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Susheel Varma
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| "modified": "2023-05-28 22:40:37.701000", | ||
| "description": "The COVID Symptom Study app has been developed by King\u2019s College London and health science company ZOE, and it is endorsed by the Welsh Government, NHS Wales, the Scottish Government and NHS Scotland.\n\nMore than 2.5 million participants have downloaded the app and are using it to regularly report on their health in order to help stop COVID.", | ||
| "license": "", | ||
| "views": 392, | ||
| "views": 393, | ||
| "category": "Mobile Application", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -219,7 +219,7 @@ | |
| "modified": "2023-05-30 20:41:32.141000", | ||
| "description": "", | ||
| "license": "", | ||
| "views": 332, | ||
| "views": 333, | ||
| "category": "Data Modelling", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -310,7 +310,7 @@ | |
| "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": 389, | ||
| "views": 390, | ||
| "category": "Package", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -887,7 +887,7 @@ | |
| "modified": "2023-05-30 15:14:05.854000", | ||
| "description": "Scalable Data Anonymization Tool - supports multiple privacy models.\n\nARX is a comprehensive open source software for anonymizing sensitive personal data. It supports a wide variety of (1) privacy and risk models, (2) methods for transforming data and (3) methods for analyzing the usefulness of output data. It supports various anonymization techniques, methods for analyzing data quality and re-identification risks and it supports well-known privacy models, such as k-anonymity, l-diversity, t-closeness and differential privacy.\nARX is an open source tool for transforming structured (i.e. tabular) personal data using selected methods from the broad areas of data anonymization and statistical disclosure control. It supports transforming datasets in ways that make sure that they adhere to user-specified privacy models and risk thresholds that mitigate attacks that may lead to privacy breaches. ARX can be used to remove direct identifiers (e.g. names) from datasets and to enforce further constraints on indirect identifiers. Indirect identifiers (or quasi-identifiers, or keys) are attributes that do not directly identify an individual but may together with other indirect identifiers form an identifier that can be used for linkage attacks. It is typically assumed that information about indirect identifiers is available to the attacker (in some form of background knowledge) and that they cannot simply be removed from the dataset (e.g. because they are required later for analyses).", | ||
| "license": "Apache License 2.0", | ||
| "views": 552, | ||
| "views": 553, | ||
| "category": "Data Anonymization Tool", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -947,7 +947,7 @@ | |
| "modified": "2023-01-02 09:37:28.311000", | ||
| "description": "Data profiling and analysis tool - User Ratings: 4.8/5\nAlso known as Open Source Data Quality and Profiling. Features Mysql, Oracle,Postgres,Access,Db2,SQL Server certified Big data support - HIVE Format Creation, Format Matching ( Phone, Date, String and Number), Format standardization Fuzzy Logic based similarity check, Cardinailty check between tables and files Export and import from XML, XLS or CSV format, PDF export File Analysis, Regex search, Standardization, DB search Complete DB Scan, SQL interface, Data Dictionary, Schema Comparison Statistical Analysis, Reporting ( dimension and measure based), Ad Hoc reports and Analytics Pattern Matching , DeDuplication, Case matching, Basket Analysis, Distribution Chart Data generation and Data masking features Meta Data Information, Reverse engineering of Data Model Timeliness analysis , String length analysis Address Correction, Single View of Customer, Product, Golden merge for records Record Match, Linkage and Merge added based on fuzzy logic.", | ||
| "license": "GNU General Public License version 3.0 (GPLv3)", | ||
| "views": 60, | ||
| "views": 61, | ||
| "category": "Data Profiling/Analysis Tool", | ||
| "relations": [], | ||
| "keywords": [ | ||
|
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@@ -5026,7 +5026,7 @@ | |
| "modified": "2023-05-23 13:16:46.141000", | ||
| "description": "An opensource platform that provides researchers with easy access to deploy collaborative workspace environments in the AWS Cloud, through a simple to use web interface. Offers the ability to operate across teams, universities, and datasets while enabling Research IT stakeholders to manage, monitor, and control spending, apply security best practices and comply with corporate governance.\n\nService Workbench targets Research workloads with out of the box service support for:\n\n JupyterLabs via Amazon SageMaker with Python Kernels\n Jupyter Notebooks on Amazon EMR + Hail v0.2\n RStudio on EC2\n Windows VM on Amazon EC2\n Linux VM on EC2\n\nAdditional product configuration can easily be added to Service Workbench through Service Catalog and be linked to researcher, institutional, and includes support for [AWS Open Data](https://registry.opendata.aws) data sets configured by Research IT for their researchers use across the institution.\n\nPlatform provides one-click option to admins for easier creation (vending) of new AWS accounts specific to researchers' teams for easier governance.\n\nThe code in the GitHub repo is fully functional but is intended as a stand-a-lone evaluation deployment and some foundational AWS knowledge is assumed.\n\nPlease contact the health research team at AWS for further information and support: [[email protected]](mailto:[email protected]); [[email protected]](mailto:[email protected]); [[email protected]](mailto:[email protected])", | ||
| "license": "Apache License 2.0", | ||
| "views": 477, | ||
| "views": 478, | ||
| "category": "Data Science Platform", | ||
| "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": 62, | ||
| "views": 63, | ||
| "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": 131, | ||
| "views": 132, | ||
| "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": 73, | ||
| "views": 74, | ||
| "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": 125, | ||
| "views": 126, | ||
| "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": 144, | ||
| "views": 145, | ||
| "category": "infographic", | ||
| "relations": [], | ||
| "keywords": [], | ||
|
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@@ -6815,7 +6815,7 @@ | |
| "modified": "2024-07-15 15:00:28.066000", | ||
| "description": "Carrot-Mapper enables conversion of data to the OMOP Common Data Model, without data being egressed from its secure location, nor requiring access to the secure location. Carrot-Mapper automates as much of the process as possible, and also enables users to reuse each others' mappings. Carrot-Mapper pairs with Carrot-CDM to complete the OMOP conversion process.", | ||
| "license": "MIT", | ||
| "views": 327, | ||
| "views": 328, | ||
| "category": "Web application", | ||
| "relations": [], | ||
| "keywords": [], | ||
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@@ -6930,7 +6930,7 @@ | |
| "modified": "2024-08-22 14:46:48.076000", | ||
| "description": "Create and use de-identified databases for research.\n\n- Anonymises relational databases.\n\n- Extracts and de-identifies text from associated binary files.\n\n- Performs some specific preprocessing tasks; e.g.\n\n - preprocesses some specific databases (e.g. Servelec RiO EMR);\n - drafts a \"data dictionary\" for anonymisation, with special knowledge of\n some databases (e.g. TPP SystmOne);\n - fetches some word lists, e.g. forenames/surnames/eponyms.\n\n- Provides tools to link databases, including via Bayesian personal identity\n matching, in identifiable or de-identified fashion.\n\n- Provides a natural language processing (NLP) pipeline, including built-in\n NLP, support for external tools, and client/server support for the Natural\n Language Processing Request Protocol (NLPRP).\n\n- Web app for\n\n - querying the anonymised database;\n - providing a de-identification API;\n - managing a consent-to-contact process.\n\n### Results & Insights\n\nReference paper: Cardinal RN (2017), Clinical records anonymisation and text extraction (CRATE): an open-source software system. BMC Medical Informatics and Decision Making 17:50. https://pubmed.ncbi.nlm.nih.gov/28441940/; https://doi.org/10.1186/s12911-017-0437-1.\n\nSource code: https://github.com/ucam-department-of-psychiatry/crate", | ||
| "license": "GNU General Public License version 3.0 (GPLv3)", | ||
| "views": 181, | ||
| "views": 182, | ||
| "category": "Clinical Informatics Pipeline", | ||
| "relations": [], | ||
| "keywords": [ | ||
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@@ -7160,7 +7160,7 @@ | |
| "modified": "2023-09-04 14:22:05.302000", | ||
| "description": "The ACEsinEHRs library allows users to search and access tools, code lists, algorithms and scripts for validated indicators to identify adverse childhood experiences (ACEs) in electronic health records (EHRs) of parents and children before and after birth.\n\nThe platform provides information on definitions, theoretical and clinical concepts and \"how to guides\" to help users apply the developed ACE indicators to create \u201cresearch-ready\u201d datasets.\n\n### Results & Insights\n\nMission statement and aims:\n-To improve the health of families and young people by using electronic health records (EHRs) to identify and measure adverse childhood experiences (ACEs).\n-To advocate for early support and family centred services for families with ACEs.\n-To work with researchers, professionals, and policymakers to promote trauma-informed care and public health policies that support families affected by ACEs.\n-To enhance the methodological standard, accessibility, and utility of data-driven think-family approaches to study adversity using EHRs.\n-To provide a validated system to measure intervenable and clinically relevant ACEs with the potential to support trauma-informed data-driven research, public health, policy, and health care.\n-To continuously develop the www.ACEsinEHRs.com platform to improve resources for researchers, professionals, and policymakers", | ||
| "license": "Apache 2", | ||
| "views": 98, | ||
| "views": 99, | ||
| "category": "Phenomics", | ||
| "relations": [], | ||
| "keywords": [ | ||
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