diff --git a/docs/our_work/a_and_e_forecasting_tool.md b/docs/our_work/a_and_e_forecasting_tool.md index 859c7d17..24c40b67 100644 --- a/docs/our_work/a_and_e_forecasting_tool.md +++ b/docs/our_work/a_and_e_forecasting_tool.md @@ -2,7 +2,7 @@ title: 'Accident and Emergency (A&E) Forecasting Tool' summary: 'A probabilistic model which gives a three-week forecast for A&E departments, predicting their expected admissions loads.' origin: '' -tags: ['MODELLING', 'HOSPITAL', 'MONTE CARLO', 'URGENT CARE', ] +tags: ['FORECASTING', 'HOSPITAL', 'URGENT CARE', 'WIP', 'PYTHON'] --- ![Image showing plot of historical and predicted admissions rates. The plot shows the confidence intervals for the model's fit to historical data, and for its predictions.](../images/a_and_e_forecasting/forecast.png) diff --git a/docs/our_work/adrenal-lesions.md b/docs/our_work/adrenal-lesions.md index 5cd68d0b..42c20386 100644 --- a/docs/our_work/adrenal-lesions.md +++ b/docs/our_work/adrenal-lesions.md @@ -3,7 +3,7 @@ title: 'Using deep learning to detect adrenal lesions in CT scans' summary: 'This project explored whether applying AI and deep learning augment the detection of adrenal incidentalomas in patients’ CT scans.' category: 'Projects' origin: 'Skunkworks' -tags: ['CLASSIFICATION','LESION DETECTION','COMPUTER VISION','AI'] +tags: ['CLASSIFICATION','COMPUTER VISION', 'ACCURACY'] ---
diff --git a/docs/our_work/ai-deep-dive.md b/docs/our_work/ai-deep-dive.md index 8722796b..1ba68158 100644 --- a/docs/our_work/ai-deep-dive.md +++ b/docs/our_work/ai-deep-dive.md @@ -3,7 +3,7 @@ title: 'AI Deep Dive Workshops' summary: 'The NHS AI Lab Skunkworks team have developed and delivered a series of workshops to improve confidence working with AI.' category: 'Playbooks' origin: 'Skunkworks' -tags : ['AI', 'GUIDANCE', 'BEST PRACTICE'] +tags : ['BEST PRACTICE'] ---
diff --git a/docs/our_work/ai-dictionary.md b/docs/our_work/ai-dictionary.md index 163efb83..3e854f51 100644 --- a/docs/our_work/ai-dictionary.md +++ b/docs/our_work/ai-dictionary.md @@ -3,7 +3,7 @@ title: 'AI Dictionary' summary: 'A simple dictionary of common AI terms with a health and care context.' category: 'Projects' origin: 'Skunkworks' -tags : ['AI', 'DICTIONARY', 'JAVASCRIPT', 'REACT'] +tags : ['DOCUMENTATION', 'WEBDEV'] --- [![Image of a browser showing the AI dictionary.](../images/ai-dictionary.png)](https://nhsx.github.io/ai-dictionary) diff --git a/docs/our_work/ai-ethics.md b/docs/our_work/ai-ethics.md index 0cb7efcb..aed37fa5 100644 --- a/docs/our_work/ai-ethics.md +++ b/docs/our_work/ai-ethics.md @@ -2,7 +2,7 @@ title: 'AI Ethics in Practice at NHS England' summary: 'Defining ethical AI development best practice for data practitioners in the NHS' origin: 'NHS England' -tags: ['AI', 'ETHICS', 'TRANSPARENCY', 'QUALITY', 'DOCUMENTATION', 'RESEARCH', 'GUIDANCE', 'BEST PRACTICE'] +tags: ['ETHICS', 'QUALITY', 'DOCUMENTATION', 'RESEARCH', 'BEST PRACTICE'] --- !!! warning diff --git a/docs/our_work/ai-skunkworks.md b/docs/our_work/ai-skunkworks.md index 43b39e91..42807579 100644 --- a/docs/our_work/ai-skunkworks.md +++ b/docs/our_work/ai-skunkworks.md @@ -3,7 +3,7 @@ title: 'NHS AI Lab Skunkworks' summary: 'The NHS AI Lab Skunkworks team demonstrates the potential for AI in health and social care through practical experience' category: 'Overview' origin: 'Skunkworks' -tags: ['CLASSIFICATION','LESION DETECTION','AI', 'PYTHON'] +tags: ['CLASSIFICATION', 'PYTHON'] --- ![AI Skunkworks website homepage](../images/ai-skunkworks.png) diff --git a/docs/our_work/ambulance-delay-predictor.md b/docs/our_work/ambulance-delay-predictor.md index f7d42f2d..965c7447 100644 --- a/docs/our_work/ambulance-delay-predictor.md +++ b/docs/our_work/ambulance-delay-predictor.md @@ -3,7 +3,7 @@ title: 'Ambulance Handover Delay Predictor' summary: 'Predict ambulance delays at hospital, with reasons, to allow them to influence hospitals'' behaviour to mitigate against queues before they happen.' category: 'Projects' origin: 'Skunkworks' -tags: ['AMBULANCE','PREDICTION','RANDOM FOREST', 'CLASSIFICATION', 'TIME SERIES', 'PYTHON'] +tags: ['AMBULANCE','FORECASTING','MACHINE LEARNING', 'CLASSIFICATION', 'TIME SERIES', 'PYTHON'] --- ![Ambulance Handover Delay Predictor screenshot showing the handover times expected for different hospitals, with the high times highlighted in orange.](../images/ambulance-delay-predictor.png) diff --git a/docs/our_work/bed-allocation.md b/docs/our_work/bed-allocation.md index 8859551d..ccfb3078 100644 --- a/docs/our_work/bed-allocation.md +++ b/docs/our_work/bed-allocation.md @@ -3,7 +3,7 @@ title: 'Bed allocation' summary: 'Machine learning to effectively aid bed management in Kettering General Hospital.' category: 'Projects' origin: 'Skunkworks' -tags: ['HOSPITAL','BAYESIAN FORECASTING','MONTE CARLO','GREEDY ALLOCATION', 'PYTHON'] +tags: ['HOSPITAL','FORECASTING', 'PYTHON', 'WEBDEV'] --- ![Browser showing the dashboard for Kettering General Hospital that shows the forecasting of their bed occupancy.](../images/bed-allocation.png) diff --git a/docs/our_work/c245_synpath.md b/docs/our_work/c245_synpath.md index f5ae8d90..998bd810 100644 --- a/docs/our_work/c245_synpath.md +++ b/docs/our_work/c245_synpath.md @@ -3,7 +3,7 @@ title: Building the Foundations for a Generic Patient Simulator (SynPath) summary: Developing an agent-based simulation for generating synthetic patient pathways and scenario modelling for healthcare specific implementations. category: Projects permalink: c245_synpath.html -tags: ['SYNTHETIC DATA', 'PATHWAYS','SIMULATION'] +tags: ['SYNTHETIC DATA','SIMULATION'] --- ![](../images/c245fig1.png) diff --git a/docs/our_work/c338_poud.md b/docs/our_work/c338_poud.md index e02f72b2..104fd230 100644 --- a/docs/our_work/c338_poud.md +++ b/docs/our_work/c338_poud.md @@ -2,7 +2,7 @@ title: How to Assess the Privacy of Unstructured Data summary: What are the privacy considerations that need to be addressed when dealing with unstructured healthcare text data permalink: c338_poud.html -tags: ['UNSTRUCTURED DATA', 'PRIVACY', 'PII', 'BEST PRACTICE'] +tags: ['UNSTRUCTURED DATA', 'ETHICS', 'PII', 'BEST PRACTICE', 'STRUCTURED DATA'] ---
diff --git a/docs/our_work/c339_sas.md b/docs/our_work/c339_sas.md index ec04a58b..4e346b9c 100644 --- a/docs/our_work/c339_sas.md +++ b/docs/our_work/c339_sas.md @@ -2,7 +2,7 @@ title: Creating a Generic Adversarial Attack for any Synthetic Dataset summary: Can the privacy of a generated dataset be assessed through downstream adversarial attacks to highlight the risk of re-identification permalink: c339_sas.html -tags: ['SYNTHETIC DATA', 'GAN','TABULAR DATA'] +tags: ['SYNTHETIC DATA','STRUCTURED DATA'] --- ![](../images/sas.png) diff --git a/docs/our_work/c399_privfinger.md b/docs/our_work/c399_privfinger.md index 87f4fe70..5b861afe 100644 --- a/docs/our_work/c399_privfinger.md +++ b/docs/our_work/c399_privfinger.md @@ -2,7 +2,7 @@ title: Building a Tool to Assess the Privacy Risk of Text Data summary: Can we generate usable privacy scores for text data to support understanding of privacy concerns and the anonymisation process permalink: c399_privfinger.html -tags: ['TEXT DATA', 'LLM','PYTHON', 'PRIVACY'] +tags: ['TEXT DATA', 'LLM','PYTHON', ] --- This work was undertaken as an external commission aiming to build a pipeline of components which firstly generated unstructured medical notes using a structured output from [Synthea:tm:](https://github.com/synthetichealth/synthea) and then running these through [GPT-3.5](https://platform.openai.com/docs/models/gpt-3-5) models to transform these into human readable notes. diff --git a/docs/our_work/casestudy-recruitment-shortlisting.md b/docs/our_work/casestudy-recruitment-shortlisting.md index 9d1a7ee2..a074913d 100644 --- a/docs/our_work/casestudy-recruitment-shortlisting.md +++ b/docs/our_work/casestudy-recruitment-shortlisting.md @@ -3,7 +3,7 @@ title: 'Examining whether recruitment data can, and should, be used to train AI summary: 'Identify where bias has potential to occur when using machine learning for shortlisting interview candidates and mitigate it' category: 'CaseStudies' origin: 'Skunkworks' -tags: ['NLP', 'NEURAL NETWORKS'] +tags: ['NLP', 'NEURAL NETWORKS', 'SYNTHETIC DATA'] --- ## Info diff --git a/docs/our_work/casestudy-synthetic-data-pipeline.md b/docs/our_work/casestudy-synthetic-data-pipeline.md index f2e96b55..c6c4168a 100644 --- a/docs/our_work/casestudy-synthetic-data-pipeline.md +++ b/docs/our_work/casestudy-synthetic-data-pipeline.md @@ -3,7 +3,7 @@ title: 'Exploring how to create mock patient data (synthetic data) from real pat summary: 'The generation of safe and effective synthetic data to be used in technologies that improve health and social care.' category: 'CaseStudies' origin: 'Skunkworks' -tags: ['SYNTHETIC DATA','VAE','PRIVACY','QUALITY','UTILITY','AI', 'PYTHON'] +tags: ['SYNTHETIC DATA','NEURAL NETWORKS','ETHICS','QUALITY','PYTHON', 'MIMIC'] --- ![Example graphs studying the fidelity of the synthetic data to the artificial data.](../images/example_report_output.png) diff --git a/docs/our_work/ct-alignment.md b/docs/our_work/ct-alignment.md index dedfb201..af3302d6 100644 --- a/docs/our_work/ct-alignment.md +++ b/docs/our_work/ct-alignment.md @@ -3,7 +3,7 @@ title: 'CT Alignment and Lesion Detection' summary: 'A range of classical and machine learning computer vision techniques to align and detect lesions in anonymised CT scans over time from George Eliot Hospital NHS Trust.' category: 'Projects' origin: 'Skunkworks' -tags: ['CT','COMPUTER VISION','IMAGE REGISTRATION','PYTHON'] +tags: ['CT','COMPUTER VISION','PYTHON', 'ACCURACY'] --- ![CT Alignment and Lesion Detection screenshot of the interface for identifying lesions.](../images/ct-alignment.png) diff --git a/docs/our_work/data-lens.md b/docs/our_work/data-lens.md index 814ec689..d2c7b20e 100644 --- a/docs/our_work/data-lens.md +++ b/docs/our_work/data-lens.md @@ -3,7 +3,7 @@ title: 'Data Lens' summary: 'Data Lens brings together information about multiple databases, providing a fast-access search in multiple languages.' category: 'Projects' origin: 'Skunkworks' -tags: ['NLP', 'SEMANTIC SEARCH', 'SCRAPING','JAVASCRIPT','PYTHON'] +tags: ['NLP','JAVASCRIPT','PYTHON', 'WEBDEV'] --- ![Image of a browser showing the data lens search front end.](../images/data-lens.png) diff --git a/docs/our_work/data-linkage-hub/linkage-projects/better-matching.md b/docs/our_work/data-linkage-hub/linkage-projects/better-matching.md index 62a8b421..599f2de7 100644 --- a/docs/our_work/data-linkage-hub/linkage-projects/better-matching.md +++ b/docs/our_work/data-linkage-hub/linkage-projects/better-matching.md @@ -3,7 +3,7 @@ title: 'Probabilistic Linkage Model' summary: 'This project is creating a probabilistic linkage model using Splink, in order to improve linkage outcomes, and by extension, patient outcomes. The aim is for this to be used to link data in a range of NHS datasets.' category: 'Projects' origin: 'NHSD' -tags: ['LINKAGE', 'PYTHON', 'PROBABILISTIC MODEL'] +tags: ['LINKAGE', 'PYTHON', 'PII', 'WIP','STRUCTURED DATA'] --- ## Crafting a model that suits NHS England data linkage needs diff --git a/docs/our_work/data-linkage-hub/linkage-projects/cop.md b/docs/our_work/data-linkage-hub/linkage-projects/cop.md index 2e29ffdc..7f91fd5a 100644 --- a/docs/our_work/data-linkage-hub/linkage-projects/cop.md +++ b/docs/our_work/data-linkage-hub/linkage-projects/cop.md @@ -3,7 +3,7 @@ title: 'Data Linkage Community of Practice (DL CoP)' summary: 'We are creating and leading a community of practice to help people do the best linkage they can, with support from the data linkage team, but also from fellow analysts who are actively working on data linkage.' category: 'Projects' origin: 'NHSD' -tags: ['BEST PRACTICE','EXPLAINABILITY','LINKAGE'] +tags: ['BEST PRACTICE','EXPLAINABILITY','LINKAGE', 'WIP'] --- ## Why do we want a Community of Practice? diff --git a/docs/our_work/data-linkage-hub/linkage-projects/mps-handbook.md b/docs/our_work/data-linkage-hub/linkage-projects/mps-handbook.md index 519c918c..1d4937fa 100644 --- a/docs/our_work/data-linkage-hub/linkage-projects/mps-handbook.md +++ b/docs/our_work/data-linkage-hub/linkage-projects/mps-handbook.md @@ -3,7 +3,7 @@ title: 'MPS Documentation - the Person_ID handbook' summary: 'Documenting how the Person_ID is generated via the Master Person Service (MPS), to make the current process of linking data in the NHS more transparent and easy to understand.' category: 'Projects' origin: 'NHSD' -tags: ['BEST PRACTICE','EXPLAINABILITY','LINKAGE'] +tags: ['BEST PRACTICE','EXPLAINABILITY','LINKAGE', 'COMPLETE'] --- The Person_ID is a unique patient identifier used by NHS England with the objective of standardising the approach to patient-level data linkage across different data sets. diff --git a/docs/our_work/data-linkage-hub/linkage-projects/qaf.md b/docs/our_work/data-linkage-hub/linkage-projects/qaf.md index 456e84fc..b3e0294d 100644 --- a/docs/our_work/data-linkage-hub/linkage-projects/qaf.md +++ b/docs/our_work/data-linkage-hub/linkage-projects/qaf.md @@ -3,7 +3,7 @@ title: 'Quality Assurance Framework for Data Linkage' summary: 'This project aims to create, test, and distribute a quality assurance framework for data linkage to ensure robust, transparent and auditable results.' category: 'Projects' origin: 'NHSD' -tags: ['BEST PRACTICE','EXPLAINABILITY','LINKAGE'] +tags: ['BEST PRACTICE','EXPLAINABILITY','LINKAGE', 'WIP'] --- Data Linkage is a business-critical process within many government organisations, including NHS England. Research publications, official statistics, but also many direct care applications depend on data linkage. Its importance is further amplified when considering privacy preserving principles that require to minimise the use of patients' personal identifiable information. Consequently, data linkage is initiated early in the data lifecycle, establishing a substantial **reliance of downstream applications on the quality of the linkage process**. diff --git a/docs/our_work/ds251_RAG.md b/docs/our_work/ds251_RAG.md index c1390831..89b173b2 100644 --- a/docs/our_work/ds251_RAG.md +++ b/docs/our_work/ds251_RAG.md @@ -2,7 +2,7 @@ title: 'Retrieval Augmented Generation' summary: 'Investigating Advanced RAG, collaborating with efforts to evaluate LLM outputs.' origin: '' -tags: ['NLP','LLM','GENAI'] +tags: ['NLP','LLM'] --- diff --git a/docs/our_work/ds255_privacyfp.md b/docs/our_work/ds255_privacyfp.md index cfd79631..97983c01 100644 --- a/docs/our_work/ds255_privacyfp.md +++ b/docs/our_work/ds255_privacyfp.md @@ -1,7 +1,7 @@ --- title: "Building a Tool to Assess the Privacy Risk of Text Data - Extended" summary: Can we generate a modular tool to score the privacy risk of healthcare free-text data using open-source LLMs and NERs. -tags: ['TEXT DATA', 'LLM','PYTHON', 'PRIVACY', 'NAMED ENTITY RECOGNITION', 'UNSTRUCTURED DATA'] +tags: ['TEXT DATA', 'LLM','PYTHON', 'ETHICS', 'NLP', 'UNSTRUCTURED DATA', 'SYNTHETIC DATA'] --- !!! warning diff --git a/docs/our_work/index.md b/docs/our_work/index.md index 9ef852f1..4cbe980b 100644 --- a/docs/our_work/index.md +++ b/docs/our_work/index.md @@ -2,7 +2,7 @@

Explore our comprehensive portfolio of ongoing and completed projects that harness the power of data to drive insight.

- [Explore our projects by topic :fontawesome-solid-tags:](./tags.md){ .md-button } + [Explore our projects by categories & tags :fontawesome-solid-tags:](./tags.md){ .md-button } +

diff --git a/docs/our_work/long-stay-baseline.md b/docs/our_work/long-stay-baseline.md index d399710d..62b2615e 100644 --- a/docs/our_work/long-stay-baseline.md +++ b/docs/our_work/long-stay-baseline.md @@ -3,7 +3,7 @@ title: 'Long Stayer Risk Stratification Baseline Models' summary: 'Baseline machine learning models using historical data from Gloucestershire Hospitals NHS Foundation Trust to predict how long a patient will stay in hospital upon admission.' category: 'Projects' origin: 'Skunkworks' -tags: ['LOS','RISK MODEL', 'REGRESSION', 'CLASSIFICATION','PYTHON'] +tags: ['LOS', 'REGRESSION', 'CLASSIFICATION','PYTHON', 'SQL', 'F1', 'ACCURACY'] --- Long Stayer risk stratification baseline models was selected as a project to run in tandem with the [Long Stayer Risk Stratification](long-stay.md) project, and started in March 2022. diff --git a/docs/our_work/long-stay.md b/docs/our_work/long-stay.md index e4faf678..55cdcef1 100644 --- a/docs/our_work/long-stay.md +++ b/docs/our_work/long-stay.md @@ -3,7 +3,7 @@ title: 'Long Stayer Risk Stratification' summary: 'Machine learning using historical data from Gloucestershire Hospitals NHS Foundation Trust to predict how long a patient will stay in hospital upon admission.' category: 'Projects' origin: 'Skunkworks' -tags: ['LOS','NEURAL NETWORKS','RISK MODEL','PYTHON', 'GAN'] +tags: ['LOS','NEURAL NETWORKS','PYTHON', 'SQL', 'WEBDEV', 'STRUCTURED DATA', 'ACCURACY'] --- ![browser with the Long Stayer Risk Stratification dashboard showing a zoom in to the current risk stratification score currently at: Level 2](../images/long-stay.png) diff --git a/docs/our_work/nhs-resolution.md b/docs/our_work/nhs-resolution.md index a60ead6b..5c4c80d2 100644 --- a/docs/our_work/nhs-resolution.md +++ b/docs/our_work/nhs-resolution.md @@ -3,7 +3,7 @@ title: 'Predicting negligence claims with NHS Resolution' summary: 'This project investigated whether it is possible to use machine learning AI to predict the number of claims a trust is likely to receive and learn what drives them in order to improve safety for patients.' category: 'Projects' origin: 'Skunkworks' -tags: ['CLASSIFICATION','PREDICTION', 'AI'] +tags: ['CLASSIFICATION','FORECASTING'] ---

diff --git a/docs/our_work/nursing-placement-optimisation.md b/docs/our_work/nursing-placement-optimisation.md index 9c1d221f..f7db6421 100644 --- a/docs/our_work/nursing-placement-optimisation.md +++ b/docs/our_work/nursing-placement-optimisation.md @@ -3,7 +3,7 @@ title: 'Nursing Placement Schedule Optimisation Tool' summary: 'Optimisation problem developed with Imperial College Healthcare Trust to produce optimised schedules for student nurses going on placement within the trust.' category: 'Projects' origin: 'Skunkworks' -tags: ['OPTIMISATION','GENETIC ALGORITHM', 'PYTHON'] +tags: ['PYTHON'] --- This project is an example of the AI Skunkworks team offering capability resources to produce proof-of-concepts which could be applicable to the NHS at large. The project ran from January 2022 to May 2022. diff --git a/docs/our_work/open-safely.md b/docs/our_work/open-safely.md index c60f684c..781ce2da 100644 --- a/docs/our_work/open-safely.md +++ b/docs/our_work/open-safely.md @@ -3,7 +3,7 @@ title: 'Working with a Trusted Research Environment - the NHS @Home programme' summary: 'An exploration of OpenSafely' category: 'Projects' origin: 'Skunkworks' -tags: ['TRE', 'PYTHON', 'OPENSAFELY'] +tags: ['PYTHON', 'OPENSAFELY'] --- OpenSAFELY gives trusted researchers restricted levels of access to the server to run analysis on real data and obtain aggregate results, without having sight of the patient level data. Aggregate results are checked to ensure there are no disclosure risks before being released from the server. This highly secure way of working enables researchers to have access to large and sensitive datasets in a safe manner. diff --git a/docs/our_work/p11_synpathdiabetes.md b/docs/our_work/p11_synpathdiabetes.md index ee95f37f..be6d99c6 100644 --- a/docs/our_work/p11_synpathdiabetes.md +++ b/docs/our_work/p11_synpathdiabetes.md @@ -3,7 +3,7 @@ layout: base title: Applying our SynPath Simulator to a Diabetes Pathway summary: Exploration work into incorporating learning into a pathway simulator for diabetes. This work has fed our current SynPathGo project to create synthetic patient pathways and a foundation for agent based modelling in the NHS. permalink: p11_synpathdiabetes.html -tags: ['SIMULATION'] +tags: ['SIMULATION', 'SYNTHETIC DATA'] ---
diff --git a/docs/our_work/p12_synthvae.md b/docs/our_work/p12_synthvae.md index 1d47671a..d50666b0 100644 --- a/docs/our_work/p12_synthvae.md +++ b/docs/our_work/p12_synthvae.md @@ -3,7 +3,7 @@ layout: base summary: The initial creation of a variational autoencoder with differential privacy for generating single table tabular gaussian data. This work demonstrated the feasibility of this approach for healthcare and fed into further interactions of the code base. title: Investigating Differential Privacy in a Variational AutoEncoder for Synthetic Data Generation permalink: p12_synthvae.html -tags: ['VAE', 'SYNTHETIC DATA', 'PYTHON'] +tags: ['NEURAL NETWORKS', 'SYNTHETIC DATA', 'PYTHON'] --- ![Diagram showing the flow of data in a variational autoencoder. Starts with an input labeled as "x" on the left, which is passed through an NN arrow. This encoder outputs two posterior parameters: μ and variance σ². These are then used to sample a latent variable "z" from a normal distribution z ~ N(μ, σ²). "z" is passed to an NN decoder, which reconstructs the input, producing the reconstruction labeled as "x̂" on the right.](../images/vae.png) diff --git a/docs/our_work/p14_mcr.md b/docs/our_work/p14_mcr.md index 0f77d092..9415645a 100644 --- a/docs/our_work/p14_mcr.md +++ b/docs/our_work/p14_mcr.md @@ -3,7 +3,7 @@ layout: base title: Using Model Class Reliance to Understand the Impact of Commerical Data on Predictions permalink: p14_mcr.html summary: How to assess the value that commercial sales data of over-the-counter prescriptions has on respiratory death predictions -tags: ['MCR', 'PYTHON', 'MORTALITY', 'RESPIRATORY'] +tags: ['MCR', 'PYTHON', 'MORTALITY', 'RESPIRATORY', 'RANDOM FOREST'] --- The primary aim of the project was to apply the novel variable importance technique, [model class reliance](https://papers.nips.cc/paper/2020/hash/fd512441a1a791770a6fa573d688bff5-Abstract.html), to machine learning models which could predict registered respiratory deaths in the UK. The objective was to assess the value of commercial health data in healthcare predictions compared to other available datasets. diff --git a/docs/our_work/p21_synthvae.md b/docs/our_work/p21_synthvae.md index 81a329c9..a414611d 100644 --- a/docs/our_work/p21_synthvae.md +++ b/docs/our_work/p21_synthvae.md @@ -3,7 +3,7 @@ layout: base title: Developing our SynthVAE code permalink: p21_synthvae2.html summary: Improving our variational autoencoder to consider fairness and to run on non-gaussian distributions -tags: ['VAE', 'PYTHON', 'GAUSSIAN MIXTURE MODEL', 'DAG'] +tags: ['NEURAL NETWORKS', 'PYTHON', 'SYNTHETIC DATA'] --- ![DAG showing the relationships between variables that may influence job opportunity. Five nodes labeled as Prior Experience, Qualifications, Networking, Gender, and Job Opportunity. Prior Experience, Qualifications, Networking, and Gender all have arrows pointing toward Job Opportunity. Additionally, Networking is influenced by Prior Experience, and Gender influences Networking.](../images/dag_job_opportunity.png) diff --git a/docs/our_work/p22_txtrayalign.md b/docs/our_work/p22_txtrayalign.md index edf892d9..39c6df72 100644 --- a/docs/our_work/p22_txtrayalign.md +++ b/docs/our_work/p22_txtrayalign.md @@ -3,7 +3,7 @@ layout: base title: Descriptive text from X-Ray Images (TxtRayAlign) permalink: p22_txtrayalign.html summary: Generating descriptive text from X-Ray images using contrastive learning on multi-modal data -tags: ['NLP', 'MULTI-MODAL', 'RETRIEVAL', 'CONTRASTIVE-LEARNING', 'PYTHON'] +tags: ['NLP', 'MULTI-MODAL', 'RETRIEVAL', 'DEEP LEARNING', 'PYTHON'] --- ![](../images/p22fig1.png) diff --git a/docs/our_work/p23_stm.md b/docs/our_work/p23_stm.md index 874c735c..43abaf2a 100644 --- a/docs/our_work/p23_stm.md +++ b/docs/our_work/p23_stm.md @@ -3,7 +3,7 @@ layout: base title: Text Analysis using Structural Topic Modelling permalink: p23_stm.html summary: An open reusable tool for topic modelling of survey responses -tags: ['NLP', 'STRUCTURAL TOPIC MODELLING', 'R'] +tags: ['NLP', 'R'] --- ![Screenshot of the STM Insights dashboard, top left has an interopic distance map, top left includes a graph for most relevant terms, beneath are various other low res graphs.](../images/stminsights_lowquality.png) diff --git a/docs/our_work/p31_txtrayalign2.md b/docs/our_work/p31_txtrayalign2.md index 81e59c80..ad88cd29 100644 --- a/docs/our_work/p31_txtrayalign2.md +++ b/docs/our_work/p31_txtrayalign2.md @@ -3,7 +3,7 @@ layout: base title: Adding a Clinical Focus to Evaluating Multi-Modal Data Representations permalink: p31_txtrayalign2.html summary: How to validate synthetic text generated from images for healthcare applications. -tags: ['GENERATION', 'NLP', 'MULTI-MODAL', 'PYTHON'] +tags: ['GENERATION', 'NLP', 'MULTI-MODAL', 'PYTHON', 'SYNTHETIC DATA'] --- ![Diagram of the proposed clinical workflow applications of ML to radiology, diagrams connected by arrows.](../images/p31fig1.png) diff --git a/docs/our_work/p32_phmdiabetes.md b/docs/our_work/p32_phmdiabetes.md index 74d508b9..25aa2194 100644 --- a/docs/our_work/p32_phmdiabetes.md +++ b/docs/our_work/p32_phmdiabetes.md @@ -3,7 +3,7 @@ layout: base title: Inequalities in Diabetes from PHM Data permalink: p32_phmdiabetes.html summary: How to extract inequalities information from linked population health management data -tags: ['LOGISTIC REGRESSION', 'CATBOOST', 'PYTHON'] +tags: ['MACHINE LEARNING', 'PYTHON'] --- ![Diagram with on the left a box containing the texts: "GP Services & Registration", "Deprivation IoD by LSOA", "Open Street Maps", "Postcode to Lat. Long", "Postcode to LSOA", "Population Demographics", and "Quality & Outcomes (QOF), each with a cartoon reflecting it next to it. This box points to "Process from source" which in turns points to "Save to remote host" and "Aggregate by GP and LSOA" which points to "Downstream analysis..." and a colored map. ](../images/p32fig1.png) diff --git a/docs/our_work/p33_patientsafetylms.md b/docs/our_work/p33_patientsafetylms.md index 516b0f26..8ef62217 100644 --- a/docs/our_work/p33_patientsafetylms.md +++ b/docs/our_work/p33_patientsafetylms.md @@ -3,7 +3,7 @@ layout: base title: Investigating Applying and Evaluating a Language Model to Patient Safety Data permalink: p33_patientsafetylms.html summary: What's the most suitable models and workflows for representing an NHS text dataset? -tags: ['PYTHON', 'NLP', 'LLM', 'PYTORCH'] +tags: ['PYTHON', 'NLP', 'LLM'] --- In collaboration with the NHS England patient safety data team, we present an exploration of a selection of different language model pretraining and finetuning objectives with patient safety incident reports as the domain of interest, followed by a discussion of a number of methods for probing and evaluating these new models, and their respective embedding spaces. diff --git a/docs/our_work/ratings-and-reviews.md b/docs/our_work/ratings-and-reviews.md index c2017929..69dff7bc 100644 --- a/docs/our_work/ratings-and-reviews.md +++ b/docs/our_work/ratings-and-reviews.md @@ -2,7 +2,7 @@ title: "NHS.UK: Automatic Moderation of Ratings & Reviews" summary: "Using NLP to reduce the average moderation time on the NHS.UK website from days to seconds " origin: "" -tags: ["NLP", "AI", "ML", "NHS.UK"] +tags: ["NLP", "AI", "MACHINE LEARNING", "NHS.UK"] --- The NHS.UK website receives around a hundred thousand reviews every year. These reviews need moderating -- there's [a set of NHS policies which need to be applied to these](https://www.nhs.uk/our-policies/comments-policy/#:~:text=Users%20should%20only%20post%20one,service%20will%20not%20be%20published.) before they can be published. diff --git a/docs/our_work/renal-health-prediction.md b/docs/our_work/renal-health-prediction.md index 84b1e734..9072bd93 100644 --- a/docs/our_work/renal-health-prediction.md +++ b/docs/our_work/renal-health-prediction.md @@ -3,7 +3,7 @@ title: 'Renal Health Prediction' summary: 'Identifying acute kidney injury (AKI) patients at higher risk of requiring ITU, needing renal support (dialysis), or likely to have a higher potential for mortality.' category: 'Projects' origin: 'Skunkworks' -tags: ['AKI','RNN','DEEP LEARNING', 'TIME SERIES', 'NEURAL NETWORKS', 'PYTHON'] +tags: ['AKI','DEEP LEARNING', 'TIME SERIES', 'NEURAL NETWORKS', 'PYTHON'] --- ![diagram showing the renal health prediction workflow. At the top there are three boxes that say "electronic observations", "medication data", and "blood test results". All three boxes flow into a box that says "time series data builders (with missing data handling)", which flows into "Machine learning model". This then flows out into a box that says "Time series prediction of: no change, ITU bed, dialysis, Death. At least 24h in advance."](../images/renal-health-prediction.png) diff --git a/docs/our_work/sde_data_validation.md b/docs/our_work/sde_data_validation.md index 79f2d63b..7aeb26ab 100644 --- a/docs/our_work/sde_data_validation.md +++ b/docs/our_work/sde_data_validation.md @@ -2,7 +2,7 @@ title: 'Reusable New Data Product Validation Functions' summary: 'More Efficient, More Consistent Data ​Through Shared Validation Functions' origin: 'NHS England Secure Data Environment Service Data Wranglers' -tags: ['DATA WRANGLERS', 'NHSE_SDE', 'SDE', 'DATA VALIDATION', 'RAP', 'PYTHON'] +tags: ['DATA VALIDATION', 'RAP', 'PYTHON'] --- ![An image showing a stack of boxes on the left and a single box with robotic legs on the right. The stack of boxes has a label "old validation process" along with titles on boxes such as "code not shared", "inconsistent approach", "unreliable" and "manual process". Above the boxes it says "3 days". Next to the boxes an unhappy man is struggling to move them. To the right is a single box with robotic legs, with a happy looking man stood next to it. The box with robotic legs is labeled "new validation process" and has words nearby such as "reusable code", "consistent process" and "easy to re-run". Above the box is a label stating it takes about 30 minutes.](../images/sde_resuable_data_validation_functions.png) diff --git a/docs/our_work/swpclab.md b/docs/our_work/swpclab.md index 4a76465b..adf04cf9 100644 --- a/docs/our_work/swpclab.md +++ b/docs/our_work/swpclab.md @@ -3,7 +3,7 @@ title: "Developing Artificial Primary Care Records" summary: "Creating an artificial longitudinal patient dataset for GPs to trial new technologies in an artificial GP environment." category: "Playbook" origin: "" -tags: ["GENERATION","SYNTHETIC DATA","PYTHON","PRIMARY CARE"] +tags: ["GENERATION","SYNTHETIC DATA","PYTHON","PRIMARY CARE", 'SIMULATION'] --- Primary care records are crucial for understanding healthcare interactions at both the population and individual levels. However, these records are difficult to obtain and integrate with other services, hindering innovation due to data unavailability and privacy concerns. diff --git a/docs/our_work/synthetic-data-pipeline.md b/docs/our_work/synthetic-data-pipeline.md index ad8562b5..11e24bc9 100644 --- a/docs/our_work/synthetic-data-pipeline.md +++ b/docs/our_work/synthetic-data-pipeline.md @@ -3,7 +3,7 @@ title: 'Synthetic Data Generation Pipeline' summary: 'Exploring how to create mock patient data from real patient data.' category: 'Playbook' origin: 'Skunkworks' -tags: ['SYNTHETIC DATA','VAE','PRIVACY','KEDRO', 'MIMIC', 'PYTHON'] +tags: ['SYNTHETIC DATA','NEURAL NETWORKS','ETHICS','MIMIC', 'PYTHON'] --- diff --git a/docs/our_work/tags.md b/docs/our_work/tags.md index 6d12c051..cb9a89e9 100644 --- a/docs/our_work/tags.md +++ b/docs/our_work/tags.md @@ -1 +1,91 @@ -# Projects By Topic \ No newline at end of file +# Project Tags + +Below the tags are sorted by **categories** (Project Status, Data Sources, Domain Areas, Techniques, Coding Language, Data Types, Evaluation Metrics, and Project Type), however if you would like an **alphabetical list** of all of the tags it can be found [here](#accuracy) + +
+ +- :material-domain:{ .lg .middle } __Domain Areas__ + + --- + + * [Primary care](#primary-care) + * [Secondary care](#secondary-care) + * [Social Care](#social-care) + * [Emergency care](#emergency-care) + * [Diseases](#diseases) + * [Public/Population Health](#population-health) + * [Workforce & Allocation](#workforce) + * [Prescribing](#prescribing) + * [Financial](#financial) + +- :octicons-ai-model-16:{ .lg .middle } __Techniques__ + + --- + + * [Forecasting](#forecasting) + * [Classification](#classification) + * [Computer Vision](#computer-vision) + * [Deep Learning](#deep-learning) + * [Large Language Models (LLM)](#llm) + * [Machine Learning](#machine-learning) + * [Neural Networks](#neural-networks) + * [Natural Language Processing (NLP)](#nlp) + * [Linkage](#linkage) + * [Simulation](#simulation) + +- :material-projector-screen:{ .lg .middle } __Project Type (exploratory, modeling, etc.)__ + + --- + + * [Best Practice Guidance](#best-practice) + * [Data Validation](#data-validation) + * [Documentation](#documentation) + * [Ethics](#ethics) + * [Explainability](#explainability) + * [Generation](#generation) + * [Linkage](#linkage) + * [Modelling](#modelling) + * [Reproducible Analytical Pipeline (RAP)](#rap) + * [Research](#research) + +- :octicons-database-16:{ .lg .middle } __Data Types__ + + --- + + **Structure** + + * [Structured](#structured-data) + * [Unstructured](#unstructured-data) + + **Data modality** + + * [Multi-modal](#multi-modal) + * [Sound/Audio](#audio-data) + * [Genomics](#genomics-data) + * [Video/Image](#visual-data) + * [Text Data](#text-data) + * [Time Series](#time-series) + + **Data Accessibility/Privacy** + + * [Patient Identifiable](#pii) + * [Synthetic](#synthetic-data) + * [Open Data](#open-data) + +- :material-language-python:{ .lg .middle } __Coding Language__ + + --- + * [Python](#python) + * [SQL](#sql) + * [R](#r) + * [Web Development (e.g. Javascript, HTML, CSS)](#webdev) + +- :material-list-status:{ .lg .middle } __Status__ + + --- + + * [Work in Progress](#wip) + * [Complete](#complete) + * [On Pause](#paused) + +
diff --git a/overrides/home.html b/overrides/home.html index eda18688..dbe87b8b 100644 --- a/overrides/home.html +++ b/overrides/home.html @@ -433,8 +433,7 @@

A showcase of some of our projects!