Spots Global Cancer Trial Database for Phase I Human Analytics (HALO) Study

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Trial Identification

Brief Title: Phase I Human Analytics (HALO) Study

Official Title: This is a Human Analytics Longitudinal Observational (HALO) Study. A Phase I Study to Analyze All Available Biomarkers and Determinants of Health to Increase Diagnostic Accuracy While Reducing the Time to Diagnosis of Disease.

Study ID: NCT05423860

Conditions

Cardiovascular Diseases

Cancer

Dementia

Traumatic Brain Injury

Interventions

no interventions will be performed (observational)

Study Description

Brief Summary: Discover, optimize, standardize, and validate clinical-trial measures and biomarkers used to diagnose and differentiate cardiovascular, oncologic, neurologic, and other diseases and disorders. Specifically, our research study endeavors to improve disease and disorder diagnosis to the earliest clinical states, in preclinical states, and to develop ensemble multivariate biomarker risk scores leading to cardiovascular, oncologic, neurologic, and other diseases and disorders. Additionally, the study aims to: * Evaluate data analysis techniques to improve diagnostic accuracy and reduce time to diagnosis. * Evaluate data analysis techniques to improve risk stratification for participants through machine learning algorithms. * Direct participants to relevant and applicable clinical trials.

Detailed Description: Electronic medical records contain data that may indicate increased risk for certain diseases and disorders, but clinicians cannot easily discern the subtle patterns required to change their diagnostic and treatment patterns. This study seeks to use machine learning and data analysis techniques to increase diagnostic confidence and reduce time-to-diagnosis related to cardiovascular, oncologic, neurologic, and other diseases and disorders. The study endeavors to develop ensemble multivariate biomarker risk scores to predict future development of diseases and disorders, improve diagnosis in preclinical states and increase diagnostic accuracy in the earliest clinical states. We also aim to evaluate data analysis techniques to improve diagnostic accuracy and reduce time to diagnosis, improve risk stratification for participants through machine learning algorithms and direct participants to relevant and applicable clinical trials upon physician review, approval and recommendation.