as images and text. However, the recent advancements in machine learning have brought these fields closer using the notion of meta-transformers. At the core of this synergy is building models that can process and relate information from multiple modalities where the raw input data from various modalities are mapped into a shared token space, allowing an encoder to extract high-level semantic features of the input data. Nerveless, the task of automatically identifying arguments in a clinical/medical text and finding their multimodal relationships remains challenging as it does not rely only on relevancy measures (e.g. how close that text to other modalities like an image) but also on the evidence supporting that relevancy. Relevancy based on evidence is a normal practice in medicine as every practice is an evidence-based. In this article we are experimenting with meta-transformers that can benefit evidence based predictions. In this article, we are experimenting with variety of fine tuned medical meta-transformers like PubmedCLIP, CLIPMD, BiomedCLIP-PubMedBERT and BioCLIP to see which one provide evidence-based relevant multimodal information. Our experimentation uses the TTi-Eval open-source platform to accommodate multimodal data embeddings. This platform simplifies the integration and evaluation of different meta-transformers models but also to variety of datasets for testing and fine tuning. Additionally, we are conducting experiments to test how relevant any multimodal prediction to the published medical literature especially those that are published by PubMed. Our experimentations revealed that the BiomedCLIP-PubMedBERT model provide more reliable evidence-based relevance compared to other models based on randomized samples from the ROCO V2 dataset or other multimodal datasets like MedCat. In this next stage of this research we are extending the use of the winning evidence-based multimodal learning model by adding components that enable medical practitioner to use this model to predict answers to clinical questions based on sound medical questioning protocol like PICO and based on standardized medical terminologies like UMLS.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».