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Found 22 papers, 8 papers with code
"What's Up, Doc?": Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets
People are increasingly seeking healthcare information from large language models (LLMs) via interactive chatbots, yet the nature and inherent risks of these conversations remain largely unexplored.
Diagnosing our datasets: How does my language model learn clinical information?
For both use cases, we investigate the frequency of relevant clinical information in their corresponding pretraining corpora, the relationship between pretraining data composition and model outputs, and the sources underlying this data.
Retrieval-augmented systems can be dangerous medical communicators
Patients have long sought health information online, and increasingly, they are turning to generative AI to answer their health-related queries.
Generative AI in Medicine
The increased capabilities of generative AI have dramatically expanded its possible use cases in medicine.
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models
In this work, we investigate the potential of large language models to generate patient summaries based on doctors' notes and study the effect of training data on the faithfulness and quality of the generated summaries.
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study
Participants (N=200, healthy, female-identifying patients) were randomly assigned three clinical notes in our tool with varying levels of augmentations and answered quantitative and qualitative questions evaluating their understanding of follow-up actions.
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout.
Machine Learning for Health symposium 2022 -- Extended Abstract track
A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28, 2022, in New Orleans, Louisiana, USA.
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
We study the application of large language models to zero-shot and few-shot classification of tabular data.
Large Language Models are Few-Shot Clinical Information Extractors
A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes.
Co-training Improves Prompt-based Learning for Large Language Models
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data.
Leveraging Time Irreversibility with Order-Contrastive Pre-training
Label-scarce, high-dimensional domains such as healthcare present a challenge for modern machine learning techniques.
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative
Automated decision support can accelerate tedious tasks as users can focus their attention where it is needed most.
Decision Making
Human-Computer Interaction
Robust Benchmarking for Machine Learning of Clinical Entity Extraction
We reformulate the annotation framework for clinical entity extraction to factor in these issues to allow for robust end-to-end system benchmarking.
Fast, Structured Clinical Documentation via Contextual Autocomplete
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation.
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Data cleaning is naturally framed as probabilistic inference in a generative model of ground-truth data and likely errors, but the diversity of real-world error patterns and the hardness of inference make Bayesian approaches difficult to automate.
Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research
Objective Electronic health records (EHRs) are a promising source of data for health outcomes research in oncology.
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge.
TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes
In this work, we present TIFTI (Temporally Integrated Framework for Treatment Intervals), a robust framework for extracting oral drug treatment intervals from a patient's unstructured notes.
Modeling polypharmacy side effects with graph convolutional networks
The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type.
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Large-scale analysis of disease pathways in the human interactome
However, we show that higher-order network structures, such as small subgraphs of the pathway, provide a promising direction for the development of new methods.
