Cross-institution text mining to uncover clinical associations: a case study relating social factors and code status in intensive care medicine

• URL: http://arxiv.org/abs/2301.06570v1
• Date: Mon, 16 Jan 2023 19:04:59 GMT
• Title: Cross-institution text mining to uncover clinical associations: a case study relating social factors and code status in intensive care medicine
• Abstract summary: We developed text mining models on different combinations of within-institution and external-institution data. We assessed the associations between social factors and having a do-not-resuscitate/intubate code.
• Score: 0.27998963147546146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: Text mining of clinical notes embedded in electronic medical records is increasingly used to extract patient characteristics otherwise not or only partly available, to assess their association with relevant health outcomes. As manual data labeling needed to develop text mining models is resource intensive, we investigated whether off-the-shelf text mining models developed at external institutions, together with limited within-institution labeled data, could be used to reliably extract study variables to conduct association studies. Materials and Methods: We developed multiple text mining models on different combinations of within-institution and external-institution data to extract social factors from discharge reports of intensive care patients. Subsequently, we assessed the associations between social factors and having a do-not-resuscitate/intubate code. Results: Important differences were found between associations based on manually labeled data compared to text-mined social factors in three out of five cases. Adopting external-institution text mining models using manually labeled within-institution data resulted in models with higher F1-scores, but not in meaningfully different associations. Discussion: While text mining facilitated scaling analyses to larger samples leading to discovering a larger number of associations, the estimates may be unreliable. Confirmation is needed with better text mining models, ideally on a larger manually labeled dataset. Conclusion: The currently used text mining models were not sufficiently accurate to be used reliably in an association study. Model adaptation using within-institution data did not improve the estimates. Further research is needed to set conditions for reliable use of text mining in medical research.

Related papers

• WhatsApp Vaccine Discourse (WhaVax): An Expert-Annotated Dataset and Benchmark for Health Misinformation Detection [0.0]
  We introduce WhaVax, a new expert-annotated dataset of vaccine-related WhatsApp messages collected from large Brazilian public groups spanning multiple pandemic years.<n>We provide a detailed characterization of WhatsApp misinformation, revealing distinctive linguistic, structural, lexical, temporal, and group-level patterns.<n>We also benchmark classical models, fine-tuned Small Language Models, and zero- or few-shot Large Language Models under realistic data-scarcity constraints.
  arXiv  Detail & Related papers  (2026-03-25T14:54:09Z)
• HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generation [89.3260120072177]
  We propose a novel Historical-Constrained Large Language Models (HC-LLM) framework for Radiology report generation. Our approach extracts both time-shared and time-specific features from longitudinal chest X-rays and diagnostic reports to capture disease progression. Notably, our approach performs well even without historical data during testing and can be easily adapted to other multimodal large models.
  arXiv  Detail & Related papers  (2024-12-15T06:04:16Z)
• 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)
• Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs [0.0]
  Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. Due to privacy and regulatory reasons, it is also problematic to aggregate all data at a trusted third party. Federated learning is a promising solution because it enables decentralized, collaborative machine learning without exchanging raw data.
  arXiv  Detail & Related papers  (2024-02-22T13:21:26Z)
• Data Augmentations for Improved (Large) Language Model Generalization [17.75815547057179]
  We propose to use counterfactual data augmentation, guided by knowledge of the causal structure of the data, to simulate interventions on spurious features. We show that this strategy is appropriate in prediction problems where the label is spuriously correlated with an attribute.
  arXiv  Detail & Related papers  (2023-10-19T14:59:25Z)
• Sample Size in Natural Language Processing within Healthcare Research [0.14865681381012494]
  Lack of sufficient corpora of previously collected data can be a limiting factor when determining sample sizes for new studies. This paper tries to address the issue by making recommendations on sample sizes for text classification tasks in the healthcare domain.
  arXiv  Detail & Related papers  (2023-09-05T13:42:43Z)
• Does Synthetic Data Generation of LLMs Help Clinical Text Mining? [51.205078179427645]
  We investigate the potential of OpenAI's ChatGPT to aid in clinical text mining. We propose a new training paradigm that involves generating a vast quantity of high-quality synthetic data. Our method has resulted in significant improvements in the performance of downstream tasks.
  arXiv  Detail & Related papers  (2023-03-08T03:56:31Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• Equivariance Allows Handling Multiple Nuisance Variables When Analyzing Pooled Neuroimaging Datasets [53.34152466646884]
  In this paper, we show how bringing recent results on equivariant representation learning instantiated on structured spaces together with simple use of classical results on causal inference provides an effective practical solution. We demonstrate how our model allows dealing with more than one nuisance variable under some assumptions and can enable analysis of pooled scientific datasets in scenarios that would otherwise entail removing a large portion of the samples.
  arXiv  Detail & Related papers  (2022-03-29T04:54:06Z)
• SAIS: Supervising and Augmenting Intermediate Steps for Document-Level Relation Extraction [51.27558374091491]
  We propose to explicitly teach the model to capture relevant contexts and entity types by supervising and augmenting intermediate steps (SAIS) for relation extraction. Based on a broad spectrum of carefully designed tasks, our proposed SAIS method not only extracts relations of better quality due to more effective supervision, but also retrieves the corresponding supporting evidence more accurately.
  arXiv  Detail & Related papers  (2021-09-24T17:37:35Z)
• Improving Clinical Document Understanding on COVID-19 Research with Spark NLP [0.0]
  Following the global COVID-19 pandemic, the number of scientific papers studying the virus has grown massively. We present a clinical text mining system that improves on previous efforts in three ways. First, it can recognize over 100 different entity types including social determinants of health, anatomy, risk factors, and adverse events. Second, the text processing pipeline includes assertion status detection, to distinguish between clinical facts that are present, absent, conditional, or about someone other than the patient.
  arXiv  Detail & Related papers  (2020-12-07T19:17:05Z)
• Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets [56.38623317907416]
  We use Google Cloud to transcribe podcast episodes of an NPR radio show. We then build a pipeline for systematically pre-processing the text. Our model successfully identified that Omeprazole can help treat heartburn.
  arXiv  Detail & Related papers  (2020-10-22T19:52:49Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

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.