Proceedings of the ACM Conference on Health, Inference, and Learning
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Multiple instance learning for predicting necrotizing enterocolitis in premature infants using microbiome data

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384466 ◽

2020 ◽

Author(s):

Thomas Hooven ◽

Yun Chao Lin ◽

Ansaf Salleb-Aouissi

Keyword(s):

Necrotizing Enterocolitis ◽

Premature Infants ◽

Multiple Instance Learning ◽

Microbiome Data

BMM-Net

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384447 ◽

2020 ◽

Author(s):

Ruru Zhang ◽

Jiawen He ◽

Shenda Shi ◽

Haihong E ◽

Zhonghong Ou ◽

...

Extracting medical entities from social media

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384467 ◽

2020 ◽

Author(s):

Sanja Scepanovic ◽

Enrique Martin-Lopez ◽

Daniele Quercia ◽

Khan Baykaner

Keyword(s):

Social Media

Survival cluster analysis

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384465 ◽

2020 ◽

Author(s):

Paidamoyo Chapfuwa ◽

Chunyuan Li ◽

Nikhil Mehta ◽

Lawrence Carin ◽

Ricardo Henao

Keyword(s):

Cluster Analysis

Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384450 ◽

2020 ◽

Author(s):

Niranjani Prasad ◽

Barbara Engelhardt ◽

Finale Doshi-Velez

Keyword(s):

Reinforcement Learning ◽

Policy Evaluation ◽

High Confidence ◽

Batch Reinforcement Learning

Disease state prediction from single-cell data using graph attention networks

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384449 ◽

2020 ◽

Cited By ~ 1

Author(s):

Neal Ravindra ◽

Arijit Sehanobish ◽

Jenna L. Pappalardo ◽

David A. Hafler ◽

David van Dijk

Keyword(s):

Single Cell ◽

Disease State ◽

Attention Networks ◽

State Prediction ◽

Cell Data

An adversarial approach for the robust classification of pneumonia from chest radiographs

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384458 ◽

2020 ◽

Cited By ~ 2

Author(s):

Joseph D. Janizek ◽

Gabriel Erion ◽

Alex J. DeGrave ◽

Su-In Lee

Keyword(s):

Chest Radiographs ◽

Robust Classification

Adverse drug reaction discovery from electronic health records with deep neural networks

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384459 ◽

2020 ◽

Author(s):

Wei Zhang ◽

Zhaobin Kuang ◽

Peggy Peissig ◽

David Page

Keyword(s):

Neural Networks ◽

Adverse Drug Reaction ◽

Drug Reaction ◽

Electronic Health Records ◽

Deep Neural Networks ◽

Health Records ◽

Reaction Discovery ◽

Electronic Health

Deidentification of free-text medical records using pre-trained bidirectional transformers

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384455 ◽

2020 ◽

Author(s):

Alistair E. W. Johnson ◽

Lucas Bulgarelli ◽

Tom J. Pollard

Keyword(s):

Medical Records ◽

Free Text

CaliForest

Proceedings of the ACM Conference on Health, Inference, and Learning ◽

10.1145/3368555.3384461 ◽

2020 ◽

Author(s):

Yubin Park ◽

Joyce C. Ho

Proceedings of the ACM Conference on Health, Inference, and LearningLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By ACM

Multiple instance learning for predicting necrotizing enterocolitis in premature infants using microbiome data

BMM-Net

Extracting medical entities from social media

Survival cluster analysis

Defining admissible rewards for high-confidence policy evaluation in batch reinforcement learning

Disease state prediction from single-cell data using graph attention networks

An adversarial approach for the robust classification of pneumonia from chest radiographs

Adverse drug reaction discovery from electronic health records with deep neural networks

Deidentification of free-text medical records using pre-trained bidirectional transformers

CaliForest

Proceedings of the ACM Conference on Health, Inference, and Learning
Latest Publications