Medicine and Health Sciences Commons^™

Exploration Of Dark Chemical Genomics Space Via Portal Learning: Applied To Targeting The Undruggable Genome And Covid-19 Anti-Infective Polypharmacology, Tian Cai, Li Xie, Muge Chen, Yang Liu, Di He, Shuo Zhang, Cameron Mura, Philip Boume, Lei Xie

Publications and Research

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning , to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) …

The Intersection Of Industry, Occupation, And Job Tasks With Hotel Room Cleaner Musculoskeletal Disorder Injuries: A Methods Approach To The Analysis Of California Workers’ Compensation Data, Pamela Vossenas

Dissertations and Theses

Abstract

Background

Hotel room cleaners are a high-risk occupation for musculoskeletal disorder (MSD) injuries among U.S. hotel workers. The Bureau of Labor Statistics (BLS) publishes occupational injury rate data for Maids and Housekeeping Cleaners, the Standard Occupation Classification closest to the job of a hotel room cleaner, and for the hotel industry, yet the BLS does not cross reference injury rate data by occupation and industry. This lack of occupational injury surveillance data limits MSD injury prevention and intervention efforts targeting this high-risk occupation in the hotel industry. Workers’ compensation (WC) data is another source of administrative data with potential …

Emerging Technologies In Healthcare: Analysis Of Unos Data Through Machine Learning, Reyhan Merekar

Student Theses and Dissertations

The healthcare industry is primed for a massive transformation in the coming decades due to emerging technologies such as Artificial Intelligence (AI) and Machine Learning. With a practical application to the UNOS (United Network of Organ Sharing) database, this Thesis seeks to investigate how Machine Learning and analytic methods may be used to predict one-year heart transplantation outcomes. This study also sought to improve on predictive performances from prior studies by analyzing both Donor and Recipient data. Models built with algorithms such as Stacking and Tree Boosting gave the highest performance, with AUC’s of 0.6810 and 0.6804, respectively. In this …

Artificial Intelligence: A New Paradigm In Obstetrics And Gynecology Research And Clinical Practice, Pulwasha Iftikhar, Marcela V. Kuijpers, Azadeh Khayyat, Aqsa Iftikhar, Maribel Degouvia De Sa

Publications and Research

Artificial intelligence (AI) is growing exponentially in various fields, including medicine. This paper reviews the pertinent aspects of AI in obstetrics and gynecology (OB/GYN) and how these can be applied to improve patient outcomes and reduce the healthcare costs and workload for clinicians.

Herein, we will address current AI uses in OB/GYN, and the use of AI as a tool to interpret fetal heart rate (FHR) and cardiotocography (CTG) to aid in the detection of preterm labor, pregnancy complications, and review discrepancies in its interpretation between clinicians to reduce maternal and infant morbidity and mortality. AI systems can be used …

Data And Statistical Methods To Analyze The Human Microbiome, Levi Waldron

Publications and Research

The Waldron lab for computational biostatistics bridges the areas of cancer genomics and microbiome studies for public health, developing methods to exploit publicly available data resources and to integrate-omics studies.