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
AI-enabled near-field coherent sensing (NCS) radio sensors to detect respiratory distress for advanced AD/ADRD patients who are unable to self-report
Awardee Organization(s): Weill Cornell Medicine
Principal Investigator(s): Veerawat Phongtankuel, MD
Official Project Title: Detecting Respiratory Distress in Patients with Advanced AD/ADRD Using Radio Sensors
AITC Partner: PennAITech
Website(s): www.weill.cornell.edu
A modern machine learning benchmark, which includes: 40 datasets in tabular numeric format specially designed to differentiate the performance of some of the leading Machine Learning (ML) methods, and a package to perform reproducible benchmarking that simplifies comparison of performance of the methods. DIGEN provides comprehensive information on the datasets, including: ground truth – a mathematical formula presenting how the target was generated for each of the datasets, the results of exploratory analysis, which includes feature correlation and histogram showing how binary endpoint was calculated, multiple statistics on the datasets, including the AUROC, AUPRC and F1 scores, each dataset comes with Receiver-Operating Characteristics (ROC) and Precision-Recall (PRC) charts for tuned ML methods, and a boxplot with projected performance of the leading methods after hyper-parameter tuning (100 runs of each method started with different random seed), Apart from providing a collection of datasets and tuned ML methods, DIGEN provides tools to easily tune and optimize parameters of any novel ML method, as well as visualize its performance in comparison with the leading ones. DIGEN also offers tools for reproducibility.
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The scikit-ExSTraCS package includes a sklearn-compatible Python implementation of ExSTraCS 2.0. ExSTraCS 2.0, or Extended Supervised Tracking and Classifying System, implements the core components of a Michigan-Style Learning Classifier System (where the system’s genetic algorithm operates on a rule level, evolving a population of rules with each their own parameters) in an easy to understand way, while still being highly functional in solving ML problems. It allows the incorporation of expert knowledge in the form of attribute weights, attribute tracking, rule compaction, and a rule specificity limit, that makes it particularly adept at solving highly complex problems. In general, Learning Classifier Systems (LCSs) are a classification of Rule Based Machine Learning Algorithms that have been shown to perform well on problems involving high amounts of heterogeneity and epistasis. Well designed LCSs are also highly human interpretable. LCS variants have been shown to adeptly handle supervised and reinforced, classification and regression, online and offline learning problems, as well as missing or unbalanced data. These characteristics of versatility and interpretability give LCSs a wide range of potential applications, notably those in biomedicine.
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Wearable sensors capturing digital autonomic biomarkers to detect empathy loss for frontotemporal dementia (bvFTD)
Awardee Organization(s): University of Pennsylvania Frontotemporal Degeneration Center
Principal Investigator(s): Emma Rhodes, PhD
Official Project Title: Feasibility of Digital Monitoring to Detect Autonomic Markers of Empathy Loss in bvFTD
AITC Partner: PennAITech
Website(s): www.med.upenn.edu/ftd
FIBERS is an evolutionary algorithm for performing feature learning by binning features to stratify risk in biomedical datasets with either a binary outcome or with right censored survival outcomes over time.
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FOCUS provides programs that simultaneously promote women’s health research, enhance medical faculty professional development and aim to create an institutional culture that foster a more diverse faculty at the Perelman School of Medicine at the University of Pennsylvania.
View ResourceEducational lectures for the course: “Foundations of Artificial Intelligence” developed by Dr. Ryan Urbanowicz in 2020 at the University of Pennsylvania’s Perelman School of Medicine. This is the first of three courses covering topics in artificial intelligence for application within the context of informatics and biomedical research. The course is divided into modules that cover (1) introductory/background materials, (2) logic, (3) other knowledge representation, (4) essentials of expert systems, (5) search, (6) uncertainty, and (7) advanced/auxiliary topics. These topics offer a global foundation for branches of AI application and research, including concepts that will later support a deeper understanding of inductive reasoning and machine learning. In a practical sense, this course focuses on how biomedical data can be organized, represented, interpreted, searched, and applied in order to derive knowledge, make decisions, and ultimately make predictions while avoiding bias. This course was assembled using content from a wide variety of textbooks, slides, and lectures by various authors and speakers on the relevant topics. Some lectures were prepared and given by guest lecturers and thus have not been posted. At the time of posting, this course is in its second year so any feedback is welcome regarding any mistakes or suggested improvements.
View ResourceThe Penn Frontotemporal Degeneration Center brings together an energetic team of creative clinicians and researchers dedicated to the investigation and treatment of early onset neurodegenerative conditions. The research expertise at the Penn FTD Center spans many levels of neuroscience ranging from detailed clinico-pathological studies, biomarker discovery, genetics, neuropsychological studies, functional and structural neuroimaging, and cognitive neuroscience investigations of language, memory, and social cognition.
View ResourceGAMETES is an algorithm for the generation of complex single nucleotide polymorphism (SNP) models for simulated association studies. GAMETES is designed to generate epistatic models which we refer to as pure and strict. These models constitute the worst-case in terms of detecting disease associations, since such associations may only be observed if all n loci are included in the disease model. The user-friendly GAMETES software rapidly and precisely generates epistatic multi-locus models, and using these models, can also generate simulated datasets exhibiting epistasis. Version 2.2 adds the ability to generate heterogeneous datasets by applying multiple independent models to different subsets of the simulated data. Further additional features include the facility to create additive datasets by applying multiple independent models to the entire dataset, as well as functionality for the design of continuous endpoints. Additionally, we have added a custom model generation feature, so that users may directly specify and examine the properties of any 2 or 3 locus SNP model. Simple Mendelian models may also be generated with this feature.
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