Rodrigo M. Carrillo‐Larco

Chase Fensore, Rodrigo M. Carrillo-Larco, Shivani A. Patel et al.

Social determinants of health (SDOH) $-$ the myriad of circumstances in which people live, grow, and age $-$ play an important role in health outcomes. However, existing outcome prediction models often only use proxies of SDOH as features. Recent open data initiatives present an opportunity to construct a more comprehensive view of SDOH, but manually integrating the most relevant data for individual patients becomes increasingly challenging as the volume and diversity of public SDOH data grows. Large language models (LLMs) have shown promise at automatically annotating structured data. Here, we conduct an end-to-end case study evaluating the feasibility of using LLMs to integrate SDOH data, and the utility of these SDOH features for clinical prediction. We first manually label 700+ variables from two publicly-accessible SDOH data sources to one of five semantic SDOH categories. Then, we benchmark performance of 9 open-source LLMs on this classification task. Finally, we train ML models to predict 30-day hospital readmission among 39k heart failure (HF) patients, and we compare the prediction performance of the categorized SDOH variables with standard clinical variables. Additionally, we investigate the impact of few-shot LLM prompting on LLM annotation performance, and perform a metadata ablation study on prompts to evaluate which information helps LLMs accurately annotate these variables. We find that some open-source LLMs can effectively, accurately annotate SDOH variables with zero-shot prompting without the need for fine-tuning. Crucially, when combined with standard clinical features, the LLM-annotated Neighborhood and Built Environment subset of the SDOH variables shows the best performance predicting 30-day readmission of HF patients.

Rodrigo M Carrillo-Larco

BACKGROUND: Most artificial intelligence tools used to estimate nutritional content rely on image input. However, whether large language models (LLMs) can accurately predict nutritional values based solely on text descriptions of foods consumed remains unknown. If effective, this approach could enable simpler dietary monitoring without the need for photographs. METHODS: We used 24-hour dietary recalls from adolescents aged 12-19 years in the National Health and Nutrition Examination Survey (NHANES). An open-source quantized LLM was prompted using a 10-shot, chain-of-thought approach to estimate energy and five macronutrients based solely on text strings listing foods and their quantities. We then applied parameter-efficient fine-tuning (PEFT) to evaluate whether predictive accuracy improved. NHANES-calculated values served as the ground truth for energy, proteins, carbohydrates, total sugar, dietary fiber and total fat. RESULTS: In a pooled dataset of 11,281 adolescents (49.9% male, mean age 15.4 years), the vanilla LLM yielded poor predictions. The mean absolute error (MAE) was 652.08 for energy and the Lin's CCC <0.46 across endpoints. In contrast, the fine-tuned model performed substantially better, with energy MAEs ranging from 171.34 to 190.90 across subsets, and Lin's CCC exceeding 0.89 for all outcomes. CONCLUSIONS: When prompted using a chain-of-thought approach and fine-tuned with PEFT, open-source LLMs exposed solely to text input can accurately predict energy and macronutrient values from 24-hour dietary recalls. This approach holds promise for low-burden, text-based dietary monitoring tools.

Rodrigo M. Carrillo-Larco, Jesus Lovón Melgarejo, Manuel Castillo-Cara et al.

BACKGROUND: Medical large language models (LLMS) have demonstrated remarkable performance in answering medical examinations. However, the extent to which this high performance is transferable to medical questions in Spanish and from a Latin American country remains unexplored. This knowledge is crucial as LLM-based medical applications gain traction in Latin America. AIMS: to build a dataset of questions from medical examinations taken by Peruvian physicians pursuing specialty training; to fine-tune a LLM on this dataset; to evaluate and compare the performance in terms of accuracy between vanilla LLMs and the fine-tuned LLM. METHODS: We curated PeruMedQA, a multiple-choice question-answering (MCQA) datasets containing 8,380 questions spanning 12 medical domains (2018-2025). We selected eight medical LLMs including medgemma-4b-it and medgemma-27b-text-it, and developed zero-shot task-specific prompts to answer the questions appropriately. We employed parameter-efficient fine tuning (PEFT)and low-rant adaptation (LoRA) to fine-tune medgemma-4b-it utilizing all questions except those from 2025 (test set). RESULTS: medgemma-27b-text-it outperformed all other models, achieving a proportion of correct answers exceeding 90% in several instances. LLMs with <10 billion parameters exhibited <60% of correct answers, while some exams yielded results <50%. The fine-tuned version of medgemma-4b-it emerged victorious agains all LLMs with <10 billion parameters and rivaled a LLM with 70 billion parameters across various examinations. CONCLUSIONS: For medical AI application and research that require knowledge bases from Spanish-speaking countries and those exhibiting similar epidemiological profiles to Peru's, interested parties should utilize medgemma-27b-text-it or a fine-tuned version of medgemma-4b-it.

Gusseppe Bravo-Rocca, Peini Liu, Jordi Guitart et al.

Monitoring Machine Learning (ML) models in production environments is crucial, yet traditional approaches often yield verbose, low-interpretability outputs that hinder effective decision-making. We propose a cognitive architecture for ML monitoring that applies feature engineering principles to agents based on Large Language Models (LLMs), significantly enhancing the interpretability of monitoring outputs. Central to our approach is a Decision Procedure module that simulates feature engineering through three key steps: Refactor, Break Down, and Compile. The Refactor step improves data representation to better capture feature semantics, allowing the LLM to focus on salient aspects of the monitoring data while reducing noise and irrelevant information. Break Down decomposes complex information for detailed analysis, and Compile integrates sub-insights into clear, interpretable outputs. This process leads to a more deterministic planning approach, reducing dependence on LLM-generated planning, which can sometimes be inconsistent and overly general. The combination of feature engineering-driven planning and selective LLM utilization results in a robust decision support system, capable of providing highly interpretable and actionable insights. Experiments using multiple LLMs demonstrate the efficacy of our approach, achieving significantly higher accuracy compared to various baselines across several domains.