78 citations · 209 across the 15 of their papers we have counts for
15 papers
Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets
Jinge Wu, Rowena Smith, Honghan Wu
Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adol…
Adverse Childhood Experiences Identification from Clinical Notes with Ontologies and NLP
Jinge Wu, Rowena Smith, Honghan Wu
Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adol…
Quantifying Health Inequalities Induced by Data and AI Models
Honghan Wu, Minhong Wang, Aneeta Sylolypavan +1
AI technologies are being increasingly tested and applied in critical environments including healthcare. Without an effective way to detect and mitigate AI induced inequalities, AI…
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang +7
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to…
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Qingyu Chen, Alexis Allot, Robert Leaman +35
The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design…
Automated Clinical Coding: What, Why, and Where We Are?
Hang Dong, Matúš Falis, William Whiteley +5
Clinical coding is the task of transforming medical information in a patient's health records into structured codes so that they can be used for statistical analysis. This is a cog…
A Unified Review of Deep Learning for Automated Medical Coding
Shaoxiong Ji, Wei Sun, Xiaobo Li +6
Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents. Recent ad…
Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision
Hang Dong, Víctor Suárez-Paniagua, Huayu Zhang +3
The identification of rare diseases from clinical notes with Natural Language Processing (NLP) is challenging due to the few cases available for machine learning and the need of da…
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
Zina M Ibrahim, Daniel Bean, Thomas Searle +7
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome predic…
Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation
Hang Dong, Víctor Suárez-Paniagua, William Whiteley +1
Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals bu…
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units
Zina Ibrahim, Honghan Wu, Richard Dobson
Many areas of research are characterised by the deluge of large-scale highly-dimensional time-series data. However, using the data available for prediction and decision making is h…
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
Rebecca Bendayan, Honghan Wu, Zeljko Kraljevic +9
Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records. In t…
On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning
Zina Ibrahim, Honghan Wu, Ahmed Hamoud +3
Current machine learning models aiming to predict sepsis from Electronic Health Records (EHR) do not account for the heterogeneity of the condition, despite its emerging importance…
Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study
Honghan Wu, Karen Hodgson, Sue Dyson +6
Background: Many efforts have been put into the use of automated approaches, such as natural language processing (NLP), to mine or extract data from free-text medical records to co…
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
Philip John Gorinski, Honghan Wu, Claire Grover +6
This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data. In particular, we look into the application of (i) rul…