Document text
Principal Investigator: Cassie S Mitchell
Organization: GEORGIA INSTITUTE OF TECHNOLOGY
Fiscal Year: 2024
Award: $366,688
Funding agency: National Institute of General Medical Sciences
Project Summary/Abstract
The Laboratory for Pathology Dynamics was founded on the principle that pathology is multifactorial, multi-
scalar, and driven by complex interacting dynamics. This proposal builds and applies new predictive medicine
technology that enables multifactorial, multi-scalar data integration to identify disease causes, develop cures,
and optimize patient care. Key knowledge gaps addressed include: 1.) integration of siloed relationships for
interactive literature-based discovery across 33+ million journal articles, including the development of a
comprehensive biomedical knowledge graph software called SemBioSys; 2) high-throughput automated meta-
analysis to quantify effect size across cohorts and scales using a combination of newly developed deep
learning, active learning, interactive weak supervision and human in the loop artificial intelligence (AI)
algorithms into a single interactive software; 3) de novo multimodal machine learning algorithms to investigate
multifactorial, multi-scalar disease dynamics; 4) human in the loop methods for enhancing AI accuracy, utility,
and interpretability, which leverages our large test bed of diverse high school and undergraduate curators
acting as subject matter experts. The developed technology will be applied to a bevy of multifactorial diseases
in collaboration with clinicians and experimentalists: neurological diseases (Alzheimer’s Disease, Amyotrophic
Lateral Sclerosis, Parkinson’s Disease), cancers (adult and pediatric acute and chronic leukemias and solid
tumor cancers), cardiovascular disease, and infectious disease. The significance of this project is integrative
technology to identify and rank novel disease mechanisms, discover and prioritize novel and repurposed
drugs, and to optimally stratify patients for fair and equitable clinical trials that minimize health outcome
disparities. The innovation includes new integrative biomedical natural language processing (NLP), multimodal
machine learning, and dynamic disease modeling and event sequencing algorithms that can be generalized to
all of medicine.
Terms: <21+ years old><AD dementia><AI Augmented><AI algorithm><AI enhanced><Active Learning><Address><Adult><Adult Human><Algorithms><Alzheimer Type Dementia><Alzheimer disease dementia><Alzheimer sclerosis><Alzheimer syndrome><Alzheimer's><Alzheimer's Disease><Alzheimers Dementia><Amyotrophic Lateral Sclerosis><Amyotrophic Lateral Sclerosis Motor Neuron Disease><Artificial Intelligence enhanced><Augmented by AI><Augmented by the AI><Augmented with AI><Augmented with the AI><Beds><Cancers><Cardiovascular Diseases><Chronic Leukemia><Clinical Trials><Collaborations><Communicable Diseases><Complex><Computer software><Cooperative Learning><Development><Disease><Disorder><Equity><Event><Experiential Learning><Gehrig's Disease><Infectious Disease Pathway><Infectious Diseases><Infectious Disorder><Knowledge><Laboratories><Literature><Lou Gehrig Disease><Malignant Neoplasms><Malignant Tumor><Medicine><Meta-Analysis><Methods><Multimodal ML><Multimodal machine learning><Natural Language Processing><Nervous System Diseases><Nervous System Disorder><Neurologic Disorders><Neurological Disorders><Paralysis Agitans><Parkinson><Parkinson Disease><Pathology><Patient Care><Patient Care Delivery><Primary Parkinsonism><Primary Senile Degenerative Dementia><Software><Solid Neoplasm><Solid Tumor><Supervision><Technology><Testing><adulthood><artificial intelligence algorithm><artificial intelligence augmented><cardiovascular disorder><care for patients><care of patients><caring for patients><cohort><data integration><deep learning><deep learning method><deep learning strategy><developmental><disease model><disorder model><drug repositioning><drug repurposing><enhanced with AI><enhanced with Artificial Intelligence><health outcome disparity><health outcome inequality><health outcome inequity><high school><human-in-the-loop><inequitable health outcome><innovate><innovation><innovative><journal article><knowledge graph><machine learned algorithm><machine learning algorithm><machine learning based algorithm><malignancy><multimodal learning><natural language understanding><neoplasm/cancer><neurological disease><novel><patient stratification><pediatric acute leukemia><primary degenerative dementia><repurposing agent><repurposing medication><senile dementia of the Alzheimer type><stratified patient><undergrad><undergraduate><undergraduate student><unequal health outcome>