Search Results for author:
Found 22 papers, 7 papers with code
CEHR-GPT: Generating Electronic Health Records with Chronological Patient Timelines
Synthetic Electronic Health Records (EHR) have emerged as a pivotal tool in advancing healthcare applications and machine learning models, particularly for researchers without direct access to healthcare data.
Variational Shapley Network: A Probabilistic Approach to Self-Explaining Shapley values with Uncertainty Quantification
Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes.
SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded Entity Retrieval
To increase entity coverage, we train a smaller, encoder-only model to predict salient entities, which are treated as content-plans to guide the LLM.
Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling
We propose a survival analysis approach which eliminates the need to tune hyperparameters such as mixture assignments and bin sizes, reducing the burden on practitioners.
A Coreset-based, Tempered Variational Posterior for Accurate and Scalable Stochastic Gaussian Process Inference
We present a novel stochastic variational Gaussian process ($\mathcal{GP}$) inference method, based on a posterior over a learnable set of weighted pseudo input-output points (coresets).
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
We conduct a human preference study on 100 CNN DailyMail articles and find that that humans prefer GPT-4 summaries that are more dense than those generated by a vanilla prompt and almost as dense as human written summaries.
Generating EDU Extracts for Plan-Guided Summary Re-Ranking
Similarly, on 1k samples from CNN / DM, we show that prompting GPT-3 to follow EDU plans outperforms sampling-based methods by 1. 05 ROUGE-2 F1 points.
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization
Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE).
A Meta-Evaluation of Faithfulness Metrics for Long-Form Hospital-Course Summarization
To better understand the limitations of abstractive systems, as well as the suitability of existing evaluation metrics, we benchmark faithfulness metrics against fine-grained human annotations for model-generated summaries of a patient's Brief Hospital Course.
Learning to Revise References for Faithful Summarization
In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text.
Assessing Phenotype Definitions for Algorithmic Fairness
While there are multiple potential sources of bias when constructing phenotype definitions which may affect their fairness, it is not standard in the field of phenotyping to consider the impact of different definitions across subgroups of patients.
What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization
Summarization of clinical narratives is a long-standing research problem.
A generative, predictive model for menstrual cycle lengths that accounts for potential self-tracking artifacts in mobile health data
Our model offers several advantages: 1) accounting explicitly for self-tracking artifacts yields better prediction accuracy as likelihood of skipping increases; 2) because it is a generative model, predictions can be updated online as a given cycle evolves, and we can gain interpretable insight into how these predictions change over time; and 3) its hierarchical nature enables modeling of an individual's cycle length history while incorporating population-level information.
Exploring Gender Disparities in Time to Diagnosis
Sex and gender-based healthcare disparities contribute to differences in health outcomes.
Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells
We introduce Latent Meaning Cells, a deep latent variable model which learns contextualized representations of words by combining local lexical context and metadata.
Towards Patient Record Summarization Through Joint Phenotype Learning in HIV Patients
In this paper, we focus our experiments on assessing the learned phenotypes and their relatedness as learned from a specific patient population.
Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics
We present an end-to-end statistical framework for personalized, accurate, and minimally invasive modeling of female reproductive hormonal patterns.
Machine Learning and Visualization in Clinical Decision Support: Current State and Future Directions
Heretofore, there has not been a review of ways in which research in machine learning and other types of data-driven techniques can contribute effectively to clinical care and the types of support they can bring to clinicians.
Phenotyping Endometriosis through Mixed Membership Models of Self-Tracking Data
We investigate the use of self-tracking data and unsupervised mixed-membership models to phenotype endometriosis.
Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories
Generative Adversarial Networks (GANs) represent a promising class of generative networks that combine neural networks with game theory.
Predicting Drug-Induced Laboratory Test Effects
Representation Learning
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Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes
The motivation for this work is to model the hormonal cycle and predict its phases in time, both for healthy individuals and for those with disorders of the reproductive system.
Deep Survival Analysis
The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care.
