Honghan Wu

Associate Professor

University College London

United Kingdom

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About

Honghan Wu is an Associate Professor at University College London, UK. His research focuses on the intersection of artificial intelligence and healthcare, with particular interests in language models, electronic health records, and predictive analytics. Recent articles by Dr. Wu delve into topics such as the evaluation of large language models, deep learning applications in patient outcome prediction, and the challenges faced by radiologists in monitoring AI technologies.

Articles (27)

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Collaborators (18)

Daniel Harris

Swansea Bay University Health Board

UNITED KINGDOM

Honghan Wu

Research Interests

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About

Articles (27)

Infusing clinical knowledge into language models by subword optimisation and embedding initialisation

Evaluation and Bias Analysis of Large Language Models in Generating Synthetic Electronic Health Records: Comparative Study

How Do Radiologists Currently Monitor AI in Radiology and What Challenges Do They Face? An Interview Study and Qualitative Analysis

Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review

A Unified Review of Deep Learning for Automated Medical Coding

Machine learning on cardiotocography data to classify fetal outcomes: A scoping review.

Applying contrastive pre-training for depression and anxiety risk prediction in type 2 diabetes patients based on heterogeneous electronic health records: a primary healthcare case study

A hybrid framework with large language models for rare disease phenotyping

Antidepressant and antipsychotic prescribing in patients with type 2 diabetes in Scotland: A time-trend analysis from 2004 to 2021

Optimising the paradigms of human AI collaborative clinical coding

The impact of inconsistent human annotations on AI driven clinical decision making

Antidepressant and antipsychotic drug prescribing and diabetes outcomes: A systematic review of observational studies

Term-BLAST-like alignment tool for concept recognition in noisy clinical texts

<i>FLAP</i>: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database.

Understanding the performance and reliability of NLP tools: a comparison of four NLP tools predicting stroke phenotypes in radiology reports.

Machine Learning to Classify Cardiotocography for Fetal Hypoxia Detection.

Ontology-driven and weakly supervised rare disease identification from clinical notes.

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review.

Automated clinical coding: what, why, and where we are?

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records.

Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

Prediction of Five-Year Cardiovascular Disease Risk in People with Type 2 Diabetes Mellitus: Derivation in Nanjing, China and External Validation in Scotland, UK

Spine-GFlow: A hybrid learning framework for robust multi-tissue segmentation in lumbar MRI without manual annotation

Increased COVID-19 mortality rate in rare disease patients: a retrospective cohort study in participants of the Genomics England 100,000 Genomes project.

Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction

A Knowledge Distillation Ensemble Framework for Predicting Short- and Long-Term Hospitalization Outcomes From Electronic Health Records Data.

Collaborators (18)

Daniel Harris

Isabel Straw

Charis Marwick

Shaoxiong Ji

Elizabeth Ellins

Huan Wang

Anoop Dinesh Shah

Tim Dong

William Whiteley

Gwenetta Curry

Adam Levine

Julia Townson

Daniel J Smith

Andreas Karwath

Linglong Qian

Michael Poon

Christina Pagel

Esa Pitkänen

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