Patent Forecast

1. A system comprising: (5) (3)

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.

2. The system of claim 1, wherein the memory further comprises instructions to: (0) (1)

3. The system of claim 1, further comprising instructions to: (0) (2)

4. The system of claim 1, further comprising instructions to determine that an objective acuity level of the given patient does not match the level of clinician acuity assessment of the given patient. (2) (0)

7. The system of claim 1, wherein at least one patient feature vector includes at least one of: (0) (4)

a feature indicative of whether a health parameter of a patient is being measured invasively or non-invasively;

a feature indicative of a frequency at which a health indicator of a patient is measured;

a feature indicative of whether a patient is supported by a life-critical system; and

a feature indicative of a dosage or duration of a medication administered to a patient.

11. The system of claim 1, wherein each of the plurality of patient feature vectors includes a label indicative of an outcome associated with the respective patient. (0)

8. (canceled) (0)

9. (canceled) (0)

10. (canceled) (0)

12. A computer-implemented method, comprising: (6) (3)

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.

13. The computer-implemented method of claim 12, further comprising 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. (0)

14. The computer-implemented method of claim 12, further comprising 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. (0)

18. The computer-implemented method of claim 12 comprising: (0) (3)

24. The computer-implemented method of claim 12, further comprising: determining that an objective acuity level of the given patient does not match the level of clinician acuity assessment of the given patient. (0)

25. The computer-implemented method of claim 12, further comprising: providing output to medical personnel to instruct the medical personnel that a current clinician assessment of the patient's acuity is inaccurate. (0)

26. The computer-implemented method of claim 12, further comprising: altering a manner in which an indicator of an objective acuity level of the given patient is output to medical personnel to notify the medical personnel that additional concern for the given patient is warranted. (0)

15. (canceled) (0)

16. (canceled) (0)

17. (canceled) (0)

19. (canceled) (0)

20. (canceled) (0)

21. (canceled) (0)

22. (canceled) (0)

Application US20190139631

ESTIMATION AND USE OF CLINICIAN ASSESSMENT OF PATIENT ACUITY

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