Preserving the knowledge of long clinical texts using aggregated ensembles of large language models

• URL: http://arxiv.org/abs/2311.01571v1
• Date: Thu, 2 Nov 2023 19:50:02 GMT
• Title: Preserving the knowledge of long clinical texts using aggregated ensembles of large language models
• Authors: Mohammad Junayed Hasan, Suhra Noor and Mohammad Ashrafuzzaman Khan
• Abstract summary: Clinical texts contain rich and valuable information that can be used for various clinical outcome prediction tasks. Applying large language models, such as BERT-based models, to clinical texts poses two major challenges. This paper proposes a novel method to preserve the knowledge of long clinical texts using aggregated ensembles of large language models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clinical texts, such as admission notes, discharge summaries, and progress notes, contain rich and valuable information that can be used for various clinical outcome prediction tasks. However, applying large language models, such as BERT-based models, to clinical texts poses two major challenges: the limitation of input length and the diversity of data sources. This paper proposes a novel method to preserve the knowledge of long clinical texts using aggregated ensembles of large language models. Unlike previous studies which use model ensembling or text aggregation methods separately, we combine ensemble learning with text aggregation and train multiple large language models on two clinical outcome tasks: mortality prediction and length of stay prediction. We show that our method can achieve better results than baselines, ensembling, and aggregation individually, and can improve the performance of large language models while handling long inputs and diverse datasets. We conduct extensive experiments on the admission notes from the MIMIC-III clinical database by combining multiple unstructured and high-dimensional datasets, demonstrating our method's effectiveness and superiority over existing approaches. We also provide a comprehensive analysis and discussion of our results, highlighting our method's applications and limitations for future research in the domain of clinical healthcare. The results and analysis of this study is supportive of our method assisting in clinical healthcare systems by enabling clinical decision-making with robust performance overcoming the challenges of long text inputs and varied datasets.

Related papers

• MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis [6.30440420617113]
  We introduce MedTsLLM, a general multimodal large language model (LLM) framework that integrates time series data and rich contextual information in the form of text to analyze physiological signals. We perform three tasks with clinical relevance: semantic segmentation, boundary detection, and anomaly detection in time series. Our model outperforms state-of-the-art baselines, including deep learning models, other LLMs, and clinical methods across multiple medical domains.
  arXiv  Detail & Related papers  (2024-08-14T18:57:05Z)
• Multimodal Clinical Trial Outcome Prediction with Large Language Models [30.201189349890267]
  We propose a multimodal mixture-of-experts (LIFTED) approach for clinical trial outcome prediction. LIFTED unifies different modality data by transforming them into natural language descriptions. Then, LIFTED constructs unified noise-resilient encoders to extract information from modal-specific language descriptions.
  arXiv  Detail & Related papers  (2024-02-09T16:18:38Z)
• Making the Most Out of the Limited Context Length: Predictive Power Varies with Clinical Note Type and Note Section [70.37720062263176]
  We propose a framework to analyze the sections with high predictive power. Using MIMIC-III, we show that: 1) predictive power distribution is different between nursing notes and discharge notes and 2) combining different types of notes could improve performance when the context length is large.
  arXiv  Detail & Related papers  (2023-07-13T20:04:05Z)
• ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation [5.690250818139763]
  Large language models have exhibited exceptional performance on various Natural Language Processing (NLP) tasks. Despite these advances, their effectiveness in medical applications is limited, due to challenges such as factual inaccuracies, reasoning abilities, and lack grounding in real-world experience. We present ClinicalGPT, a language model explicitly designed and optimized for clinical scenarios.
  arXiv  Detail & Related papers  (2023-06-16T16:56:32Z)
• Leveraging text data for causal inference using electronic health records [1.4182510510164876]
  This paper presents a unified framework for leveraging text data to support causal inference with electronic health data. We show how incorporating text data in a traditional matching analysis can help strengthen the validity of an estimated treatment effect. We believe these methods have the potential to expand the scope of secondary analysis of clinical data to domains where structured EHR data is limited.
  arXiv  Detail & Related papers  (2023-06-09T16:06:02Z)
• 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)
• Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse [53.797797404164946]
  The study highlights the difficulties faced in sharing tools and resources in this domain. We annotated a corpus of clinical documents according to 12 types of identifying entities. We build a hybrid system, merging the results of a deep learning model as well as manual rules.
  arXiv  Detail & Related papers  (2023-03-23T17:17:46Z)
• A Multi-View Joint Learning Framework for Embedding Clinical Codes and Text Using Graph Neural Networks [23.06795121693656]
  We propose a framework that learns from codes and text to combine the availability and forward-looking nature of text and better performance of ICD codes. Our approach uses a Graph Neural Network (GNN) to process ICD codes, and Bi-LSTM to process text. In experiments using planned surgical procedure text, our model outperforms BERT models fine-tuned to clinical data.
  arXiv  Detail & Related papers  (2023-01-27T09:19:03Z)
• Towards more patient friendly clinical notes through language models and ontologies [57.51898902864543]
  We present a novel approach to automated medical text based on word simplification and language modelling. We use a new dataset pairs of publicly available medical sentences and a version of them simplified by clinicians. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning.
  arXiv  Detail & Related papers  (2021-12-23T16:11:19Z)
• Self-supervised Answer Retrieval on Clinical Notes [68.87777592015402]
  We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. We report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages.
  arXiv  Detail & Related papers  (2021-08-02T10:42:52Z)
• Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation [48.87254340298189]
  We construct a new dataset named MedLane to support the development and evaluation of automated clinical language simplification approaches. We propose a new model called DECLARE that follows the human annotation procedure and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-12-04T06:09:02Z)

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.