METHOD OF CONTINUOUS PREDICTION OF PATIENT SEVERITY OF ILLNESS, MORTALITY, AND LENGTH OF STAY

• US 20130232103A1
• Filed: 11/03/2011
• Published: 09/05/2013
• Est. Priority Date: 11/08/2010
• Status: Active Grant

• Current Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Saeed, Mohammed
• Granted Patent: US 9,569,723 B2
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G01N 2800/52 Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A method for predicting a patient'"'"'s outcome variable, such as a probability of mortality/recovery, includes accessing at least one of a plurality past patients'"'"' data fields including physiological and/or laboratory data, and a time of stay indicating how long each patient had been under care at the time. An outcome variable estimation algorithm is generated by data mining from a plurality of past patients'"'"' physiological and/or laboratory data, corresponding time of stay, and associated outcome variables. A current patient'"'"'s outcome variable is determined from the current patient'"'"'s physiological and/or laboratory data, the current time of stay, and the outcome variable estimation algorithm.

Claims

1. A patient information device comprising:

a controller which receives physiological and/or laboratory data about a current patient from a patient monitoring device;

a unit which determines a time of stay indicative of a time that the current patient has been under care; and

a prediction unit which predicts an outcome variable by applying an outcome variable estimation algorithm to the current patient'"'"'s physiological and/or laboratory data using the determined time of stay,

3. The device according claim 1, wherein the prediction unit predicts the outcome variable continuously in real time.

4. The device according to claim 1, wherein the outcome variable prediction algorithm is generated by:

accessing at least one of a plurality past patients'"'"' data fields including physiological and/or laboratory data, and a time of stay indicating how long each patient had been under care at the time the physiological and/or laboratory data were generated and associated outcome variables from a clinical database;

generating a outcome variable estimation algorithm from the plurality past patients physiological and/or laboratory data,. corresponding time of stay, and associated outcome variables.

5. The device according to claim 1, wherein the outcome variable estimation algorithm is generated using at least one of linear regression analysis, nonlinear regression analysis, neural networks, support vector machines, radial basis functions, expert rule based, and classifier decision trees.

6. A patient monitoring system comprising:

the patient information device according to claim 1; and

the patient monitor device which collects physiological data from a patient;

7. The system according to claim 6, further including:

a laboratory server which transmits laboratory data of the patient to the patient information device.

8. The system according to claim 6, further including:

a display which displays the predicted outcome variable.

2. The device according to claim I, wherein the outcome variables include at least one of a length of stay, a probability of mortality, and a need for intervention.

9. A method for predicting a patient'"'"'s outcome variable, the method comprising:

accessing at least one of a plurality past patients'"'"' data fields including physiological and or laboratory data, and a time of stay indicating how long each patient had been under care at the time the physiological and, or laboratory data were generated and associated outcome variables from a clinical database;

generating a outcome variable estimation algorithm from the plurality past patients physiological and/or laboratory data, corresponding time of stay, and associated outcome variables.

10. The method according to claim 9, wherein the outcome variables include at least one of a length of stay, a probability of mortality, and a need for intervention.

11. The method according to claim 9, further including:

receiving a current patient'"'"'s physiological and/or laboratory data;

determining the current patient'"'"'s current time of stay under care; and

applying the outcome variable estimation algorithm to the current patient'"'"'s physiological and/or laboratory data and the current time of stay to estimate the current patient'"'"'s probability of outcome.

12. The method according claim 9, wherein in the past patient data fields include at least one of:

one or more vital signs, automated or user-input problem lists including chronic problems, acute admitting problems, clinical laboratory data, fluid-balance, medications, ventilator settings, subjective nursing assessment, results of imaging studies, patient demographics, and current length of stay in the hospital or ICU.

13. The method according to claim 9, wherein the past patient associated outcome variables include at least one of:

time of stay, mortality, and need for an intervention.

14. The method according to claim 9, wherein the outcome variable estimation algorithm is identified using at least one of:

linear regression analysis, nonlinear regression analysis, neural networks, support vector machines, radial basis functions, expert rule-based, and classifier decision trees.

15. The method according to claim 9, wherein the prediction of the current patient'"'"'s outcome variable is continuous in real time.

16. A computer readable medium containing software which when loaded into a processor programs the processor to perform the method according to claim 9.

17. A method of predicting a probability of outcome, comprising measuring physiological and/or laboratory data for a current patient who is staying under care in a medical facility;

determining a time of stay which the current patient has been staying under care in the medical facility;

applying a time of stay dependent outcome prediction algorithm using the measured physiological and/or laboratory data and the determined time of stay to predict a probability of outcome.

18. A computer readable medium containing software which when loaded into a processor programs the processor to perform the method according to claim 17.

19. A processor programmed to perform the method according to claim 17.

20. A patient information system comprising:

a patient information unit which receives physiological and/or laboratory data from a current patient;

a unit which calculates time of stay;

a prediction processor which programmed to perform the method of claim 17; and

a display which displays the probability of outcome.