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Predicting acute cardiopulmonary events and survivability of a patient

  • US 9,295,429 B2
  • Filed: 12/12/2014
  • Issued: 03/29/2016
  • Est. Priority Date: 03/15/2010
  • Status: Active Grant
First Claim
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1. A method of producing an artificial neural network capable of predicting the survivability of a patient, the method comprising:

  • storing in an electronic database patient health data, the patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data, a second parameter relating to vital sign data, a third parameter relating to patient survivability, and a fourth parameter including at least one of ST segment elevation and depression;

    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, third, and fourth 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 within the next 72 hours.

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