We present Bonito, the first open-source model for conditional task generation: the problem of converting unannotated corpus into a collection of tasks for instruction tuning. Our goal is to enable efficient task adaptation of instruction tuned language models on users’ specialized, private data without relying on proprietary API-access-only models like GPT-4. We create Bonito by remixing existing, general-purpose instruction tuning…

Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science. We chart a path forward as a collective effort to sustain the creation and maintenance of these datasets and methods…

Background: Foundation models hold promise for transforming artificial intelligence (AI) in healthcare by providing modular components that are easily adaptable to downstream healthcare tasks, making AI development more scalable and cost-effective. Foundation models for structured electronic health records (EHR), trained on coded medical records from millions of patients, demonstrated benefits including increased performance with fewer training labels, and improved robustness…

Synthesizing information from multiple data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus on single-modality data due to a lack of publicly available, multimodal medical datasets. To address this limitation, we introduce INSPECT, which contains de-identified longitudinal records from a large cohort of patients at risk for pulmonary…

Objective: We sought to develop a weak supervision-based approach to demonstrate feasibility of post-market surveillance of wearable devices that render AF pre-diagnosis. Materials and Methods: Two approaches were evaluated to reduce clinical note labeling overhead for creating a training set for a classifier: one using programmatic codes, and the other using prompts to large language models (LLMs). Probabilistically labeled notes…

A promising approach for improving the performance of vision-language models like CLIP for image classification is to extend the class descriptions (i.e., prompts) with related attributes, e.g., using brown sparrow instead of sparrow. However, current zero-shot methods select a subset of attributes regardless of commonalities between the target classes, potentially providing no useful information that would have helped to distinguish…

Introduction: With the advent of consumer-facing devices that can render atrial fibrillation (AF) pre-diagnosis, medical wearables now have the potential to affect diagnosis rates and medical care. Post-market surveillance is necessary to understand the impact of wearables on patient outcomes and health care utilization, but is hindered by the lack of codified terms in EHR that capture wearable use. Research…