A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory data.
Statistical physics and information theory is applied to the clinical chemistry measurements present in a patient database containing 2.5 million patients' data over a 20-year period. Despite the seemingly naive approach of aggregating all patients over all times (with respect to particular clinical chemistry measurements), both a diurnal signal in the decay of the time-delayed mutual information and the presence of two sub-populations with differing health are detected. This provides a proof in principle that the highly fragmented data in electronic health records has potential for being useful in defining disease and human phenotypes.
Albers
D J
DJ
Department of Biomedical Informatics, Columbia University, 622 W 168 St. VC-5, New York, NY 10032.
Hripcsak
George
G