Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is simply not scalable, while previous attempts aimed at automating nutrition coaching lack the personalization needed to address these diverse challenges. This paper introduces a novel LLM-powered agentic workflow designed to provide personalized nutrition coaching by directly targeting and mitigating patient-specific barriers. Grounded in behavioral science principles, the workflow leverages a comprehensive mapping of nutrition-related barriers to corresponding evidence-based strategies. A specialized LLM agent intentionally probes for and identifies the root cause of a patient's dietary struggles. Subsequently, a separate LLM agent delivers tailored tactics designed to overcome those specific barriers with patient context. We designed and validated our approach through a user study with individuals with cardiometabolic conditions, demonstrating the system's ability to accurately identify barriers and provide personalized guidance. Furthermore, we conducted a large-scale simulation study, grounding on real patient vignettes and expert-validated metrics, to evaluate the system's performance across a wide range of scenarios. Our findings demonstrate the potential of this LLM-powered agentic workflow to improve nutrition coaching by providing personalized, scalable, and behaviorally-informed interventions.

Large Language Models (LLMs) have demonstrated their efficacy across a broad spectrum of tasks in healthcare applications. However, often LLMs need to be fine-tuned on taskspecific expert-annotated data to achieve optimal performance, which can be expensive and time consuming. In this study, we fine-tune PaLM-2 (Anil et al. (2023)) with parameter efficient finetuning (PEFT) using noisy labels obtained from gemini-pro 1.0 (Google (2024)) for the detection of Schedule-of-Event (SoE) tables, which specify care plan in clinical trial protocols. We introduce a filtering mechanism to select high-confidence labels for this table classification task, thereby reducing the noise in the auto-generated labels. We show that fine-tuned PaLM-2 with those labels achieves performance that exceeds the gemini-pro 1.0 and other LLMs. Furthermore, its performance is close to a PaLM-2 fine-tuned on labels obtained from non-expert annotators. Our results show that leveraging LLM-generated labels through powerful models like gemini-pro can potentially serve as a viable strategy for improving LLM performance through fine-tuning in specialized tasks, particularly in domains where expert annotations are scarce, expensive, or time-consuming to obtain.

Both models demonstrate mastery of the pre-training task but neither consistently outperform XGBoost on downstream supervised tasks. We encourage future work to focus on joint modeling of multiple EHR data categories and to include tree-based baselines in their evaluations. In recent years, self-supervised pre-training of masked language models (MLMs) (see Appendix A for background) has demonstrated remarkable success across a wide range of machine learning problems and has led to significant downstream improvements across diverse tasks in natural language processing (Liu et al., 2019; Devlin et al., 2019; Raffel et al., 2020). There is considerable excitement surrounding the potential of large pre-trained MLMs to achieve similar success in medical applications. For instance, existing applications of MLMs in medicine have already yielded promising results in tasks related to medical text understanding (Lee et al., 2020; Alsentzer et al., 2019; Huang et al., 2019; Yang et al., 2019; Beltagy et al., 2019). Laboratory data is abundant, routinely collected, less biased compared to other types of data in electronic health records (EHRs) like billing codes (Beam et al., 2021), and directly measure a patient's physiological state, offering a valuable opportunity for creating a medical foundation model. However, there is a large body of evidence showing that deep learning is consistently outperformed on so-called "tabular" data prediction tasks by traditional machine learning techniques like random forests, XGBoost, and even simple regression models (Bellamy et al., 2020; Finlayson et al., 2023; Sharma, 2013). The reasons for this are only partially understood, but previous work (Grinsztajn et al., 2022) has suggested that this phenomenon may be caused by a rotational invariance in deep learning models that is harmful for tabular data. More broadly, the success of deep learning is thought to be largely due to inductive biases that can be leveraged for images, text, and graphs. These inductive biases are absent or only weakly present in tabular data. Conversely, tree-based methods are scale invariant and robust to uninformative features. We evaluated both models on several downstream outcome prediction tasks and validated the success of pre-training with a set of intrinsic evaluations.

As large language models continue to be widely developed, robust uncertainty quantification techniques will become crucial for their safe deployment in high-stakes scenarios. In this work, we explore how conformal prediction can be used to provide uncertainty quantification in language models for the specific task of multiple-choice question-answering. We find that the uncertainty estimates from conformal prediction are tightly correlated with prediction accuracy. This observation can be useful for downstream applications such as selective classification and filtering out low-quality predictions. We also investigate the exchangeability assumption required by conformal prediction to out-of-subject questions, which may be a more realistic scenario for many practical applications. Our work contributes towards more trustworthy and reliable usage of large language models in safety-critical situations, where robust guarantees of error rate are required.

Self-supervised models trained with a contrastive loss such as CLIP have shown to be very powerful in zero-shot classification settings. However, to be used as a zero-shot classifier these models require the user to provide new captions over a fixed set of labels at test time. In many settings, it is hard or impossible to know if a new query caption is compatible with the source captions used to train the model. We address these limitations by framing the zero-shot classification task as an outlier detection problem and develop a conformal prediction procedure to assess when a given test caption may be reliably used. On a real-world medical example, we show that our proposed conformal procedure improves the reliability of CLIP-style models in the zero-shot classification setting, and we provide an empirical analysis of the factors that may affect its performance.