The Division of Medical Ethics aims to improve patient care, medical sciences, and health care policy through outstanding bioethics scholarship and the training of the next generation of bioethics scholars. With strengths in research ethics, neuro- and mental health ethics, global bioethics and the ethics of health care policy, it is among the leading centers of bioethics scholarship in the world.
View ResourcePenn Machine Learning Benchmarks (PMLB)
March 5, 2023
by
Ray
This repository contains the code and data for a large, curated set of benchmark datasets for evaluating and comparing supervised machine learning algorithms. These data sets cover a broad range of applications, and include binary/multi-class classification problems and regression problems, as well as combinations of categorical, ordinal, and continuous features.
View ResourcePennAITech Home
March 5, 2023
by
Ray
The overarching goal of the Penn Artificial Intelligence and Technology (PennAITech) Collaboratory for Healthy Aging is to identify, develop, evaluate, commercialize, and disseminate innovative technology and artificial intelligence (AI) methods and software to support older adults and those with Alzheimer’s Disease (AD) and Alzheimer’s Disease and Related Dementias (ADRD) in their home environment. The Collaboratory is motivated by the need for a comprehensive pipeline from technology-based monitoring of older adults in the home, collection and processing monitoring data, integration of those data with clinical data from electronic health records, analysis with cutting-edge AI methods and software, and deployment of validated AI models at point of care for decision support.
View ResourcePennAITech News and Events
March 5, 2023
by
Ray
You can download our latest newsletter here. We are launching our webinar series for this academic year. The purpose of this webinar is to foster a dialogue exploring clinical, ethical and technological opportunities and challenges associated with the use of technology to promote aging, and to introduce different perspectives at the intersection of informatics and gerontology.
View ResourcePennAITech Resources
March 5, 2023
by
Ray
The overarching goal of the Technology Identification and Training Core is to use evidence from the literature, stakeholder and expert inputs to identify the technology needs of older Americans, as well as develop training activities for artificial intelligence (AI) and technology for scientists, engineers, clinicians, medical professionals, patients, policy makers, and investors.
View ResourcePennAITech Youtube Channel
March 5, 2023
by
Ray
The overarching goal of the Penn Artificial Intelligence and Technology (PennAITech) Collaboratory for Healthy Aging is to identify, develop, evaluate, commercialize, and disseminate innovative technology and artificial intelligence (AI) methods and software to support older adults and those with Alzheimer’s Disease (AD) and Alzheimer’s Disease and Related Dementias (ADRD) in their home environment. The Collaboratory is motivated by the need for a comprehensive pipeline from technology-based monitoring of older adults in the home, collection and processing monitoring data, integration of those data with clinical data from electronic health records, analysis with cutting-edge AI methods and software, and deployment of validated AI models at point of care for decision support. We share PennAITech videos (e.g., webinars, call for applications) in this youtube channel.
View ResourcePhysiological Detection and Monitoring of Alzheimer’s Disease
March 5, 2023
by
Ray
Using wireless dry-sensor EEG wearables and an AI-based neural algorithms to detect early-stage AD/ADRD
Awardee Organization(s): Cogwear LLC
Principal Investigator(s): David Yonce, MS, MBA
Official Project Title: Physiological Detection and Monitoring of Alzheimer’s Disease
AITC Partner: PennAITech
Website(s): www.cogweartech.com
Program for Diversity and Inclusion (PDI)
March 5, 2023
by
Ray
IDEAL MEd supports the educational mission of the Perelman School of Medicine by promoting an inclusive, welcoming, supportive and socially engaged medical student community, while cultivating an equitable student experience.
View ResourceRandom Forests (StatQuest)
March 5, 2023
by
Ray
Random Forests make a simple, yet effective, machine learning method. They are made out of decision trees, but don’t have the same problems with accuracy. In this video, I walk you through the steps to build, use and evaluate a random forest.
View ResourceRelief-based Algorithm Training Environment (REBATE)
March 5, 2023
by
Ray
This package includes a scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. These Relief-Based algorithms (RBAs) are designed for feature weighting/selection as part of a machine learning pipeline (supervised learning). Presently this includes the following core RBAs: ReliefF, SURF, SURF*, MultiSURF*, and MultiSURF. Additionally, an implementation of the iterative TuRF mechanism and VLSRelief is included. It is still under active development and we encourage you to check back on this repository regularly for updates. These algorithms offer a computationally efficient way to perform feature selection that is sensitive to feature interactions as well as simple univariate associations, unlike most currently available filter-based feature selection methods. The main benefit of Relief algorithms is that they identify feature interactions without having to exhaustively check every pairwise interaction, thus taking significantly less time than exhaustive pairwise search.
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