Search Results for author: Chufan Gao

Found 10 papers, 4 papers with code

Signal Quality Auditing for Time-series Data

no code implementations1 Feb 2024 Chufan Gao, Nicholas Gisolfi, Artur Dubrawski

Signal quality assessment (SQA) is required for monitoring the reliability of data acquisition systems, especially in AI-driven Predictive Maintenance (PMx) application contexts.

Denoising Time Series

DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients

1 code implementation22 Sep 2023 Hanyin Wang, Chufan Gao, Christopher Dantona, Bryan Hull, Jimeng Sun

In the U. S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient.

Language Modelling Large Language Model

MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement

1 code implementation20 May 2023 Zifeng Wang, Chufan Gao, Cao Xiao, Jimeng Sun

Tabular data prediction has been employed in medical applications such as patient health risk prediction.

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 +1

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.

Math text-classification +1

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 +2

Cannot find the paper you are looking for? You can Submit a new open access paper.