Method of predicting acute cardiopulmonary events and survivability of a patient

Abstract

According to embodiments of the invention, there is provided a method of producing an artificial neural network capable of predicting the survivability of a patient, the method including: storing in an electronic database patient health data, the patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron of the plurality of artificial neurons is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data, such that the artificial neural network is trained to produce a prediction on the survivability of a patient.

Description

FIELD OF THE INVENTION[0001]The invention relates to a method of predicting acute cardiopulmonary (ACP) events and survivability of a patient. The invention also relates to a system for predicting acute cardiopulmonary events and survivability of a patient.BACKGROUND OF THE INVENTION[0002]Triage is an important part of any Emergency Medical Response. This is the clinical process of rapidly screening large numbers of patients to assess severity and assign appropriate priority of treatment. Triage is a reality as medical resources are never enough for all patients to be attended instantaneously. It is thus important to be able to quickly identify patients of higher severity, who would need such resources more urgently. Therefore, a device for automatic patient outcome (cardiac arrest and mortality) analysis could be helpful to conduct triage, especially in disaster or mass casualty situations, where demand overwhelms resources.[0003]Current triage systems are based on clinical judgmen...

AI Technical Summary

Problems solved by technology

They tend to be subjective, and are not so convenient and efficient for clinicians.

Moreover, the clinical ‘vital signs’ including heart rate, respiratory rate, blood pressure, temperature and pulse oximetry have not been shown to correlate well with short or long-term clinical outcomes.

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