activity
20202022
most citedA Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20221 cited

A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases

Jingqing Zhang, Atri Sharma, Luis Bolanos +4

Clinicians may rely on medical coding systems such as International Classification of Diseases (ICD) to identify patients with diseases from Electronic Health Records (EHRs). Howev…

cs.CL20221 cited

Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge

Ashwani Tanwar, Jingqing Zhang, Julia Ive +2

Extracting phenotypes from clinical text has been shown to be useful for a variety of clinical use cases such as identifying patients with rare diseases. However, reasoning with nu…

cs.LG2022

Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions

Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra +2

We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, o…

cs.CL2021

Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case

Jingqing Zhang, Luis Bolanos, Tong Li +6

Contextualised word embeddings is a powerful tool to detect contextual synonyms. However, most of the current state-of-the-art (SOTA) deep learning concept extraction methods remai…

cs.LG2020

Handling Variable-Dimensional Time Series with Graph Neural Networks

Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra +2

Several applications of Internet of Things (IoT) technology involve capturing data from multiple sensors resulting in multi-sensor time series. Existing neural networks based appro…