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Found 13 papers, 5 papers with code
The Potential and Pitfalls of using a Large Language Model such as ChatGPT or GPT-4 as a Clinical Assistant
Recent studies have demonstrated promising performance of ChatGPT and GPT-4 on several medical domain tasks.
Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint
Structured (tabular) data in the preclinical and clinical domains contains valuable information about individuals and an efficient table-to-text summarization system can drastically reduce manual efforts to condense this data into reports.
A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases
This paper proposes a scalable workflow which leverages both structured data and unstructured textual notes from EHRs with techniques including NLP, AutoML and Clinician-in-the-Loop mechanism to build machine learning classifiers to identify patients at scale with given diseases, especially those who might currently be miscoded or missed by ICD codes.
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge
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.
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case
Contextualised word embeddings is a powerful tool to detect contextual synonyms.
Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases
We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to predict outcomes in the Intensive Care Unit (ICU).
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization.
Ranked #1 on
Abstractive Text Summarization
on AESLC
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis.
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier.
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification.
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.
Ranked #1 on
Traffic Prediction
on Q-Traffic
The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation
Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge.
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation
We demonstrate that %the capability of our method to understand the sentence descriptions, so as to I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art.
