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

ALIRO: AI Driven Data Science

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ALIRO is an easy-to-use data science assistant. It allows researchers without machine learning or coding expertise to run supervised machine learning analysis through a clean web interface. It provides results visualization and reproducible scripts so that the analysis can be taken anywhere. And, it has an AI assistant that can choose the analysis to run for you. Dataset profiles are generated and added to a knowledgebase as experiments are run, and the AI assistant learns from this to give more informed recommendations as it is used. Aliro comes with an initial knowledgebase generated from the PMLB benchmark suite.

 

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Ethics and policy | Penn Resources | All Resources

Alliance of Minority Physicians

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The mission of AMP is to develop leaders in clinical, academic, and community medicine through active recruitment, career development, mentorship, social opportunities and community outreach geared towards underrepresented faculty, house staff, and medical students at UPHS, CHOP, and the Perelman School of Medicine. Dr. Iris Reyes provides faculty leadership for AMP.

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ADRD | Data | Publicly available biomedical data

Alzbiomarker

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The Alzbiomarker database organizes decades of data on fluid biomarkers for Alzheimer’s disease. Biomarker measurements are curated from published studies and meta-analyzed. Version 1.0 through 2.1 contains studies comparing measurements in Alzheimer’s disease to cognitively healthy individuals and studies comparing progressive MCI to stable MCI. Version 3.0 includes comparisons of biomarker levels in non-AD neurological conditions to Alzheimer’s disease. The data can be downloaded by requesting data from contacting alzbiomarker@alzforum.org

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AITC Consortium | Basic Information | PennAITech Awardees | All Resources

An Accessible Machine Learning-Based ADRD Screening Tool for Families and Caregivers

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A machine learning-enabled speech-based screening tool for AD/ADRD caregivers
Awardee Organization(s): University of Southern California
Principal Investigator(s): Maja Matarić, PhD
Official Project Title: An Accessible Machine Learning-Based ADRD Screening Tool for Families and Caregivers
AITC Partner: PennAITech
Website(s): www.robotics.usc.edu/~maja

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HealthCare | Data | Publicly available biomedical data

China Health and Retirement Longitudinal Study (CHARLS)

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The China Health and Retirement Longitudinal Study (CHARLS) aims to collect a high quality nationally representative sample of Chinese residents ages 45 and older to serve the needs of scientific research on the elderly. The baseline national wave of CHARLS is being fielded in 2011 and includes about 10,000 households and 17,500 individuals in 150 counties/districts and 450 villages/resident committees. The individuals will be followed up every two years. CHARLS adopts multistage stratified PPS sampling. As an innovation of CHARLS, a software package (CHARLS-Gis) is being created to make village sampling frames.

 

CHARLS is based on the Health and Retirement Study (HRS) and related aging surveys such as the English Longitudinal Study of Aging (ELSA) and the Survey of Health, Aging and Retirement in Europe (SHARE). The pilot survey of CHARLS was conducted in two provinces (Gansu and Zhejiang) in 2008 and collected data from 48 communities/villages in 16 counties/districts, covering 2,685 individuals living in 1,570 households. The response rate of the pilot survey was 85%.

 

The CHARLS questionnaire includes the following modules: demographics, family structure/transfer, health status and functioning, biomarkers, health care and insurance, work, retirement and pension, income and consumption, assets (individual and household), and community level information.

 

CHARLS has received critical support from Peking University, the National Natural Science Foundation of China, the Behavioral and Social Research Division of the NIA and the World Bank.

 

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AITC Consortium | Basic Information | PennAITech Awardees | All Resources

Conversational Care Technologies

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The PI’s research team will use a human-centered approach to design, build, and evaluate conversational care technologies that scaffold meaningful care discussions between older adults and their informal caregivers
Awardee Organization(s): University of Michigan
Principal Investigator(s): Robin Brewer, PhD
Official Project Title: Conversational Care Technologies
AITC Partner: PennAITech
Website(s): www.umich.edu, www.si.umich.edu

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ADRD | Data | Publicly available biomedical data

Critical Path For Alzheimer’s Disease (CPAD)

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The Critical Path For Alzheimer’s Disease (CPAD) is a de-identified database directed by Critical Path Institute. The database collects patient-level data from 12811 patients across 36 clinical trials of AD and MCI. It contains demographic information, APOE4 genotype, concomitant medications, and cognitive scales, such as MMSE and ADAS-Cog. It also provides limited treatment-arm data and limited AD biomarker data including biofluid, tau or amyloid positron emission tomography (PET), EEG data. All data in this database have been remapped to the CDISC SDTM v3.1.2 data standard.

This database is open to CPAD members, as well as to external qualified researchers who submit, and are approved for, a request for access.

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Software | Technology: Tools, Hardware, and Software | Deep Learning | All Resources

Deep Learning for Toxicology (DTox)

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In drug development, a major reason for attrition is the lack of understanding of cellular mechanisms governing drug toxicity. The black-box nature of conventional classification models has limited their utility in identifying toxicity pathways. Here we developed DTox (Deep learning for Toxicology), an interpretation framework for knowledge-guided neural networks, which can predict compound response to toxicity assays and infer toxicity pathways of individual compounds. We demonstrate that DTox can achieve the same level of predictive performance as conventional models with a significant improvement in interpretability. Using DTox, we were able to rediscover mechanisms of transcription activation by three nuclear receptors, recapitulate cellular activities induced by aromatase inhibitors and PXR agonists, and differentiate distinctive mechanisms leading to HepG2 cytotoxicity. Virtual screening by DTox revealed that compounds with predicted cytotoxicity are at higher risk for clinical hepatic phenotypes. In summary, DTox provides a framework for deciphering cellular mechanisms of toxicity in silico.

 

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AITC Consortium | Basic Information | PennAITech Awardees | All Resources

Designing Usable Technologies for Older Adults via Data-Driven Whole-Person User Personas

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Using machine learning to create data-driven user personas for the design of usable technologies for older adults
Awardee Organization(s): University of Minnesota

Principal Investigator(s): Robin Austin, PhD, DNP, DC, RN-BC
Official Project Title: Designing Usable Technologies for Older Adults via Data-Driven Whole-Person User Personas
AITC Partner: PennAITech
Website(s): www.nursing.umn.edu

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