Posters

A framework for studying machine learning methods in healthcare: The First EHR DREAM Challenge

Bergquist and Schaffter et al.

A Modified Early Warning Score Predicts Decompensation in COVID-19 Patients

Cavalier et al.

Automated disease cohort selection using word embeddings improves upon electronic phenotyping algorithms

De Freitas et al.

Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information

Flamholz et al.

On the design of CNN for automatic detection of Alzheimer’s disease

Liu et al.

Deep learning to assess long-term mortality from chest radiographs (x-rays)

Lu et al.

Predicting Preventable Hospital Readmissions with Causal Machine Learning

Marafino et al.

AI/ML to Predict Extubation Failure

Michelson et al.

Feature Robustness in Non-stationary Health Records

Nestor and McDermott et al.

Machine learning models improve post-transplant survival predictions

Pettit et al.

Fair Clinical Risk Prediction: Trade-offs and Alternatives

Pfohl et al.

The Role of Context in the Prediction of Acute Hypotension in Critical Care

Prasad et al.

Deep Learning to Estimate Biological Age from Chest Radiographs

Raghu et al.

Medication Regimen Extraction From Clinical Conversations

Selvaraj et al.

Do deep learning chest X-ray classifiers discriminate?

Seyyed-Kalantari et al.

Predicting Future Epilepsy Surgery Using Multimodal Electronic Health Record Data

Wissel et al.

Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

Zhang and Lu et al.