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Susheel Varma
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Oct 20, 2024
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| "modified": "2023-05-30 08:41:52.053000", | ||
| "description": "This project comprises a set of R packages to assist in epidemiological studies using electronic health records databases.\n\nCALIBER (http://caliberresearch.org/) is led from the Farr Institute @ London. CALIBER investigators represent a collaboration between epidemiologists, clinicians, statisticians, health informaticians and computer scientists with initial funding from the Wellcome Trust and the National Institute for Health Research.\n\nThe goal of CALIBER is to provide evidence across different stages of translation, from discovery, through evaluation to implementation where electronic health records provide new scientific opportunities.\n", | ||
| "license": "GNU General Public License (GPL)", | ||
| "views": 394, | ||
| "views": 395, | ||
| "category": "Package", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -567,7 +567,7 @@ | |
| "modified": "2023-05-30 15:25:06.543000", | ||
| "description": "SAIL stands for Secure Anonymised Information Linkage. The SAIL Databank is a world-class flagship for the robust secure storage and use of anonymised person-based data for research to improve health, well-being and services. Its databank of anonymised data about the population of Wales is internationally recognised.\u00a0SAIL also provides trusted research environment (TRE) services to national programmes and hubs, such as BREATHE, the Health Data Research Hub for Respiratory Health.", | ||
| "license": "", | ||
| "views": 379, | ||
| "views": 380, | ||
| "category": "Platform", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -1150,7 +1150,7 @@ | |
| "modified": "2023-05-24 04:36:06.195000", | ||
| "description": "OpenRefine (previously Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.\n\nTool for cleaning and transforming data\n\nOpenRefine keeps your data private on your own computer by running a small server on your computer and you use your web browser to interact with it. OpenRefine is available in more than 15 languages. OpenRefine is part of Code for Science & Society.", | ||
| "license": "Apache License 2.0", | ||
| "views": 35, | ||
| "views": 36, | ||
| "category": "Application", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -1221,7 +1221,7 @@ | |
| "modified": "2023-05-15 14:30:20.012000", | ||
| "description": "DataPreparator is a free software tool designed to assist with common tasks of data preparation (or data preprocessing) in data analysis and data mining. \n\nDataPreparator provides:\n\n-A variety of techniques for data cleaning, transformation, and exploration\n-Chaining of preprocessing operators into a flow graph (operator tree)\n-Handling of large volumes of data (since data sets are not stored in the computer memory)\n-Stand alone tool independent of any other tools\n-User friendly graphical user interface\n\nDataPreparator can assist you with exploring and preparing data in various ways prior to data analysis or data mining. It includes operators for cleaning, discretization, numeration, scaling, attribute selection, missing values, outliers, statistics, visualization, balancing, sampling, row selection, and several other tasks. See Features for details.", | ||
| "license": "https://www.datapreparator.com/software-license-agreement-2.html", | ||
| "views": 42, | ||
| "views": 43, | ||
| "category": "Software", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -1611,7 +1611,7 @@ | |
| "modified": "2023-03-29 14:14:58.253000", | ||
| "description": "The RADAR-base platform is a scalable and inter-operable mHealth platform which provides capabilities for remote monitoring using passively (e.g. sensor data, wearables, IoT) and actively (e.g. questionnaires, digital tests). The platform developed at King's College London is already being used in a number of large-scale longitudinal mental and physical health-related disorder projects (see https://radar-base.org and https://github.com/RADAR-base/). The complete RADAR-base technology stack is available under an Apache 2 open source license and is supported by an active community of developers, researchers and clinicians who focus on continuously improving data quality, user experience, validation and extending the platform with new features and data sources.", | ||
| "license": "Apache License 2.0", | ||
| "views": 43, | ||
| "views": 44, | ||
| "category": "Platform", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -1842,7 +1842,7 @@ | |
| "modified": "2023-04-13 22:04:40.578000", | ||
| "description": "SemEHR is a text mining and semantic search system designed for Surfacing Semantic Data from Clinical Notes in Electronic Health Records for Tailored Care, Trial Recruitment and Clinical Research.\n\n### Results & Insights\n\nBuilt upon off-the-shelf toolkits including a Natural Language Processing (NLP) pipeline (Bio-Yodie) and an enterprise search system (CogStack), SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualised mentions of a wide range of biomedical concepts from unstructured clinical notes. Its IE functionality features an adaptive and iterative NLP mechanism where specific requirements and fine-tuning can be fulfilled and realised on a study basis. NLP annotations are further assembled at patient level and extended with clinical and EHR-specific knowledge to populate a panorama for each patient, which comprises a) longitudinal semantic data views and b) structured medical profile(s). The semantic data is serviced via ontology-based search and analytics interfaces to facilitate clinical studies.", | ||
| "license": "Apache License 2.0", | ||
| "views": 37, | ||
| "views": 38, | ||
| "category": "NLP System", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -2938,7 +2938,7 @@ | |
| "modified": "2022-11-06 21:59:11.736000", | ||
| "description": "Application to extract occurrences where visual hallucination is present. Visual hallucinations may be due to a.\n\nBoth past and present symptom.\n\nOutput values: Positive, negative and unknown.\n\n### Results & Insights\n\nFor further details please visit the CRIS link provided", | ||
| "license": "Available upon request", | ||
| "views": 21, | ||
| "views": 22, | ||
| "category": "NLP System", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -4198,7 +4198,7 @@ | |
| "modified": "2023-04-30 08:06:36.974000", | ||
| "description": "Application to identify instances of coughing.\n\nBoth past and present symptom.\n\nOutput values: Positive, Negative and Unknown.\n\n### Results & Insights\n\nFor further details please visit the CRIS link provided", | ||
| "license": "Available upon request", | ||
| "views": 23, | ||
| "views": 24, | ||
| "category": "NLP System", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -6065,7 +6065,7 @@ | |
| "modified": "2023-05-07 21:23:07.261000", | ||
| "description": "Application to extract a when a Diagnosis of Epilepsy was made. Output Values: CUI (of Epileptic Syndrome) and Age (Units, Upper and Lower Limit).\n\n### Results & Insights\n\nResults of version 1 are available here - https://bmjopen.bmj.com/content/9/4/e023232", | ||
| "license": "", | ||
| "views": 55, | ||
| "views": 56, | ||
| "category": "NLP System", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -6100,7 +6100,7 @@ | |
| "modified": "2023-01-30 10:56:07.665000", | ||
| "description": "Application to extract a when a Diagnosis of Epilepsy was made. Output Values: CUI (of Epileptic Syndrome) and Date (Day, Month, Year). \n\n### Results & Insights\n\nResults of version 1 are available here - https://bmjopen.bmj.com/content/9/4/e023232", | ||
| "license": "", | ||
| "views": 66, | ||
| "views": 67, | ||
| "category": "NLP System", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -6133,7 +6133,7 @@ | |
| "modified": "2023-05-31 08:05:54.372000", | ||
| "description": "A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.", | ||
| "license": "GPLv3", | ||
| "views": 135, | ||
| "views": 136, | ||
| "category": "Software", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -6274,7 +6274,7 @@ | |
| "modified": "2023-03-17 04:21:51.240000", | ||
| "description": "This interactive visualizations report focus on exploring how the risk of precarious work has evolved in the COVID-19 UK from a gender, ethnicity, and class perpective.\n\nThe code of this dashboard is available here: https://github.com/luistorresr/gender_covid_uk/tree/main/Outputs/Report_2_precariousness\n\nThis tool is part of the research project \"How is COVID-19 impacting women and men's working lives in the UK?\" which is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20029).\n\n### Results & Insights\n\nThis is a report written in R Markdown for interactive data visualizations. Reports are produced as a word document and as an html file that can be uploded to a web hosting.\n\nVisualization can be adapted to create dashboards and monitor seudo-longitudinal trends.", | ||
| "license": "MIT license", | ||
| "views": 76, | ||
| "views": 77, | ||
| "category": "Data Visualisation", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -6318,7 +6318,7 @@ | |
| "modified": "2023-05-19 10:47:41.492000", | ||
| "description": "This interactive visualizations report focus on exploring how self-employment has changed since the COVID-19 pandemic in the UK\n\nThe code of this dashboard is available here: https://github.com/luistorresr/gender_covid_uk/tree/main/Outputs/Report_3_selfemployment\n\nThis tool is part of the research project \"How is COVID-19 impacting women and men's working lives in the UK?\" which is part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20029).\n\n### Results & Insights\n\nThis is a report written in R Markdown for interactive data visualizations. Reports are produced as a word document and as an html file that can be uploded to a web hosting.\n\nVisualization can be adapted to create dashboards and monitor seudo-longitudinal trends.", | ||
| "license": "MIT license", | ||
| "views": 130, | ||
| "views": 131, | ||
| "category": "Data Visualisation", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -6361,7 +6361,7 @@ | |
| "modified": "2023-05-24 12:59:59.863000", | ||
| "description": "Infographic summarising results of research study\n\n### Results & Insights\n\n RESULTS: The study found increased risk of thrombocytopenia after ChAdOx1 nCoV-19 vaccination (incidence rate ratio 1.33, 95% confidence interval 1.19 to 1.47 at 8-14 days) and after a positive SARS-CoV-2 test (5.27, 4.34 to 6.40 at 8-14 days); increased risk of venous thromboembolism after ChAdOx1 nCoV-19 vaccination (1.10, 1.02 to 1.18 at 8-14 days) and after SARS-CoV-2 infection (13.86, 12.76 to 15.05 at 8-14 days); and increased risk of arterial thromboembolism after BNT162b2 mRNA vaccination (1.06, 1.01 to 1.10 at 15-21 days) and after SARS-CoV-2 infection (2.02, 1.82 to 2.24 at 15-21 days). Secondary analyses found increased risk of CVST after ChAdOx1 nCoV-19 vaccination (4.01, 2.08 to 7.71 at 8-14 days), after BNT162b2 mRNA vaccination (3.58, 1.39 to 9.27 at 15-21 days), and after a positive SARS-CoV-2 test; increased risk of ischaemic stroke after BNT162b2 mRNA vaccination (1.12, 1.04 to 1.20 at 15-21 days) and after a positive SARS-CoV-2 test; and increased risk of other rare arterial thrombotic events after ChAdOx1 nCoV-19 vaccination (1.21, 1.02 to 1.43 at 8-14 days) and after a positive SARS-CoV-2 test. CONCLUSION: Increased risks of haematological and vascular events that led to hospital admission or death were observed for short time intervals after first doses of the ChAdOx1 nCoV-19 and BNT162b2 mRNA vaccines. The risks of most of these events were substantially higher and more prolonged after SARS-CoV-2 infection than after vaccination in the same population.", | ||
| "license": "CC BY NC 3.0", | ||
| "views": 148, | ||
| "views": 149, | ||
| "category": "infographic", | ||
| "relations": [], | ||
| "keywords": [], | ||
|
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@@ -7028,7 +7028,7 @@ | |
| "modified": "2023-07-03 07:19:59.791000", | ||
| "description": "TREEHOOSE is an open source infrastructure-as-code Trusted Research Environment (TRE) freely available for use. It is built upon >10 years of TRE experience by the Health Informatics Centre - a Scottish regional Safe Haven - and uses modern cloud technology for flexibility, scalability and performance.\n\nTo find out more about TREEHOOSE see the DARE UK report: https://doi.org/10.5281/zenodo.7085504\n\nContact email: [email protected]", | ||
| "license": "Apache License 2.0", | ||
| "views": 134, | ||
| "views": 135, | ||
| "category": "Platform", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -7198,7 +7198,7 @@ | |
| "modified": "2024-08-22 07:20:20.279000", | ||
| "description": "The Data Safe Haven is an open-source infrastructure-as-code Trusted Research Environment (TRE) that is freely available for use, adaptation and extension by anyone. It has been used since autumn 2018 for all projects at the Alan Turing Institute that handle sensitive data. It is built on the Microsoft Azure cloud which makes it easy to deploy and to scale according to your needs.\n\nTo find out more, look at the following resources:\n\n- Our GitHub repository: https://github.com/alan-turing-institute/data-safe-haven/\n- Our documentation: https://data-safe-haven.readthedocs.io/en/latest/\n- Our Slack workspace: https://turingdatasafehaven.slack.com/\n- Contact email: [email protected]", | ||
| "license": "BSD 3-Clause \"New\" or \"Revised\" license", | ||
| "views": 155, | ||
| "views": 157, | ||
| "category": "Platform", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -7268,7 +7268,7 @@ | |
| "modified": "2024-10-03 16:39:57.300000", | ||
| "description": "DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data.\n\nDataSHIELD follows privacy by design principles built into the computing infrastructure, analytic functionality, automated output checking and is designed to operate with best practice data governance for the use of senstive health data.\n\nAnalysts do not directly connect to DataSHIELD data sites, secure connection is only available through a DataSHIELD client by authorised analysts. Analysts do not view individual patient data, and it is not shared or moved from the data site.\n\nDataSHIELD analysis can be aggregated from individual patient data at each location or a global analysis can be conducted simultaneously at all sites without sharing or moving individual patient data.\n\nDataSHIELD analytic functions have been designed to only share non disclosive summary statistics, with built in automated output checking based on statistical disclosure control. Only data sites can set the threshold values for the automated output checks.\n\nDataSHIELD base package dsBase runs over 12,000 tests every software release. These tests run against synthetic datasets included in the package checking for correct operation, outputs and that disclosure control is not breached.\n\n### Results & Insights\n\nThere are over 200 data sites using DataSHIELD globally. These cover cohort studies, clinical trials, routine health care data, genomics data. A collection of the papers published using DataSHIELD is at https://papers.datashield.org", | ||
| "license": "GNU General Public License version 3.0 (GPLv3)", | ||
| "views": 7, | ||
| "views": 8, | ||
| "category": "Federated analysis software", | ||
| "relations": [], | ||
| "keywords": [ | ||
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