2020 Abstracts

Oral Presentations

An artificial intelligence method for fast diagnosis of rare disease

Francisco M. De La Vega, Barry Moore, Erwin Frise, Shimul Chowdhury, Narayanan Veeraraghavan, Stephen F. Kingsmore, Martin G. Reese, and Mark Yandell

Deep learning assistance for physician diagnosis of tuberculosis
using chest x-rays in HIV patients

Pranav Rajpurkar, Chloe O’Connell, Amit Schechter, Nishit Asnani, Jason Li, Amirhossein Kiani, Robyn L. Ball, Marc Mendelson, Gary Maartens, Daniël J. van Hoving, Rulan Griesel, Andrew Y. Ng, Tom H. Boyles & Matthew P. Lungren

Laura Early Warning System (LEWS)

Hugo Morales, Jhonatan Kobylarz, Mateus Cichelero, Felipe Barletta, Cristian Rocha

Deep Learning to Predict Renal Outcomes Solely Based on
Kidney Ultrasound Images

Mandy Rickard, Lauren Erdman, Marta Skreta, Daniel T. Keefe, Michael Brudno, Armando J. Lorenzo, Anna Goldenberg

System for High Intensity EvaLuation During Radiation Therapy
(SHIELD-RT)

Julian C. Hong, Neville C.W. Eclov, Nicole Dalal, Samantha M. Thomas, Sarah J. Stephens, Mary Malicki, Stacey Shields, Alyssa Cobb, Yvonne M. Mowery, Donna Niedzwiecki, Jessica D. Tenenbaum, Manisha Palta

Posters

Predicting Future Epilepsy Surgery Using Multimodal Electronic Health Record Data

Wissel et al.

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

Lu et al.

Computational Phenotyping with Limited Data and Winning the Partners Healthcare Biobank Challenge

Chakraborty et al.

Deep Learning to Estimate Biological Age from Chest Radiographs

Raghu et al.

Do deep learning chest X-ray classifiers discriminate?

Seyyed-Kalantari et al.

Fair Clinical Risk Prediction: Trade-offs and Alternatives

Pfohl et al.

Machine learning models improve post-transplant survival predictions

Pettit et al.

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

Flamholz et al.

Identification of antibiotic resistance in tuberculosis with whole genome sequencing and neural networks

Chen et al.

AI/ML to Predict Extubation Failure

Michelson et al.

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

Freitas et al.

Feature Robustness in Non-stationary Health Records

Nestor and McDermott et al.

Predicting Preventable Hospital Readmissions with Causal Machine Learning

Marafino et al.

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

Cavalier et al.

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

Liu et al.

Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

Zhang and Lu et al.

A framework for studying machine learning methods in healthcare: the first EHR DREAM challenge

Bergquist and Schaffter et al.

Medication Regimen Extraction From Clinical Conversations

Selvaraj et al.

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

Prasad et al.