Software | Feature learning | Technology: Tools, Hardware, and Software | All Resources

Multifactor Dimensionality Reduction (scikit-MDR)

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A scikit-learn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction. This project is still under active development and we encourage you to check back on this repository regularly for updates. MDR is an effective feature construction algorithm that is capable of modeling higher-order interactions and capturing complex patterns in data sets. MDR currently only works with categorical features and supports both binary classification and regression problems. We are working on expanding the algorithm to cover more problem types and provide more convenience features.

 

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Ontologies | Data | All Resources

NCBO BioPortal

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BioPortal is an open repository of biomedical ontologies that stores ontologies developed in various formats, that provides for automatic updates by user submissions of new versions, and that provides access via Web browsers and through Web services. This is a great place to explore and search for ontologies related to different types of data and fields of biomedical study.

 

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Machine Learning | External | Training Resources | All Resources

Neural Networks/ Deep Learning (StatQuest)

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Neural Networks are one of the most popular Machine Learning algorithms, but they are also one of the most poorly understood. Everyone says Neural Networks are “black boxes”, but that’s not true at all. In this video I break each piece down and show how it works, step-by-step, using simple mathematics that is still true to the algorithm. By the end of this video you will have a deep understanding of what Neural Networks do.

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HealthCare | Data | Publicly available biomedical data | All Resources

OASIS

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OASIS is a key component of the Centers for Medicare and Medicaid (CMS) partnership with the home care industry to foster and monitor improved home health care outcomes.  It is also proposed to become an integral part of the revised Conditions of Participation for Medicare-certified home health agencies (HHAs). The Outcome and Assessment Information Set-C (OASIS-C) is a group of data elements that: (1) Represent core items of a comprehensive assessment for an adult home care patient; and (2) Form the basis for measuring patient outcomes for the purposes of outcome-based quality improvement (OBQI)

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AITC Consortium | Basic Information | PennAITech Awardees | All Resources

Patient-Surrogate Alignment in Digital Advance Care Planning

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AI-based advanced care planning platform to align surrogate decision makers to improve end-of-life care
Awardee Organization(s): Koda Health
Principal Investigator(s): Desh Mohan, MD
Official Project Title: Patient-Surrogate Alignment in Digital Advance Care Planning
AITC Partner: PennAITech
Website(s): www.kodahealthcare.com

Advance Care Planning (ACP) encompasses educating patients on potential future healthcare decisions, clarifying patients’ values and healthcare preferences, and sharing decisions with family and care providers. Most individuals lacking decision-making capacity near end-of-life will need a surrogate decision maker (SDM) to make or voice decisions for them. However, less than 25% of SDMs are engaged in the ACP process or aware of patient preferences, and often feel unprepared for decision-making. Koda is a machine learning based ACP platform which offers dynamic educational content, decision guidance, and advanced directive documentation for patients and SDMs. Aims of this study are to: 1) determine motivators of patient-SDM alignment among Koda users and 2) develop a machine learning algorithm to perform SDM persona identification.
Participants will be 50 patient-SDM dyads. Eligible patients will be 50+ years of age, without dementia or blindness. SDMs will be 18+ years old. All participants should have an email address and ability to read and speak English. Following informed consent, patients will complete the Koda ACP platform and a survey about SDMs’ values and experiences. SDMs will complete a self-survey and perceived alignment surveys before and after reviewing the patient’s completed ACP. Survey data will be used to train theSDM persona detection algorithm. Dyads will also be invited to complete qualitative interviews, for further exploration of patient-SDM experiences.This pilot project aims to better understand patientSDM alignments and to develop an algorithm for identification of SDM personas, with the ultimate goal of facilitating increasingly high-quality ACP and goal-concordant care.

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Engineering and science | Penn Resources | All Resources

Penn Center for Health, Devices and Technology (Penn HealthTech)

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Sponsored in part by a generous gift from Penn alum Jonathan Brassington, the Penn Center for Health, Devices and Technology, known simply as Penn Health-Tech, was established as a collaboration between the Perelman School of Medicine, the School of Engineering and Applied Science, and the Office of the Vice Provost of Research. Penn Health-Tech provides resources and links innovators to regional partners, including the University of Pennsylvania health system and The Children’s Hospital of Philadelphia, expanding Penn’s biomedical technology pipeline

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Ethics and policy | Penn Resources | All Resources

Penn Department of Medical Ethics and Health Policy

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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.

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Ethics and policy | Clinical/biomedical | Penn Resources | All Resources

Penn Injury Science Center

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Funded by a grant from the Centers for Disease Control and Prevention, the Penn Injury Science Center brings together university, community, and government partners around intervention programs with the greatest potential for impact. They promote and perform the highest quality research, training, and translation of scientific discoveries into practice and policy in order to reduce injuries, violence and their impact on people around the world.

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