Patent Document ID: 9730645
Application ID: 14005107
Patent Flag: 1

Claim One:
1. An automated clinical decision support system configured for managing drug delivery to one or more patients based on generated predictions of potential late onset neonatal sepsis in the one or more patients estimated based on at least on computational analysis of encoded time series data obtained indicative of one or more relationships between heart rate variability (HRV) scores and respiratory rate variability (RRV) scores, the system comprising: a) one or more medical monitoring devices operable to acquire time series drug infusion data indicative of one or more characteristics of drug infusion events, and time series heart rate and respiratory rate signals at regular intervals relating to one or more patients and to record the time series heart rate and respiratory signals as the encoded time series data; and b) a data analysis means operable to: analyze the encoded time series data to generate the heart rate variability (HRV) scores and the respiratory rate variability (RRV) scores, the HRV and RRV scores obtained by relations: ∀ t ∈ p ⁡ ( t ) : HRV ⁡ ( t ) = ( ∑ n = 1 120 ⁢ (  HR n - HR n + 1  < th_HR ) ) / 2 ∀ t ∈ p ⁡ ( t ) : RRV ⁡ ( t ) = ( ∑ n = 1 120 ⁢ (  RR n - RR n + 1  < th_RR ) ) / 2 , where HR is a heart rate for a given point in time, RR is the respiratory rate for the given point in time, th HR is a heart rate threshold, and th_RR is a respiratory rate threshold, the HRV scores and RRV scores each indicative of corresponding numbers of periods of low variability; apply temporal abstraction analysis to the HRV and the RRV scores to generate analysis data by distinguishing temporal patterns in the HRV and the RRV scores to identify periods of high variability and low variability correlated to the one or more characteristics of the drug infusion events to identify characteristics of physiological responses to the drug infusion events represented in the temporal patterns found in the HRV and RRV scores; generate or update a classification matrix based on the distinguished temporal patterns, the classification matrix determined based on one or more identified relationships between the HRV and the RRV scores, the classification matrix maintained in relation to a target demographic population subset receiving similar drug infusion events; store the classification matrix in a data structure on a data record associated with the one or more patients; periodically apply the classification matrix to the HRV and RRV scores, obtained by said relations, of another specific patient to generate a prediction of potential late onset neonatal sepsis for the specific patient; and upon a positive prediction of potential late onset neonatal sepsis, manage the delivery of the drug to the specific patient based at least on the prediction of late onset neonatal sepsis.