Using language models to improve a traceable rare-disease diagnosis tool
Combining a language model with an ontology-based ranking tool improves rare-disease diagnosis while keeping the evidence trail.
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Knowledge graphs and rare disease
Ontology-grounded diagnosis and phenotyping that keeps its evidence traceable.
Knowing when a rare-disease AI should hold back its answer
A diagnostic tool needs to know when to commit to its top guess and when to pass the case to a person, and for rare diseases that is hard to get right.
A realistic test for AI that diagnoses rare diseases
RareArena is a large test built from real case reports, designed to show how well AI language models can spot rare diseases.
An AI proofreader for radiology reports
A three-stage method that detects, locates and corrects mistakes in radiology reports, using medical knowledge to help language models do it.
Who has which rare disease? A map of 406 rare diseases across ethnic groups in England
Of 62.5 million people in England, 1.7% had one of 406 rare diseases, and they had more recorded COVID-19 infection (32.8% vs 29.0%).
Can wearables and apps make rare disease clinical trials easier?
A review of 262 trials in ten rare diseases shows digital tools increasingly used for remote monitoring, treatment and long-term care.
Teaching clinical knowledge to language models through how they split words
K-Tokeniser builds medical concepts such as drugs and diseases into the way a language model reads text, so it needs less training data.
Can AI connect the dots in biomedicine? A test of multi-step reasoning
BioHopR tests whether AI models can chain several facts together to answer biomedical questions with more than one correct answer.
Rare diseases and COVID-19: what the records of 58 million people show
Eight rare diseases carried a clearly higher risk of dying from COVID-19, even after vaccination, in a study covering 331 rare diseases and 58 million people.
Finding rare disease patients in clinical notes with a hybrid of dictionaries and language models
Combining dictionary-based language processing with large language models identified rare diseases better than either alone, and found patients missing from structured records.
Using retrieval to help language models correct radiology report errors
A three-stage method that detects, locates and corrects errors in radiology reports using internal and external knowledge retrieval.
Teaching a language model radiology vocabulary to write better report summaries
RadBARTsum retrains a language model with medical-entity masking so it learns radiology knowledge, which improves summaries of radiology reports.
Three ways to teach AI to curate a chemistry ontology, compared
For building a biomedical ontology, we compared in-context learning, fine-tuning and supervised learning on more than 1.8 million facts.
Reading messy clinical text with a tool inspired by DNA sequence search
TBLAT borrows ideas from BLAST, the tool for searching DNA sequences, to match misspelt and abbreviated clinical terms to the right concepts.
Finding rare diseases in clinical notes without hand-labelled training data
By combining language technology with a disease ontology, we identified rare disease mentions in health records with over 90% accuracy, with no human annotation of the training data.
Automated clinical coding: what, why and where we are
A perspective on why coding clinical records is hard to automate, covering seven challenges and why knowledge-based methods should be combined with deep learning.
Derive insights from health data using knowledge graph technologies
How knowledge graph technologies help computers read free-text health records, told through real examples from an invited talk.
A very simple guide of Knowledge Graph for Health Data Scientists
A quick, jargon-light introduction to knowledge graphs and why they matter for health data science.
Telling types of stroke apart in UK Biobank by reading brain scan reports
Combining language processing with clinical rules on brain scan reports identified bleeding strokes in UK Biobank with high accuracy, improving on coded data alone.
Which algorithm best finds new genes for small vessel disease of the brain?
We benchmarked seven network-based gene-ranking methods for cerebral small vessel disease and found one clearly best overall, with an important limit on discovering truly new genes.
Letting researchers define a disease once and run it on any health database
A knowledge-driven framework turns a clinical definition of a phenotype into queries that run automatically on different data sources, tested on five Scottish health datasets.
SemEHR: turning weeks of hospital record searching into minutes
SemEHR is an open-source tool that finds what clinical notes say about patients, cutting recruitment-criteria checking from days to minutes.
Predicting drug side effects that clinical trials missed
A knowledge-graph method predicted unknown adverse drug reactions (AUC 0.92 on known ones), and high-confidence predictions were checked against real hospital records.
Looking for the formal papers? Every story links to its paper. Browse publications →