Cogstack pleased to be supporting the recently announced OLS funded Mental Health Mission
26 May 2023
Find out more
26 May 2023
Cogstack pleased to be supporting the recently announced OLS funded Mental Health Mission
CogStack is a healthcare application framework that allows you to handle, analyse and draw insights from information from unstructured free-form clinical data sources e.g. Electronic Health Records where majority of the expressive clinical content is locked-up in multiple formats of unstructured data (i.e. binary word docs, PDFs, images, text). Features include Natural Language Processing (NLP), Full Search, Alerting, Cohort Selection, Population Health Dashboards and Clinical Research.
5
UK Hospitals
3
countries deployed
350
million diagnostic results, reports, documents
Latest News
New CogStack Large Language Model releases
24 May 2023
1. OpenGPT a framework for producing grounded domain-specific LLMs
2. NHS-LLM a conversational model for healthcare using OpenGPT, trained
Sites in the Netherlands, Spain and Czech republic adopt CogStack as part of EU Programme
24 May 2023
Find out more
De-Identification of Patient notes with AnonCAT
2 May 2023
A new blog article describing how CogStack AnonCAT can patient privacy using MedCAT library and Transformers.
Read the article
CogStack Tools
CogStack-NiFi
Building data processing pipelines for documents processing with NLP using Apache NiFi and related services
MedCAT
Healthcare concept Natural Language Processing for UMLS/SNOMED or any other ontology. Ready for multilingual NLP.
MedCATtrainer
Trainer interface to inspect, improve and fine-tune biomedical and clinical natural language processing
Cogstack-Foresight
A deep learning generative MedGPT AI producing longitudinal forecasts of patient diseases, conditions, symptoms, medications and outcomes.
CogStack-Pipeline
A Distributed, fault tolerant batch processing for Natural Language Applications and Search, using remote partitioning
CogStack-Recipes
Example deployment recipes based on CogStack
Team
Prof Richard Dobson
Academic Lead, Professor of Health Informatics
Prof James Teo
Clinical Lead, Professor of Neurology
Tom Searle
Programme Manager and PhD student
Dr Anthony Shek
Senior AI Engineer
Zeljko Kraljevic
Research Fellow
Barbara Rafferty
Head of Digital & Data
Aleksandra Foy
NLP Workstream Manager
Kawsar Noor
Research Software Developer
Vlad Dinu
Research Software Engineer
Dr Angus Roberts
Senior Lecturer in Health Informatics
Amos Folarin
Software Development Lead
Yamiko Msosa
Postdoctoral Research Systems Engineer
Tao Wang
Postdoctoral Research Associate
Dr Joshua Au-Yeung
Cogstack Clinical Fellow
Honghan Wu
Clinical Informatics
Alex Handy
Senior Research Fellow and Data Scientist
Dan Bean
Clinical Informatics
Testimonials
At King’s, a brilliant team led by Dr James Teo and Professor Richard Dobson have built a natural language processing tool called Cogstack. The Cogstack AI can perform manual coding and data collection tasks in a tenth of the time that it takes a human analyst. Technologies like these have potential to save millions in the cost of coding and analysing data.
Matt Hancock MP
Secretary of State for Health and Social Care
The team at King’s College Hospital NHS Foundation Trust and the South London and Maudsley Hospital tested CogStack for clinical coding in a fracture outpatient clinic setting to identify under-coding and was able to triple the depth of coding within a month (from ~10% of cases to 30% of cases having procedures recorded accurately). This translates to £1,260,000 of financial activity per annum even without the efficiency gains
NHSX
Health data is key in getting it right for patients, but we’re not so good at the IT. CRIS, CogStack and automated systems support data collection and analysis can provide powerful evidence and very important for outcome measurement.
Matthew Hotopf CBE
Director of NIHR Maudsley BRC
Have a look at CogStack - big opportunities for these use cases at East Kent Hospitals once the electronic health record is in place.
Marc Farr
Chief Analytical Officer, East Kent Hospitals
Kullu Cecil and I are involved in work using historical data and current data to predict severe depression. Uses CogStack from King's College London and AI expertise from Liverpool Uni. Some early promising results. Looking for approvals to trial in clinical setting.
Jim Hughes
Strategic Advisor Digital Programmes, Mersey Care NHS Foundation Trust
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