Heart rate variability (HRV) signals are reported to be associated with the personalized drug
response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc.
But the relationships between HRV signals and the personalized drug response in different diseases and
patients are complex and remain unclear. With the fast development of modern smart sensor technologies
and the popularization of big data paradigm, more and more data on the HRV and drug response
will be available, it then provides great opportunities to build models for predicting the association of
the HRV with personalized drug response precisely. We here review the present status of the HRV data
resources and models for predicting and evaluating of personalized drug responses in different diseases.
The future perspectives on the integration of knowledge and personalized data at different levels such as,
genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of
drug therapy and their response will be provided.
Keywords: Heart rate variability, Computational models, Personalized drug therapy, Prediction of drug response, Brain activity,
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