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Related Experiment Video

Updated: Apr 28, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Dynamical phenotyping: using temporal analysis of clinically collected physiologic data to stratify populations.

D J Albers¹, Noémie Elhadad¹, E Tabak²

¹Department of Biomedical Informatics, Columbia University, New York, New York, United States of America.

|June 17, 2014

Summary

Glucose predictability varies with health status, with stable glucose levels indicating better health. This finding connects endocrine models to real-world patient data, offering insights into human health dynamics.

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Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

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Published on: April 7, 2020

Area of Science:

Endocrinology
Biomedical Informatics
Data Science

Background:

Glucose levels are a primary indicator of human health.
Understanding glucose dynamics is crucial for managing metabolic health.
Electronic Health Records (EHR) offer rich data for physiological studies.

Purpose of the Study:

To investigate factors influencing glucose predictability using time-series data.
To correlate glucose predictability with health states using a mechanistic endocrine model.
To link clinical data insights with physiological modeling.

Main Methods:

Utilized glucose time-series data from an Electronic Health Record (EHR) repository.
Employed time-delayed mutual information (TDMI) to quantify glucose predictability.
Integrated a mechanistic endocrine model and patient record reviews (manual and automated).

Main Results:

Glucose predictability is significantly influenced by health state variations.
Less insulin requirement for glucose processing correlates with higher glucose predictability.
The presence or absence of health state variation (in-control vs. out-of-control glucose) is a key determinant of predictability.

Conclusions:

Glucose predictability is a dynamic indicator of health status, beyond mere glucose magnitude.
This study bridges mechanistic endocrine modeling with real-world clinical data.
Findings highlight the importance of glucose dynamics in assessing human health.