Weakly-supervised diagnosis identification from Italian discharge letters

• URL: http://arxiv.org/abs/2410.15051v1
• Date: Sat, 19 Oct 2024 09:42:20 GMT
• Title: Weakly-supervised diagnosis identification from Italian discharge letters
• Authors: Vittorio Torri, Elisa Barbieri, Anna Cantarutti, Carlo Giaquinto, Francesca Ieva,
• Abstract summary: We propose a novel weakly-supervised pipeline to recognize diseases from Italian discharge letters. Our pipeline is based on a fine-tuned version of the Italian Umberto model.
• Score: 0.0
• License:
• Abstract: Objective: Recognizing diseases from discharge letters is crucial for cohort selection and epidemiological analyses, as this is the only type of data consistently produced across hospitals. This is a classic document classification problem, typically requiring supervised learning. However, manual annotation of large datasets of discharge letters is uncommon since it is extremely time-consuming. We propose a novel weakly-supervised pipeline to recognize diseases from Italian discharge letters. Methods: Our Natural Language Processing pipeline is based on a fine-tuned version of the Italian Umberto model. The pipeline extracts diagnosis-related sentences from a subset of letters and applies a two-level clustering using the embeddings generated by the fine-tuned Umberto model. These clusters are summarized and those mapped to the diseases of interest are selected as weak labels. Finally, the same BERT-based model is trained using these weak labels to detect the targeted diseases. Results: A case study related to the identification of bronchiolitis with 33'176 Italian discharge letters from 44 hospitals in the Veneto Region shows the potential of our method, with an AUC of 77.7 % and an F1-Score of 75.1 % on manually annotated labels, improving compared to other non-supervised methods and with a limited loss compared to fully supervised methods. Results are robust to the cluster selection and the identified clusters highlight the potential to recognize a variety of diseases. Conclusions: This study demonstrates the feasibility of diagnosis identification from Italian discharge letters in the absence of labelled data. Our pipeline showed strong performance and robustness, and its flexibility allows for easy adaptation to various diseases. This approach offers a scalable solution for clinical text classification, reducing the need for manual annotation while maintaining good accuracy.

Related papers

• Improving Extraction of Clinical Event Contextual Properties from Electronic Health Records: A Comparative Study [2.0884301753594334]
  This study performs a comparative analysis of various natural language models for medical text classification. BERT outperforms Bi-LSTM models by up to 28% and the baseline BERT model by up to 16% for recall of the minority classes.
  arXiv  Detail & Related papers  (2024-08-30T10:28:49Z)
• Assessing and Enhancing Large Language Models in Rare Disease Question-answering [64.32570472692187]
  We introduce a rare disease question-answering (ReDis-QA) dataset to evaluate the performance of Large Language Models (LLMs) in diagnosing rare diseases. We collected 1360 high-quality question-answer pairs within the ReDis-QA dataset, covering 205 rare diseases. We then benchmarked several open-source LLMs, revealing that diagnosing rare diseases remains a significant challenge for these models. Experiment results demonstrate that ReCOP can effectively improve the accuracy of LLMs on the ReDis-QA dataset by an average of 8%.
  arXiv  Detail & Related papers  (2024-08-15T21:09:09Z)
• 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)
• CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study [17.96401880059829]
  Large Language Models (LLMs) such as ChatGPT have achieved tremendous success in various downstream tasks. We propose to use a knowledge graph as auxiliary information to guide the LLMs in making predictions. Our few-shot learning method achieves satisfactory performance compared with fine-tuning strategies.
  arXiv  Detail & Related papers  (2023-07-21T04:43:00Z)
• WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma [51.50991881342181]
  This challenge includes 10,091 patch-level annotations and over 130 million labeled pixels. First place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919)
  arXiv  Detail & Related papers  (2022-04-13T15:27:05Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)
• Multi-class versus One-class classifier in spontaneous speech analysis oriented to Alzheimer Disease diagnosis [58.720142291102135]
  The aim of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from speech signal. The use of information about outlier and Fractal Dimension features improves the system performance.
  arXiv  Detail & Related papers  (2022-03-21T09:57:20Z)
• ACPL: Anti-curriculum Pseudo-labelling forSemi-supervised Medical Image Classification [22.5935068122522]
  We propose a new SSL algorithm, called anti-curriculum pseudo-labelling (ACPL) ACPL introduces novel techniques to select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems. Our method outperforms previous SOTA SSL methods on both datasets.
  arXiv  Detail & Related papers  (2021-11-25T05:31:52Z)
• Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification [26.864435224276964]
  We propose a novel hybrid approach to rare disease classification, featuring two key novelties. First, we adopt the unsupervised representation learning (URL) based on self-supervising contrastive loss. Second, we integrate the URL with pseudo-label supervised classification for effective self-distillation of the knowledge about the rare diseases.
  arXiv  Detail & Related papers  (2021-10-09T12:56:09Z)
• Label-Assemble: Leveraging Multiple Datasets with Partial Labels [68.46767639240564]
  "Label-Assemble" aims to unleash the full potential of partial labels from an assembly of public datasets. We discovered that learning from negative examples facilitates both computer-aided disease diagnosis and detection.
  arXiv  Detail & Related papers  (2021-09-25T02:48:17Z)
• 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.