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Found 10 papers, 4 papers with code
SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing
This paper presents our approach to multi-class text categorization of tweets mentioning prescription medications as being indicative of potential abuse/misuse (A), consumption/non-abuse (C), mention-only (M), or an unrelated reference (U) using natural language processing techniques.
Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model
Overall, this study provides new insights for enhancing the deployment of large language models in the societally important domain of healthcare, and improving their performance for broader populations.
A dynamic risk score for early prediction of cardiogenic shock using machine learning
We developed a deep learning-based risk stratification tool, called CShock, for patients admitted into the cardiac ICU with acute decompensated heart failure and/or myocardial infarction to predict onset of cardiogenic shock.
New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography
We found ECG-based assessment outperforms the ADA Risk test, achieving a higher area under the curve (0. 80 vs. 0. 68) and positive predictive value (13% vs. 9%) -- 2. 6 times the prevalence of diabetes in the cohort.
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
While the need for interpretable machine learning has been established, many common approaches are slow, lack fidelity, or hard to evaluate.
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction
The first is deterioration prediction from a single image, where our model achieves an area under receiver operating characteristic curve (AUC) of 0. 742 for predicting an adverse event within 96 hours (compared to 0. 703 with supervised pretraining) and an AUC of 0. 765 for predicting oxygen requirements greater than 6 L a day at 24 hours (compared to 0. 749 with supervised pretraining).
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time.
Assessment of Amazon Comprehend Medical: Medication Information Extraction
To further establish the generalizability of its medication extraction performance, a set of random internal clinical text notes from NYU Langone Medical Center were also included in this work.
Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research
Conventional text classification models make a bag-of-words assumption reducing text into word occurrence counts per document.
A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions.
