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AI Biotech/Diagnostics: Other Innovation
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Application US20200353271
Published 2020-11-12
Selection Of Probability Thresholds For Generating Cardiac Arrhythmia Notifications
Techniques are disclosed for monitoring a patient for the occurrence of a cardiac arrhythmia. A computing system generates sample probability values by applying a machine learning model to sample patient data. The machine learning model determines a respective probability value that indicates a probability that the cardiac arrhythmia occurred during each respective temporal window. The computing system outputs a user interface comprising graphical data based on the sample probability values and receives, via the user interface, an indication of user input to select a probability threshold for a patient. The computing system receives patient data for the patient and applies the machine learning model to the patient data to determine a current probability value. In response to the determination that the current probability exceeds the probability threshold for the patient, the computing system generates an alert indicating the patient has likely experienced the occurrence of the cardiac arrhythmia.
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- 1. A method comprising:
generating, by a computing system that comprises processing circuitry and a storage medium, a set of sample probability values by applying a machine learning model to a sample set of patient data, wherein:
the machine learning model is trained using patient data for a plurality of patients,
the sample set comprises a plurality of temporal windows, and
for each respective temporal window of the plurality of temporal windows, the machine learning model is configured to determine a respective probability value in the set of sample probability values that indicates a probability that a cardiac arrhythmia occurred during the respective temporal window;
generating, by the computing system, graphical data based on the sample probability values; outputting, by the computing system, a user interface for display on a display device, the user interface comprising the graphical data; receiving, by the computing system, via the user interface, an indication of user input to select a probability threshold for a patient; receiving, by the computing system, patient data for the patient, wherein the patient data is collected by one or more medical devices; applying, by the computing system, the machine learning model to the patient data to determine a current probability value that indicates a probability that the patient has experienced an occurrence of a cardiac arrhythmia; determining, by the computing system, that the current probability value exceeds the probability threshold for the patient; and in response to determining that the current probability value is greater than or equal to the probability threshold for the patient, generating, by the computing system, a notification indicating that the patient has likely experienced the occurrence of the cardiac arrhythmia.
- 11. A computing system comprising:
one or more processing circuits; and a storage medium storing instructions that, when executed, configure the one or more processing circuits to:
generate a set of sample probability values by applying a machine learning model to a sample set of patient data, wherein:
the machine learning model is trained using patient data for a plurality of patients,
the sample set comprises a plurality of temporal windows, and
for each respective temporal window of the plurality of temporal windows, the machine learning model is configured to determine a respective probability value in the set of sample probability values that indicates a probability that a cardiac arrhythmia occurred during the respective temporal window;
generate graphical data based on the sample probability values;
output a user interface for display on a display device, the user interface comprising the graphical data;
receive, via the user interface, an indication of user input to select a probability threshold for a patient;
receive patient data for the patient, wherein the patient data is collected by one or more medical devices;
apply the machine learning model to the patient data to determine a current probability value that indicates a probability that the patient has experienced an occurrence of a cardiac arrhythmia;
determine that the current probability value exceeds the probability threshold for the patient; and
in response to determining that the current probability value is greater than or equal to the probability threshold for the patient, generate a notification indicating that the patient has likely experienced the occurrence of the cardiac arrhythmia.
- 20. A computer-readable data storage medium having instructions stored thereon that, when executed, cause one or more processing circuits of a computing system to:
generate a set of sample probability values by applying a machine learning model to a sample set of patient data, wherein:
the machine learning model is trained using patient data for a plurality of patients, the sample set comprises a plurality of temporal windows, and
for each respective temporal window of the plurality of temporal windows, the machine learning model is configured to determine a respective probability value in the set of sample probability values that indicates a probability that a cardiac arrhythmia occurred during the respective temporal window;
generate graphical data based on the sample probability values; output a user interface for display on a display device, the user interface comprising the graphical data; receive, via the user interface, an indication of user input to select a probability threshold for a patient; receive patient data for the patient, wherein the patient data is collected by one or more medical devices; apply the machine learning model to the patient data to determine a current probability value that indicates a probability that the patient has experienced an occurrence of a cardiac arrhythmia; determine that the current probability value exceeds the probability threshold for the patient; and in response to determining that the current probability value is greater than or equal to the probability threshold for the patient, generate a notification indicating that the patient has likely experienced the occurrence of the cardiac arrhythmia.
