Search Results for author:
Found 29 papers, 13 papers with code
meds_reader: A fast and efficient EHR processing library
The growing demand for machine learning in healthcare requires processing increasingly large electronic health record (EHR) datasets, but existing pipelines are not computationally efficient or scalable.
Answering real-world clinical questions using large language model based systems
In contrast, retrieval augmented generation (RAG)-based and agentic LLM systems produced relevant and evidence-based answers for 24% (OpenEvidence) to 58% (ChatRWD) of questions.
Do Multimodal Foundation Models Understand Enterprise Workflows? A Benchmark for Business Process Management Tasks
To address this gap we present WONDERBREAD, the first benchmark for evaluating multimodal FMs on BPM tasks beyond automation.
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
However, current medical VLMs are generally limited to 2D images and short reports, and do not leverage electronic health record (EHR) data for supervision.
Automating the Enterprise with Foundation Models
We conduct initial experiments showing that multimodal FMs can address the limitations of traditional RPA with (1) near-human-level understanding of workflows (93% accuracy on a workflow understanding task) and (2) instant set-up with minimal technical barrier (based solely on a natural language description of a workflow, ECLAIR achieves end-to-end completion rates of 40%).
Standing on FURM ground -- A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems
Our novel contributions - usefulness estimates by simulation, financial projections to quantify sustainability, and a process to do ethical assessments - as well as their underlying methods and open source tools, are available for other healthcare systems to conduct actionable evaluations of candidate AI solutions.
Zero-Shot Clinical Trial Patient Matching with LLMs
First, we design an LLM-based system which, given a patient's medical history as unstructured clinical text, evaluates whether that patient meets a set of inclusion criteria (also specified as free text).
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.
EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models
The success of foundation models creates new challenges for healthcare ML by requiring access to shared pretrained models to validate performance benefits.
All models are local: time to replace external validation with recurrent local validation
We submit that external validation is insufficient to establish ML models' safety or utility.
Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery
Our objective was to determine whether two LLMs can serve information needs submitted by physicians as questions to an informatics consultation service in a safe and concordant manner.
The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs
The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations.
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record
Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant.
Instability in clinical risk stratification models using deep learning
While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised.
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of health inequities that may follow from the use of model-guided decision-making.
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinforcing inequities in care access and quality.
Ontology-driven weak supervision for clinical entity classification in electronic health records
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities.
Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data
Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes.
Counterfactual Reasoning for Fair Clinical Risk Prediction
We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data.
Medical device surveillance with electronic health records
Using hip replacements as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96. 3% precision, 98. 5% recall, and 97. 4% F1, improved classification performance by 12. 7- 53. 0% over rule-based methods, and detected over 6 times as many complication events compared to using structured data alone.
A Semi-Supervised Machine Learning Approach to Detecting Recurrent Metastatic Breast Cancer Cases Using Linked Cancer Registry and Electronic Medical Record Data
The dataset consisted of structured data and unstructured free-text clinical notes from EMR, linked to CCR, a component of the Surveillance, Epidemiology and End Results (SEER) database.
Predicting Inpatient Discharge Prioritization With Electronic Health Records
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care.
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk
Guidelines for the management of atherosclerotic cardiovascular disease (ASCVD) recommend the use of risk stratification models to identify patients most likely to benefit from cholesterol-lowering and other therapies.
The Effectiveness of Multitask Learning for Phenotyping with Electronic Health Records Data
We present experiments that elucidate when multitask learning with neural nets improves performance for phenotyping using EHR data relative to neural nets trained for a single phenotype and to well-tuned logistic regression baselines.
Countdown Regression: Sharp and Calibrated Survival Predictions
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance.
Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality.
Improving Palliative Care with Deep Learning
The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care.
Some methods for heterogeneous treatment effect estimation in high-dimensions
When devising a course of treatment for a patient, doctors often have little quantitative evidence on which to base their decisions, beyond their medical education and published clinical trials.
