Evaluating Computational Accuracy of Large Language Models in Numerical Reasoning Tasks for Healthcare Applications

• URL: http://arxiv.org/abs/2501.13936v1
• Date: Tue, 14 Jan 2025 04:29:43 GMT
• Title: Evaluating Computational Accuracy of Large Language Models in Numerical Reasoning Tasks for Healthcare Applications
• Authors: Arjun R. Malghan,
• Abstract summary: Large Language Models (LLMs) have emerged as transformative tools in the healthcare sector. Their proficiency in numerical reasoning, particularly in high-stakes domains like in clinical applications, remains underexplored. This study investigates the computational accuracy of LLMs in numerical reasoning tasks within healthcare contexts.
• Score: 0.0
• License:
• Abstract: Large Language Models (LLMs) have emerged as transformative tools in the healthcare sector, demonstrating remarkable capabilities in natural language understanding and generation. However, their proficiency in numerical reasoning, particularly in high-stakes domains like in clinical applications, remains underexplored. Numerical reasoning is critical in healthcare applications, influencing patient outcomes, treatment planning, and resource allocation. This study investigates the computational accuracy of LLMs in numerical reasoning tasks within healthcare contexts. Using a curated dataset of 1,000 numerical problems, encompassing real-world scenarios such as dosage calculations and lab result interpretations, the performance of a refined LLM based on the GPT-3 architecture was evaluated. The methodology includes prompt engineering, integration of fact-checking pipelines, and application of regularization techniques to enhance model accuracy and generalization. Key metrics such as precision, recall, and F1-score were utilized to assess the model's efficacy. The results indicate an overall accuracy of 84.10%, with improved performance in straightforward numerical tasks and challenges in multi-step reasoning. The integration of a fact-checking pipeline improved accuracy by 11%, underscoring the importance of validation mechanisms. This research highlights the potential of LLMs in healthcare numerical reasoning and identifies avenues for further refinement to support critical decision-making in clinical environments. The findings aim to contribute to the development of reliable, interpretable, and contextually relevant AI tools for healthcare.

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)
• Leveraging Large Language Models through Natural Language Processing to provide interpretable Machine Learning predictions of mental deterioration in real time [5.635300481123079]
  Based on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. To this end, Artificial Intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. We contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.
  arXiv  Detail & Related papers  (2024-09-05T09:27:05Z)
• SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research [45.2233252981348]
  Large Language Models have shown promising results in their ability to encode general medical knowledge. We test the ability of state-of-the-art LLMs to leverage their internal knowledge and reasoning for epilepsy diagnosis.
  arXiv  Detail & Related papers  (2024-07-03T11:02:12Z)
• A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry [2.1717945745027425]
  Large Language Models (LLMs) have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation. This comprehensive survey delineates the extensive application and requisite evaluation of LLMs within healthcare. Our survey is structured to provide an in-depth analysis of LLM applications across clinical settings, medical text data processing, research, education, and public health awareness.
  arXiv  Detail & Related papers  (2024-04-24T09:55:24Z)
• Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
  Large language models (LLMs) are used to automate decision-making tasks. In this paper, we evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts.
  arXiv  Detail & Related papers  (2024-04-08T14:15:56Z)
• Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models [29.05425041393475]
  Generative Large Language Models (LLMs) hold significant promise in healthcare. This study assessed the potential of LLMs to function as autonomous agents in a simulated tertiary care medical center.
  arXiv  Detail & Related papers  (2024-01-05T15:09:57Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• 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)
• FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction [3.9026461169566673]
  Large-scale electronic health records provide machine learning models with an abundance of clinical text and vital sign data. Despite the emergence of advanced Natural Language Processing (NLP) algorithms for clinical note analysis, the complex textual structure and noise present in raw clinical data have posed significant challenges. We propose FINEEHR, a system that utilizes two representation learning techniques, namely metric learning and fine-tuning, to refine clinical note embeddings.
  arXiv  Detail & Related papers  (2023-04-24T02:42:52Z)
• 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)
• 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.