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AI Biotech/Diagnostics: Cardio

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Application US20190139631


Published 2019-05-09

Estimation And Use Of Clinician Assessment Of Patient Acuity

The present disclosure relates to estimation and use of clinician assessment of patient acuity. In various embodiments, a plurality of patient feature vectors associated with a plurality of respective patients may be obtained (302, 304). Each patient feature vector may include one or more health indicator features indicative of observable health indicators of a patient, and one or more treatment features indicative of characteristics of treatment provided to the patient. A machine learning model (216) may be trained (306) based on the patient feature vectors to receive, as input, subsequent patient feature vectors, and to provide, as output, indications of levels of clinician acuity assessment. Later, a patient feature vector associated with a given patient may be provided (404) as input to the machine learning model. Based on output from the machine learning model, a level of clinician acuity assessment associated with the given patient may be estimated (406) and used (408-416) for various applications.



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13 Independent Claims

  • 1. A system comprising: one or more processors; and memory coupled with the one or more processors, the memory storing instructions that, in response to execution of the instructions by the one or more processors, cause the one or more processors to: obtain a plurality of patient feature vectors associated with a plurality of patients, each patient feature vector including a plurality of health indicator features associated with a patient of the plurality of patients, and a plurality of treatment features associated with treatment of the patient by medical personnel based at least in part on the plurality of health indicator features associated with the patient; and train a machine learning model based on the patient feature vectors including the plurality of treatment features associated with treatment of the patient by medical personnel to receive, as input, subsequent patient feature vectors, and to provide, as output, indications of levels of clinician acuity assessment; provide one or more feature vectors that include health indicator features and treatment features associated with a given patient to the machine learning model as input; estimate a level of clinician acuity assessment of the given patient based on output of the machine learning model; and performing at least one of: adjusting one or more medical alarm thresholds based at least in part on the estimated level of clinician acuity assessment associated with the given patient; and providing output to medical personal advising on whether to admit, discharge, or transfer the given patient based at least in part on the estimated level of clinician acuity assessment associate with the given patient.

  • 8. (canceled)

  • 9. (canceled)

  • 10. (canceled)

  • 12. A computer-implemented method, comprising: obtaining, by one or more processors, a patient feature vector associated with a given patient, the patient feature vector including one or more health indicator features indicative of one or more observable health indicators of the given patient, and one or more treatment features indicative of one or more characteristics of treatment provided to the given patient; providing, by the one or more processors, as input to a machine learning model operated by the one or more processors, the patient feature vector; and estimating, by the one or more processors, based on output from the machine learning model, a level of clinician acuity assessment associated with the given patient.

  • 15. (canceled)

  • 16. (canceled)

  • 17. (canceled)

  • 19. (canceled)

  • 20. (canceled)

  • 21. (canceled)

  • 22. (canceled)

  • 23. (canceled)