MICA: Mental Health Data Pathfinder: University of Cambridge, Cambridgeshire & Peterborough NHS Foundation Trust, and Microsoft
Lead Research Organisation:
University of Cambridge
Department Name: UNLISTED
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Technical Summary
With strong NHS partnerships and recent contributions to national mental health (MH) informatics, we shall add novel methods, epidemiology and phenotyping to the MH Platform. We envisage a modular pipeline that de-identifies MH data; supports flexible consent for
sharing/contact; and links MH, cognitive, physical, psychosocial and biomarker data.
Project (P) 1. Our open-source tools de-identify clinical records to create CPFT’s Research Database, supporting research and participation. We shall extend them to generate anonymised subsets and link data from consenting patients across MH/community services,
acute care, and research organizations, including from existing deeply phenotyped longitudinal cohorts. We emphasize rigorous interface standards and NHS governance over identifiable/pseudonymised data. We shall collaborate on a national natural language
processing framework, allowing NHS/research organizations to generate structured data from
free text.
P2. We have created novel open-source neuropsychiatric assessment software. We shall
extend it for broad and integrated NHS and research use. This will take automated cognitive testing into routine clinical practice. As a bold but tractable exemplar with research and clinical applications, we shall use it to apply electronic diagnostic algorithms and neuropsychiatric phenotyping, and link these detailed data to clinical records and biomarkers that include
immunophenotyping.
P3. We shall apply P1 tools to a public health crisis: the premature death of those with serious mental illness. We shall link MH, national and acute Trust data and use machine learning to develop early predictors of mortality.
P4. We shall democratize MH research though broad consultation on generic tiered consent models for data-sharing and participation, by giving the research database direct clinical interfaces, and by enhancing data visualization to help clinicians and service users develop research and the NHS improve local services.
sharing/contact; and links MH, cognitive, physical, psychosocial and biomarker data.
Project (P) 1. Our open-source tools de-identify clinical records to create CPFT’s Research Database, supporting research and participation. We shall extend them to generate anonymised subsets and link data from consenting patients across MH/community services,
acute care, and research organizations, including from existing deeply phenotyped longitudinal cohorts. We emphasize rigorous interface standards and NHS governance over identifiable/pseudonymised data. We shall collaborate on a national natural language
processing framework, allowing NHS/research organizations to generate structured data from
free text.
P2. We have created novel open-source neuropsychiatric assessment software. We shall
extend it for broad and integrated NHS and research use. This will take automated cognitive testing into routine clinical practice. As a bold but tractable exemplar with research and clinical applications, we shall use it to apply electronic diagnostic algorithms and neuropsychiatric phenotyping, and link these detailed data to clinical records and biomarkers that include
immunophenotyping.
P3. We shall apply P1 tools to a public health crisis: the premature death of those with serious mental illness. We shall link MH, national and acute Trust data and use machine learning to develop early predictors of mortality.
P4. We shall democratize MH research though broad consultation on generic tiered consent models for data-sharing and participation, by giving the research database direct clinical interfaces, and by enhancing data visualization to help clinicians and service users develop research and the NHS improve local services.
Organisations
- University of Cambridge (Lead Research Organisation)
- Kooth plc (Collaboration)
- Akrivia Health (Collaboration)
- University College London (Collaboration)
- Swansea University (Collaboration)
- Manchester University (Collaboration)
- McPin Foundation (Collaboration)
- Microsoft Research (Collaboration)
- Health Data Research UK (Collaboration)
- AIMES Grid Services Ltd (Collaboration)
- Cardiff University (Collaboration)
- UNIVERSITY OF GLASGOW (Collaboration)
- SAIL Databank (Collaboration)
- Queen's University Belfast (Collaboration)
- Bangor University (Collaboration)
- Huazhong University of Science and Technology (Collaboration)
- UNIVERSITY OF EDINBURGH (Collaboration)
- National Collaborating Centre for Mental Health (NCC MH) (Collaboration)
- King's College London (Collaboration)
- Psychiatric Genomics Consortium (PGC) (Collaboration)
- MQ Mental Health Research (Collaboration)
- University of Bristol (Collaboration)
Publications
Alabaf S
(2022)
Early versus late risk factors for deficit and nondeficit schizophrenia.
in Revista de psiquiatria y salud mental
Alabaf S
(2021)
Early versus late risk factors for deficit and nondeficit schizophrenia.
in Revista de psiquiatria y salud mental
Banerjee S
(2021)
A Human-Interpretable Machine Learning Approach to Predict Mortality in Severe Mental Illness
in SSRN Electronic Journal
Banerjee S
(2021)
A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness.
in NPJ schizophrenia
Banerjee S
(2022)
Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies.
in Patterns (New York, N.Y.)
Bhardwaj A
(2021)
Survey of CAMHS clinicians about their experience of remote consultation: brief report.
in BJPsych open
Burrin C
(2021)
Iatrogenic Complications of Compulsory Treatment in a Patient Presenting with an Emotionally Unstable Personality Disorder and Self-Harm
in Case Reports in Psychiatry
| Description | Case studies of best practice in mental health data science: 1. Creating a research database of de-identified NHS data |
| Geographic Reach | National |
| Policy Influence Type | Citation in other policy documents |
| URL | https://mhdss.ac.uk/case-studies |
| Description | Case studies of best practice in mental health data science: 2. Data linkage - linking health data with other sources of data |
| Geographic Reach | National |
| Policy Influence Type | Citation in other policy documents |
| URL | https://mhdss.ac.uk/case-studies |
| Description | Case studies of best practice in mental health data science: 3. Exploring questions of consent |
| Geographic Reach | National |
| Policy Influence Type | Citation in other policy documents |
| URL | https://mhdss.ac.uk/case-studies |
| Description | DATAMIND - Data Hub for Mental Health Informatics Research Development. |
| Amount | £2,000,000 (GBP) |
| Funding ID | MR/W014386/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2021 |
| End | 08/2024 |
| Description | FAIR TREATMENT: Federated analytics and AI Research across TREs for AdolescenT MENTal health |
| Amount | £342,708 (GBP) |
| Funding ID | MC_PC_21025 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2022 |
| End | 08/2022 |
| Description | NIHR Cambridge Biomedical Research Centre |
| Amount | £86,200,000 (GBP) |
| Funding ID | NIHR203312 |
| Organisation | National Institute for Health Research |
| Sector | Public |
| Country | United Kingdom |
| Start | 12/2022 |
| End | 11/2027 |
| Description | TIMELY: towards early identification of child and adolescent mental health problems |
| Amount | £251,302 (GBP) |
| Organisation | Alan Turing Institute |
| Sector | Academic/University |
| Country | United Kingdom |
| Start | 09/2021 |
| End | 03/2022 |
| Description | Towards early identification of adolescent mental health problems |
| Amount | £100,576 (GBP) |
| Funding ID | MR/T046430/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 05/2020 |
| End | 06/2021 |
| Description | Using deep learning approaches to examine serious mental illness and physical multimorbidity across the life-course: from mechanisms towards novel interventions |
| Amount | £117,422 (GBP) |
| Funding ID | NIHR202646 |
| Organisation | National Institute for Health Research |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2021 |
| End | 04/2022 |
| Description | What CAMHS interventions predict positive outcomes for which young people with a social worker: a mixed-methods study of clinical support and cost-effectiveness utilising linked operational data |
| Amount | £1,316,008 (GBP) |
| Organisation | National Institute for Health Research |
| Sector | Public |
| Country | United Kingdom |
| Start | 05/2022 |
| End | 05/2026 |
| Title | De-identified Bayesian personal identity matching for record linkage despite errors |
| Description | Technique for linking records about people (a) without a common person-unique identifier such as an NHS number, but ultimately using names, dates of birth, and so forth; and (b) in de-identified fashion, so none of those identifiers are visible at linkage (irreversibly encrypted versions are used). Currently under review but available as a preprint. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | A candidate method to replace some current national data linkage schemes using identifiable data (and NHS Act Section 251 approval), but peer review awaited. |
| URL | https://doi.org/10.21203/rs.3.rs-1929135/v1 |
| Title | Natural Language Processing Request Protocol (NLPRP) |
| Description | A software protocol to allow research sites to request natural language processing (NLP) tools, e.g. hosted elsewhere, to be run over text. Formal publication to follow, but is available and in use. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Impact | Contributes to research via the CPFT Research Database, https://www.cpft.nhs.uk/research-database |
| URL | https://crateanon.readthedocs.io/en/latest/nlp/nlprp.html |
| Title | Data for: Public opinion on sharing data from health services for clinical and research purposes without explicit consent: an anonymous online survey in the UK. |
| Description | Jones LA, Nelder JR, Fryer JM. Data for: Public opinion on sharing data from health services for clinical and research purposes without explicit consent: an anonymous online survey in the UK. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | National survey about NHS data use. Preprint at https://www.medrxiv.org/content/10.1101/2021.07.19.21260635v1. Data embargoed until publication. |
| URL | https://doi.org/10.17863/CAM.75784 |
| Title | The CPFT Clinical Data Linkage Service |
| Description | Linkage of NHS data from Cambridgeshire and Peterborough NHS Foundation Trust to a range of national data sources, under REC approvals and in some cases NHS Act section 251 approval via the CAG. These include (some in setup): (1) Hospital Episode Statistics, via NHS Digital. (2) Office for National Statistics Mortality data, via NHS Digital. (3) National Cancer Registry. (4) National Pupil Database. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | In progress. |
| URL | https://www.cpft.nhs.uk/research-database |
| Title | The CPFT Research Database |
| Description | This is a database for research and research recruitment created by de-identifying Cambridgeshire & Peterborough NHS FT (CPFT) clinical records. The current grant is enabling this to be developed further -- so far with enhanced natural language processing (NLP) tools, with other developments to follow. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2013 |
| Provided To Others? | No |
| Impact | Previous publications arising from this database. More to follow based on the extensions from this grant (but none of those yet). |
| URL | http://www.cpft.nhs.uk/research |
| Description | Cambridge / King's College London collaboration for MRC Mental Health Data pathfinder awards |
| Organisation | King's College London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | We have designed an application programming interface (API) for computerized natural language processing (NLP), suitable for a national NHS NLP platform. We have refined this with KCL and are now implementing it for use on KCL (+ South London & Maudsley NHS Foundation Trust) servers in the Microsoft Azure cloud. We have written NLP tools (e.g. for finding inflammatory markers within free text) that we have made open-source and will contribute to this platform. |
| Collaborator Contribution | KCL provide many other NLP tools relevant to psychiatry and server infrastructure. |
| Impact | Developments to our software at https://crateanon.readthedocs.io/ . |
| Start Year | 2018 |
| Description | DATAMIND consortium |
| Organisation | AIMES Grid Services Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Akrivia Health |
| Country | United Kingdom |
| Sector | Hospitals |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Bangor University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Cardiff University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Health Data Research UK |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | King's College London |
| Department | Institute of Psychiatry, Psychology & Neuroscience |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Kooth Plc |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | MQ Mental Health Research |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Manchester University |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | McPin Foundation |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | National Collaborating Centre for Mental Health (NCC MH) |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Psychiatric Genomics Consortium (PGC) |
| Country | Global |
| Sector | Learned Society |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Queen's University Belfast |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | SAIL Databank |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | Swansea University |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | University College London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | University of Bristol |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | University of Edinburgh |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | DATAMIND consortium |
| Organisation | University of Glasgow |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Cambridge contribution: leading patient/public involvement for DATAMIND, along with technical and epidemiological elements. |
| Collaborator Contribution | Other core activities (FAIR curated data, business development/sustainability, workforce capacity/training/development) and "road builder" projects. |
| Impact | See ResearchFish for MRC grant MR/W014386/1 |
| Start Year | 2021 |
| Description | Microsoft Research |
| Organisation | Microsoft Research |
| Department | Microsoft Research Cambridge |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | We aimed to bring our expertise in psychiatry and analysis of de-identified clinical data, plus machine learning expertise from the University, to a collaboration with Microsoft Research UK to improve the prediction of adverse outcomes (such as premature death) in schizophrenia. |
| Collaborator Contribution | Microsoft Research UK planned to provide sophisticated machine learning algorithms, suitable for being trained within a secure NHS environment on de-identified NHS clinical data, to predict outcomes in serious mental illness. The aim was that the trained algorithms could then be deployed elsewhere, without any direct transfer of data derived from NHS clinical records. |
| Impact | Microsoft withdrew citing lack of resources. |
| Start Year | 2018 |
| Description | Tongji Medical College of Huazhong University of Science and Technology |
| Organisation | Huazhong University of Science and Technology |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Give technical (especially statistical) support and help culturing student. |
| Collaborator Contribution | the team in Tongji Medical College provided de-identified data and increased our culture variability of research.GIv |
| Impact | Help culture a PhD student by half-year exchange in the University of Cambridge |
| Start Year | 2019 |
| Title | CRATE: Clinical Records Anonymisation and Text Extraction |
| Description | The CRATE package (1) de-identifies clinical records; (2) manages a natural language processing (NLP) pipeline, provides its own NLP tools to extract structured information from free text, and manages third-party NLP tools; (3) provides a research interface to arbitrary databases derived from clinical records; (4) provides a computerized consent-to-contact process operational within the NHS. In 2018 it has been extended to support an NLP API that we have designed in support of a national NHS NLP service (collaboration with KCL; q.v.). |
| Type Of Technology | Software |
| Year Produced | 2017 |
| Open Source License? | Yes |
| Impact | (1) Implementation of a research database across an NHS Trust (Cambridgeshire & Peterborough NHS FT; CPFT). In use internationally (e.g. Canada). (2) Patients recruited to studies via its consent-to-contact methods in CPFT. (3) Via MRC Pathfinder grant MC_PC_17213, supports subset anonymisation and generic linkage services. (4) Via MRC Pathfinder grant MC_PC_17213, supports Natural Language Processing Request Protocol as client and server. (4) Via MRC Pathfinder grant MC_PC_17213, provides a direct clinical interface. |
| URL | https://crateanon.readthedocs.io/ |
| Title | CamCOPS: the Cambridge Cognitive and Psychiatric Assessment Kit |
| Description | CamCOPS is an open-source application for capturing information relevant for cognitive and psychiatric assessment, on tablets, laptops, and desktops. It offers simple questionnaires and more complex tasks, and sends its data securely to your server. |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Open Source License? | Yes |
| Impact | (1) In use for preclinical and clinical research in the UK, Denmark, and Singapore. (2) In NHS clinical use (including CPFT Perinatal Psychiatry service). (3) Capable of integration with REDCap. (4) Single-patient mode (for home testing) piloting and due for launch in Q1-2 of 2021. (5) iOS version developed and currently being vetted by the Apple App Store. (6) New tasks added, including for immunopsychiatry studies. |
| URL | https://camcops.readthedocs.io/ |
| Title | Natural Language Processing Request Protocol (NLPRP) |
| Description | Protocol for cloud-based application of natural language processing (NLP) tools to create structured data from free text. Developed via MRC Mental Health Data Pathfinder grants to Cambridge and KCL. |
| Type Of Technology | Software |
| Year Produced | 2018 |
| Open Source License? | Yes |
| Impact | In use for natural language processing of de-identified NHS data (e.g. via Cambridgeshire & Peterborough NHS Foundation Trust, South London & Maudsley NHS Foundation Trust). |
| URL | https://crateanon.readthedocs.io/en/latest/nlp/nlprp.html |
| Title | PyCap v1.1.0 |
| Description | PyCap is a python module exposing the REDCap API through some helpful abstractions. Information about the REDCap project can be found at http://project-redcap.org/. PyCap v1.0 has DOI:10.5281/zenodo.9917 |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | We extended PyCap to support missing RedCAP API features, which will be of benefit to software developers wishing to integrate other python-based systems with RedCAP. These changes to PyCap enable tasks created in CamCOPS https://camcops.readthedocs.io/ to be exported to the REDCap platform. This allows data captured with CamCOPS to be integrated with existing REDCap records and analyzed/visualized with tools not available in CamCOPS. |
| URL | https://github.com/redcap-tools/PyCap/releases/tag/1.1.0 |
| Description | "Brainworks" public event, 1 Nov 2018 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Presentation on research databases (and their use for recruitment to research studies) derived from de-identified NHS mental health records at Cambridge Biomedical Research Centre event, "Brainworks". |
| Year(s) Of Engagement Activity | 2018 |
| Description | Engagement activity - "big data" illustration |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Public illustration of the importance of sample size in detecting risk factors, via interactive web-based simulation. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.climbproject.org.uk/big-data-illustration |
| Description | Engagement activity - classification via a dance mat game |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Game to illustrate patient selection and classification via a dance mat game. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.climbproject.org.uk/dance-mat |
| Description | Engagement activity - encapsulated machine learning demonstration of facial expression recognition |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Demonstration of "live" machine learning of facial expressions via self-taught webcam-using web page. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.climbproject.org.uk/machine-learning-webcam |
| Description | Patient engagement including development of a Research Advisory Group for NHS mental health data research in Cambridgeshire & Peterborough NHS FT |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Patients, carers and/or patient groups |
| Results and Impact | Ongoing work to create a local advisory group prior to national consultation. |
| Year(s) Of Engagement Activity | 2019 |
| Description | Survey of UK public opinion regarding the use of NHS data |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Anonymous survey of UK public opinion. Due for publication shortly. Recruited 29,275 consented participants. |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.isrctn.com/ISRCTN37444142 |