Search Results for author: Chufan Gao

Found 6 papers, 2 papers with code

Artificial Intelligence for In Silico Clinical Trials: A Review

no code implementations16 Sep 2022 Zifeng Wang, Chufan Gao, Lucas M. Glass, Jimeng Sun

In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials.

Classifying Unstructured Clinical Notes via Automatic Weak Supervision

1 code implementation24 Jun 2022 Chufan Gao, Mononito Goswami, Jieshi Chen, Artur Dubrawski

Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes.

Text Classification

Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection

1 code implementation15 Nov 2021 Saswati Ray, Sana Lakdawala, Mononito Goswami, Chufan Gao

In this work, we propose GLUE (Graph Deviation Network with Local Uncertainty Estimation), building on the recently proposed Graph Deviation Network (GDN).

Anomaly Detection Time Series Anomaly Detection

ACTIVE REFINEMENT OF WEAKLY SUPERVISED MODELS

no code implementations29 Sep 2021 Mononito Goswami, Chufan Gao, Benedikt Boecking, Saswati Ray, Artur Dubrawski

In domains such as clinical research, where data collection and its careful characterization is particularly expensive and tedious, this reliance on pointillisticaly labeled data is one of the biggest roadblocks to the adoption of modern data-hungry ML algorithms.

Active Learning

The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming

no code implementations24 Aug 2021 Chufan Gao, Mononito Goswami

Most advanced supervised Machine Learning (ML) models rely on vast amounts of point-by-point labelled training examples.

text-classification Text Classification

Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning

no code implementations12 Nov 2019 Chufan Gao, Fabian Falck, Mononito Goswami, Anthony Wertz, Michael R. Pinsky, Artur Dubrawski

By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition.

BIG-bench Machine Learning Survival Prediction +1

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