10 citations · 40 across the 10 of their papers we have counts for
11 papers
Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke
Anjali K. Kapoor, Anton Alyakin, Jin Vivian Lee +8
Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) predic…
Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks
Krithik Vishwanath, Mrigayu Ghosh, Anton Alyakin +3
Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike fron…
Generalist Foundation Models Are Not Clinical Enough for Hospital Operations
Lavender Y. Jiang, Angelica Chen, Xu Han +16
Hospitals and healthcare systems rely on operational decisions that determine patient flow, cost, and quality of care. Despite strong performance on medical knowledge and conversat…
Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model
Salman Rahman, Lavender Yao Jiang, Saadia Gabriel +3
Advances in large language models (LLMs) provide new opportunities in healthcare for improved patient care, clinical decision-making, and enhancement of physician and administrator…
A dynamic risk score for early prediction of cardiogenic shock using machine learning
Yuxuan Hu, Albert Lui, Mark Goldstein +18
Myocardial infarction and heart failure are major cardiovascular diseases that affect millions of people in the US. The morbidity and mortality are highest among patients who devel…
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
Hao Zhang, Neil Jethani, Aahlad Puli +4
Diabetes has a long asymptomatic period which can often remain undiagnosed for multiple years. In this study, we trained a deep learning model to detect new-onset diabetes using 12…
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs +1
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate. Amortized explanation methods reduce th…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Assessment of Amazon Comprehend Medical: Medication Information Extraction
Benedict Guzman, MS, Isabel Metzger +5
In November 27, 2018, Amazon Web Services (AWS) released Amazon Comprehend Medical (ACM), a deep learning based system that automatically extracts clinical concepts (which include…
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research
Vincent Major, Alisa Surkis, Yindalon Aphinyanaphongs
Conventional text classification models make a bag-of-words assumption reducing text into word occurrence counts per document. Recent algorithms such as word2vec are capable of lea…
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
Josua Krause, Aritra Dasgupta, Jordan Swartz +2
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propos…