ADRD | Data | Publicly available biomedical data

Religious Orders Study

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The Religious Orders Study is a collaborative study with Rush and other U.S. medical centers. It involves more than 1,100 older religious clergy (nuns, priests and brothers) who have agreed to medical and psychological evaluation each year and brain donation after death. Researchers are using information from the study to discover what changes in the brain are responsible for memory and movement problems. The study also looks closely at the transition from normal functioning of the aging brain to the mild cognitive impairment that can be an early sign of Alzheimer’s disease.

 

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AITC Consortium | Basic Information | PennAITech Awardees | All Resources

RGBd+ Thermal Computer Vision Platform for Home Monitoring and Telehealth

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Combining AI-enabled RGBd+ stereo vision and thermal sensors for home monitoring and telehealth
Awardee Organization(s): Bestie Bot
Principal Investigator(s): Richard Everts
Official Project Title: RGBd+ Thermal Computer Vision Platform for Home Monitoring and Telehealth
AITC Partner: PennAITech
Website(s): www.bestiebot.com

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Ontologies | Data | All Resources

RxNorm

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RxNorm provides normalized names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software, including those of First Databank, Micromedex, Multum, and Gold Standard Drug Database. By providing links between these vocabularies, RxNorm can mediate messages between systems not using the same software and vocabulary.

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Software | Data harmonization | Technology: Tools, Hardware, and Software | All Resources

Semi-automated Term Harmonization Pipeline

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This repository includes a set of Python-based Jupyter notebooks that comprise a semi-automated term harmonization pipeline applied to harmonize medical history terms across 28 clinical trials of pulmonary arterial hypertension. These notebooks pair with the paper ‘A Semi-Automated Term Harmonization Pipeline Applied to Pulmonary Arterial Hypertension Clinical Trials’. Below, we offer an overview of these pipelines and provide guidance for users on how to adapt these notebooks to their own target harmonization tasks.

 

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Software | AutoML | Technology: Tools, Hardware, and Software | All Resources

Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE)

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STREAMLINE is an end-to-end automated machine learning (AutoML) pipeline that empowers anyone to easily run, interpret, and apply a rigorous and customizable analysis for data mining or predictive modeling. Notably, this tool is currently limited to supervised learning on tabular, binary classification data but will be expanded as our development continues. The development of this pipeline focused on (1) overall automation, (2) avoiding and detecting sources of bias, (3) optimizing modeling performance, (4) ensuring complete reproducibility (under certain STREAMLINE parameter settings), (5) capturing complex associations in data (e.g. feature interactions), and (6) enhancing interpretability of output. Overall, the goal of this pipeline is to provide a transparent framework to learn from data as well as identify the strengths and weaknesses of ML modeling algorithms or other AutoML algorithms.

 

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ADRD | Data | Publicly available biomedical data

Single-Cell RNA-Seq database for Alzheimer’s Disease (scREAD)

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The Single-Cell RNA-Seq database for Alzheimer’s Disease (scREAD) dedicates to management of all the existing scRNA-Seq and snRNA-Seq data sets from the human postmortem brain tissue with AD and mouse models with AD pathology. It provides comprehensive analysis results for 73 data sets from 10 brain regions. These data sets include various types of data, such as control atlas construction, cell-type prediction,identification of differentially expressed genes, and identification of cell-type-specific regulons.

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Ontologies | Data | All Resources

SNOMED

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SNOMED CT is one of a suite of designated standards for use in U.S. Federal Government systems for the electronic exchange of clinical health information and is also a required standard in interoperability specifications of the U.S. Healthcare Information Technology Standards Panel. The clinical terminology is owned and maintained by SNOMED International, a not-for-profit association.

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HealthCare | Publicly available biomedical data | Data

Study on Global Ageing and Adult Health (SAGE)

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The Study on Global Ageing and Adult Health (SAGE) is part of an ongoing program of work to compile comprehensive longitudinal information on the health and well-being of adult populations and the ageing process. The core SAGE collects data on adults aged 18+ years, with an emphasis on populations aged 50+ years, from nationally representative samples in six countries: China, Ghana, India, Mexico, Russian Federation and South Africa. The study is composed of three stages. 

 

Wave 1 total sample size is over 40,000 individuals. 

 

Wave 2 data collection was completed in 2014/15 in five countries. Wave 2 data collection was released in the public domain at the end 2020. 

 

Wave 3 data collection was completed in March 2020.

 

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HealthCare | Data | Publicly available biomedical data

Survey of Healthy Ageing and Retirement in Europe (SHARE)

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The Survey of Healthy Ageing and Retirement in Europe (SHARE) is a research infrastructure for studying the effects of health, social, economic and environmental policies over the life-course of European citizens and beyond. The SHARE contains various types of data, including data of participants’ health status, economic status, social status, psychological status, lifestyle, and biomarker. The study is led by the Munich Center for the Economics of Aging (MEA), which is part of the Max Planck Institute for Social Law and Social Policy in Germany, and funded by the European Commission and NIA. 

 

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