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Found 21 papers, 8 papers with code
Edinburgh_UCL_Health@SMM4H’22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination
Our team participated in the tasks related to the Identification of Adverse Drug Events (ADEs), the classification of change in medication (change-med) and the classification of self-report of vaccination (self-vaccine).
Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns
This work proposes a novel approach to enhance human-computer interaction in chest X-ray analysis using Vision-Language Models (VLMs) enhanced with radiologists' attention by incorporating eye gaze data alongside textual prompts.
Hallucination Benchmark in Medical Visual Question Answering
The recent success of large language and vision models (LLVMs) on vision question answering (VQA), particularly their applications in medicine (Med-VQA), has shown a great potential of realizing effective visual assistants for healthcare.
Exploring Multimodal Large Language Models for Radiology Report Error-checking
This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports.
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest
Five setups were designed to assess ML and FT model performance across different data availability scenarios. Datasets for curation tasks included: task 1 (620, 386), task 2 (611, 430), and task 3 (617, 381), maintaining a 50:50 positive versus negative ratio.
Adverse Childhood Experiences Identification from Clinical Notes with Ontologies and NLP
Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence.
Ontology-Driven Self-Supervision for Adverse Childhood Experiences Identification Using Social Media Datasets
In this paper, we present an ontology-driven self-supervised approach (derive concept embeddings using an auto-encoder from baseline NLP results) for producing a publicly available resource that would support large-scale machine learning (e. g., training transformer based large language models) on social media corpus.
Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes
Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes.
Quantifying Health Inequalities Induced by Data and AI Models
Extensive analyses were carried out to quantify health inequalities (a) embedded in two real-world ICU datasets; (b) induced by AI models trained for two resource allocation scenarios.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature.
Automated Clinical Coding: What, Why, and Where We Are?
Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.
A Unified Review of Deep Learning for Automated Medical Coding
Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents.
Rare Disease Identification from Clinical Notes with Ontologies and Weak Supervision
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 data annotation from clinical experts.
A Systematic Review of Natural Language Processing Applied to Radiology Reports
Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited.
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data
The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care.
Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation
LE initialisation consistently boosted most deep learning models for automated medical coding.
Ranked #1 on
Multi-Label Text Classification
on MIMIC-III
Modeling Rare Interactions in Time Series Data Through Qualitative Change: Application to Outcome Prediction in Intensive Care Units
Many areas of research are characterised by the deluge of large-scale highly-dimensional time-series data.
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records.
On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine Learning
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 in prognosis and treatment.
Efficiently Reusing Natural Language Processing Models for Phenotype-Mention Identification in Free-text Electronic Medical Records: Methodology Study
Reusing NLP models in new settings, however, remains cumbersome - requiring validation and/or retraining on new data iteratively to achieve convergent results.
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches
This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data.
