Towards Scalable and Cross-Lingual Specialist Language Models for Oncology

• URL: http://arxiv.org/abs/2503.08323v1
• Date: Tue, 11 Mar 2025 11:34:57 GMT
• Title: Towards Scalable and Cross-Lingual Specialist Language Models for Oncology
• Authors: Morteza Rohanian, Tarun Mehra, Nicola Miglino, Farhad Nooralahzadeh, Michael Krauthammer, Andreas Wicki,
• Abstract summary: General-purpose large models (LLMs) struggle with challenges such as clinical terminology, context-dependent interpretations, and multi-modal data integration.<n>We develop an oncology-specialized, efficient, and adaptable NLP framework that combines instruction tuning, retrieval-augmented generation (RAG), and graph-based knowledge integration.
• Score: 4.824906329042275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinical oncology generates vast, unstructured data that often contain inconsistencies, missing information, and ambiguities, making it difficult to extract reliable insights for data-driven decision-making. General-purpose large language models (LLMs) struggle with these challenges due to their lack of domain-specific reasoning, including specialized clinical terminology, context-dependent interpretations, and multi-modal data integration. We address these issues with an oncology-specialized, efficient, and adaptable NLP framework that combines instruction tuning, retrieval-augmented generation (RAG), and graph-based knowledge integration. Our lightweight models prove effective at oncology-specific tasks, such as named entity recognition (e.g., identifying cancer diagnoses), entity linking (e.g., linking entities to standardized ontologies), TNM staging, document classification (e.g., cancer subtype classification from pathology reports), and treatment response prediction. Our framework emphasizes adaptability and resource efficiency. We include minimal German instructions, collected at the University Hospital Zurich (USZ), to test whether small amounts of non-English language data can effectively transfer knowledge across languages. This approach mirrors our motivation for lightweight models, which balance strong performance with reduced computational costs, making them suitable for resource-limited healthcare settings. We validated our models on oncology datasets, demonstrating strong results in named entity recognition, relation extraction, and document classification.

Related papers

• Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images [7.048241543461529]
  We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification. We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings. A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
  arXiv  Detail & Related papers  (2025-03-13T12:18:37Z)
• Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation [2.9921619703037274]
  We propose a retrieval augmented generation (RAG) framework backed by a large language model (LLM) to correct the output of a smaller model for the linguistic task of morphological glossing. We leverage linguistic information to make up for the lack of data and trainable parameters, while allowing for inputs from written descriptive grammars interpreted and distilled through an LLM. We show that a compact, RAG-supported model is highly effective in data-scarce settings, achieving a new state-of-the-art for this task and our target languages.
  arXiv  Detail & Related papers  (2024-10-01T04:20:14Z)
• Transfer Learning with Clinical Concept Embeddings from Large Language Models [4.838020198075334]
  Large Language Models (LLMs) show significant potential of capturing the semantic meaning of clinical concepts. This study analyzed electronic health records from two large healthcare systems to assess the impact of semantic embeddings on local, shared, and transfer learning models.
  arXiv  Detail & Related papers  (2024-09-20T20:50:55Z)
• Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
  We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR) We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models. We use the resulting dataset to investigate the human decision-making process in NL-DAR.
  arXiv  Detail & Related papers  (2024-09-09T06:55:37Z)
• A Hybrid Framework with Large Language Models for Rare Disease Phenotyping [4.550497164299771]
  Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary.
  arXiv  Detail & Related papers  (2024-05-16T20:59:28Z)
• UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognition [4.865221751784403]
  This work contributes a data-centric paradigm for enriching the language representations of biomedical transformer-encoder LMs by extracting text sequences from the UMLS. Preliminary results from experiments in the extension of pre-trained LMs as well as training from scratch show that this framework improves downstream performance on multiple biomedical and clinical Named Entity Recognition (NER) tasks.
  arXiv  Detail & Related papers  (2023-07-20T18:08:34Z)
• Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models [59.89454513692417]
  Tabular data is often hidden in text, particularly in medical diagnostic reports. We propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM. We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics.
  arXiv  Detail & Related papers  (2023-06-08T09:12:28Z)
• PathologyBERT -- Pre-trained Vs. A New Transformer Language Model for Pathology Domain [2.3628956573813498]
  Successful text mining of a large pathology database can play a critical role to advance 'big data' cancer research. No pathology-specific language space exist to support the rapid data-mining development in pathology space. PathologyBERT is a pre-trained masked language model which was trained on 347,173 histopathology specimen reports.
  arXiv  Detail & Related papers  (2022-05-13T20:42:07Z)
• A Meta-embedding-based Ensemble Approach for ICD Coding Prediction [64.42386426730695]
  International Classification of Diseases (ICD) are the de facto codes used globally for clinical coding. These codes enable healthcare providers to claim reimbursement and facilitate efficient storage and retrieval of diagnostic information. Our proposed approach enhances the performance of neural models by effectively training word vectors using routine medical data as well as external knowledge from scientific articles.
  arXiv  Detail & Related papers  (2021-02-26T17:49:58Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.