A method of predicting Acute Cardiopulmonary events, the method comprising: measuring a first set of parameters relating to heart rate variability data of a patient; measuring a second set of parameters relating to vital sign data of the patient comprising at least one of systolic blood pressure data, diastolic blood pressure data, pulse oximetry data, and respiratory rate; providing an artificial neural network comprising a network of interconnected nodes, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight adjusted by training the artificial neural network using an electronic database having a plurality of sets of data, each set having at least a parameter relating to heart rate variability data and a parameter relating to vital sign data, each set further having a parameter relating to patient survivability; processing the first set of parameters and the second set of parameters to produce processed data suitable for input into the artificial neural network; providing the processed data as input into the artificial neural network; and obtaining an output from the artificial neural network, the output providing a calculation of a statistical probability of an ACP event within 72 hours of the calculation; wherein measuring a first set of parameters relating to heart rate variability data of a patient comprises: filtering an ECG signal to remove noise and artifacts, locating a QRS complex within the filtered ECG signal, finding RR intervals between successive QRS peaks of the QRS complex, and processing the RR intervals according to at least one of a global method, local method, total segment method, and selective segment method to obtain the first set of parameters relating to heart rate variability data. measuring a first set of parameters relating to heart rate variability data of a patient; measuring a second set of parameters relating to vital sign data of the patient comprising at least one of systolic blood pressure data, diastolic blood pressure data, pulse oximetry data, and respiratory rate; providing an artificial neural network comprising a network of interconnected nodes, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight adjusted by training the artificial neural network using an electronic database having a plurality of sets of data, each set having at least a parameter relating to heart rate variability data and a parameter relating to vital sign data, each set further having a parameter relating to patient survivability; processing the first set of parameters and the second set of parameters to produce processed data suitable for input into the artificial neural network; providing the processed data as input into the artificial neural network; and obtaining an output from the artificial neural network, the output providing a calculation of a statistical probability of an ACP event within 72 hours of the calculation; wherein measuring a first set of parameters relating to heart rate variability data of a patient comprises: filtering an ECG signal to remove noise and artifacts, locating a QRS complex within the filtered ECG signal, finding RR intervals between successive QRS peaks of the QRS complex, and processing the RR intervals according to at least one of a global method, local method, total segment method, and selective segment method to obtain the first set of parameters relating to heart rate variability data. filtering an ECG signal to remove noise and artifacts, locating a QRS complex within the filtered ECG signal, finding RR intervals between successive QRS peaks of the QRS complex, and processing the RR intervals according to at least one of a global method, local method, total segment method, and selective segment method to obtain the first set of parameters relating to heart rate variability data.