Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data

Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materials and Methods: We analyzed data from 9,134 COVID-19 patients collected across four hospitals. Seven CML models, including XGBoost and random forest (RF), were trained and evaluated. The structured data was converted into text for zero-shot classification by eight LLMs, including GPT-4 and Mistral-7b. Additionally, Mistral-7b was fine-tuned using the QLoRA approach to enhance its predictive capabilities. Results: Among the CML models, XGBoost and RF achieved the highest accuracy, with F1 scores of 0.87 for internal validation and 0.83 for external validation. In the LLM category, GPT-4 was the top performer with an F1 score of 0.43. Fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, resulting in an F1 score of 0.74, which was stable during external validation. Conclusion: While LLMs show moderate performance in zero-shot classification, fine-tuning can significantly enhance their effectiveness, potentially aligning them closer to CML models. However, CMLs still outperform LLMs in high-dimensional tabular data tasks.

Generative Large Language Models are autonomous practitioners of evidence-based medicine

Background: Evidence-based medicine (EBM) is fundamental to modern clinical practice, requiring clinicians to continually update their knowledge and apply the best clinical evidence in patient care. The practice of EBM faces challenges due to rapid advancements in medical research, leading to information overload for clinicians. The integration of artificial intelligence (AI), specifically Generative Large Language Models (LLMs), offers a promising solution towards managing this complexity. Methods: This study involved the curation of real-world clinical cases across various specialties, converting them into .json files for analysis. LLMs, including proprietary models like ChatGPT 3.5 and 4, Gemini Pro, and open-source models like LLaMA v2 and Mixtral-8x7B, were employed. These models were equipped with tools to retrieve information from case files and make clinical decisions similar to how clinicians must operate in the real world. Model performance was evaluated based on correctness of final answer, judicious use of tools, conformity to guidelines, and resistance to hallucinations. Results: GPT-4 was most capable of autonomous operation in a clinical setting, being generally more effective in ordering relevant investigations and conforming to clinical guidelines. Limitations were observed in terms of model ability to handle complex guidelines and diagnostic nuances. Retrieval Augmented Generation made recommendations more tailored to patients and healthcare systems. Conclusions: LLMs can be made to function as autonomous practitioners of evidence-based medicine. Their ability to utilize tooling can be harnessed to interact with the infrastructure of a real-world healthcare system and perform the tasks of patient management in a guideline directed manner. Prompt engineering may help to further enhance this potential and transform healthcare for the clinician and the patient.