Machine Learning in Healthcare

Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database

The paper learns patient trajectories from EHR data using deep learning methods and compares them to traditional machine learning approaches.

Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery

The paper develops a machine learning system called Prescience to predict hypoxemia risk during surgery and improve anesthesiologists' ability to prevent it by providing interpretable explanations.

A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

This paper develops a machine learning model to predict short-term mortality risk in chemotherapy patients using electronic health records, achieving high accuracy across various cancer types and treatment regimens.

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models

This paper applies deep learning and Bayesian optimization to predict cancer clinical outcomes from genomic data, showing improved prognostic accuracy through transfer learning and providing an open-source framework.

Natural Language Processing for Classification of Acute, Communicable Findings on Unstructured Head CT Reports: Comparison of Neural Network and Non-Neural Machine Learning Techniques

The paper compares neural network and non-neural machine learning models for classifying acute communicable findings in head CT reports, finding neural networks achieve higher accuracy than SVM.