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.
We study the application of large language models to zero-shot and few-shot classification of tabular data.
A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes.
We demonstrate that co-training (Blum & Mitchell, 1998) can improve the performance of prompt-based learning by using unlabeled data.
Label-scarce, high-dimensional domains such as healthcare present a challenge for modern machine learning techniques.
Automated decision support can accelerate tedious tasks as users can focus their attention where it is needed most.
Decision Making Human-Computer Interaction
We reformulate the annotation framework for clinical entity extraction to factor in these issues to allow for robust end-to-end system benchmarking.
We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation.
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.
no code implementations • 13 Jan 2020 • Benjamin Birnbaum, Nathan Nussbaum, Katharina Seidl-Rathkopf, Monica Agrawal, Melissa Estevez, Evan Estola, Joshua Haimson, Lucy He, Peter Larson, Paul Richardson
Objective Electronic health records (EHRs) are a promising source of data for health outcomes research in oncology.
Increasingly large electronic health records (EHRs) provide an opportunity to algorithmically learn medical knowledge.
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.
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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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.