Name: Destinee Morrow
Pronouns: she/her
Biography:
She received her Masters of Science in Bioinformatics from Hood College (Frederick, Maryland) in 2020. Through the Sustainable Research Pathways (SRP) program, she interned under the mentorship of Dr. Xinlian Liu (Hood College) and Dr. Silvia Crivelli (LBNL). Soon after, she accepted a research associate position at LBNL working under Dr. Silvia Crivelli, which has evolved into a full-time staff position. Her current research includes machine learning and deep learning analysis of electronic health records (EHR). More specifically, scaled natural language processing (NLP) with the use of high-performance computing (HPC) for healthcare advancements in collaboration with the Department of Veterans Affairs (VA). This work helps facilitate improved precision care with regards to high suicide risk, obstructive sleep apnea and lung cancer patients. Destinee loves her work in the interdisciplinary sciences, and strives to see how computer science can continually improve our world.
Institution/Lab: Lawrence Berkeley National Laboratory
Website: https://crd.lbl.gov/divisions/amcr/computational-science-dept/acsd/staff/staff-members/destinee-morrow/
SRP Collaboration Topic/Title: Using Machine Learning for Early Detection of Obstructive Sleep Apnea
Field or research area: AI in Healthcare
Please select all the topical areas that apply to your project:
Computational Science Applications (i.e., bioscience, cosmology, chemistry, environmental science, nanotechnology, climate, etc.); Data Science (i.e., data analytics, data management & storage systems, visualization); High-Performance Computing; Machine Learning and AI
Brief Abstract:
Obstructive sleep apnea (OSA) affects 24% of all Veterans or 1 in every 15 Americans and is associated with increased risk for developing cardiovascular and metabolic comorbidities such as heart disease, stroke, hypertension, and type 2 diabetes mellitus. A vast number of patients are already experiencing at least one of these comorbidities by the time they are diagnosed with OSA; suggesting that OSA is being diagnosed well after the onset. OSA shares many symptoms with other diagnoses such as depression, increasing the diagnosis gap. With the use of machine learning and natural language processing, we can better understand and identify OSA and target patients for treatment closer to the onset, reducing the likelihood of patients developing comorbidities.
Desired relevant skills, background, or interests:
python, pytorch, mpi, dask, R, clinical data, big data, electronic health records, high-performance computing, natural language processing, large language models, machine learning, predictive modeling
Other comments:
Remote work only.
Do any special requirements apply? other
Other, specify: Remote work only
Keywords:
clinical data; big data; high-performance computing; natural language processing; large language models; machine learning; predictive modeling; healthcare
Lightning Talk Title: Impact of Natural Language Processing on Healthcare Research