Clinical Risk Prediction Using Language Models: Benefits And Considerations

• URL: http://arxiv.org/abs/2312.03742v1
• Date: Wed, 29 Nov 2023 04:32:19 GMT
• Title: Clinical Risk Prediction Using Language Models: Benefits And Considerations
• Authors: Angeela Acharya, Sulabh Shrestha, Anyi Chen, Joseph Conte, Sanja Avramovic, Siddhartha Sikdar, Antonios Anastasopoulos, Sanmay Das
• Abstract summary: This study focuses on using structured descriptions within vocabularies to make predictions exclusively based on that information. We find that employing LMs to represent structured EHRs leads to improved or at least comparable performance in diverse risk prediction tasks.
• Score: 23.781690889237794
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The utilization of Electronic Health Records (EHRs) for clinical risk prediction is on the rise. However, strict privacy regulations limit access to comprehensive health records, making it challenging to apply standard machine learning algorithms in practical real-world scenarios. Previous research has addressed this data limitation by incorporating medical ontologies and employing transfer learning methods. In this study, we investigate the potential of leveraging language models (LMs) as a means to incorporate supplementary domain knowledge for improving the performance of various EHR-based risk prediction tasks. Unlike applying LMs to unstructured EHR data such as clinical notes, this study focuses on using textual descriptions within structured EHR to make predictions exclusively based on that information. We extensively compare against previous approaches across various data types and sizes. We find that employing LMs to represent structured EHRs, such as diagnostic histories, leads to improved or at least comparable performance in diverse risk prediction tasks. Furthermore, LM-based approaches offer numerous advantages, including few-shot learning, the capability to handle previously unseen medical concepts, and adaptability to various medical vocabularies. Nevertheless, we underscore, through various experiments, the importance of being cautious when employing such models, as concerns regarding the reliability of LMs persist.

Related papers

• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• IntelliCare: Improving Healthcare Analysis with Variance-Controlled Patient-Level Knowledge from Large Language Models [14.709233593021281]
  The integration of external knowledge from Large Language Models (LLMs) presents a promising avenue for improving healthcare predictions. We propose IntelliCare, a novel framework that leverages LLMs to provide high-quality patient-level external knowledge. IntelliCare identifies patient cohorts and employs task-relevant statistical information to augment LLM understanding and generation.
  arXiv  Detail & Related papers  (2024-08-23T13:56:00Z)
• LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction [38.11497959553319]
  We investigate the feasibility of applying Large Language Models to convert structured patient visit data into natural language narratives. We evaluate the zero-shot and few-shot performance of LLMs using various EHR-prediction-oriented prompting strategies. Our results demonstrate that with the proposed approach, LLMs can achieve decent few-shot performance compared to traditional supervised learning methods in EHR-based disease predictions.
  arXiv  Detail & Related papers  (2024-03-19T18:10:13Z)
• Interpretable Machine Learning for Survival Analysis [3.618561939712435]
  interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. Lack of readily available IML methods may have deterred medical practitioners and policy makers in public health from leveraging the full potential of machine learning. We present a review of the limited existing amount of work on IML methods for survival analysis within the context of the general IML taxonomy.
  arXiv  Detail & Related papers  (2024-03-15T12:38:00Z)
• Prompting Large Language Models for Zero-Shot Clinical Prediction with Structured Longitudinal Electronic Health Record Data [7.815738943706123]
  Large Language Models (LLMs) are traditionally tailored for natural language processing. This research investigates the adaptability of LLMs, like GPT-4, to EHR data. In response to the longitudinal, sparse, and knowledge-infused nature of EHR data, our prompting approach involves taking into account specific characteristics.
  arXiv  Detail & Related papers  (2024-01-25T20:14:50Z)
• MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation [58.93221876843639]
  This paper introduces a novel, end-to-end diffusion-based risk prediction model, named MedDiffusion. It enhances risk prediction performance by creating synthetic patient data during training to enlarge sample space. It discerns hidden relationships between patient visits using a step-wise attention mechanism, enabling the model to automatically retain the most vital information for generating high-quality data.
  arXiv  Detail & Related papers  (2023-10-04T01:36:30Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Time Associated Meta Learning for Clinical Prediction [78.99422473394029]
  We propose a novel time associated meta learning (TAML) method to make effective predictions at multiple future time points. To address the sparsity problem after task splitting, TAML employs a temporal information sharing strategy to augment the number of positive samples. We demonstrate the effectiveness of TAML on multiple clinical datasets, where it consistently outperforms a range of strong baselines.
  arXiv  Detail & Related papers  (2023-03-05T03:54:54Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records [8.854691034104071]
  Disease risk prediction has attracted increasing attention in the field of modern healthcare. One challenge of applying AI models for risk prediction lies in generating interpretable evidence. We propose the method of jointly embedding words and labels.
  arXiv  Detail & Related papers  (2022-01-18T07:21:20Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)

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.