System, method, and software for automated detection of predictive events

• US 20100066540A1
• Filed: 08/28/2009
• Published: 03/18/2010
• Est. Priority Date: 11/23/2004
• Status: Abandoned Application

• Current Assignee: Vecna Incorporated
• Original Assignee: Vecna Incorporated
• Inventors: Theobald, Daniel, Niemczyk, Steven
• Application Number:
  US12/583,978
• Publication Number:
  US 20100066540A1
• Time in Patent Office: Days
• US Class Current: 340/573.100
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/80 for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

A system for the automatic detection and communication of detection of nosocomial infection and/or antimicrobial resistance events in a health care environment includes an input unit that receives nosocomial infection and/or antimicrobial resistance related data, an an event detection machine, and a knowledge discovery unit. The event detection machine sorts and analyzes the nosocomial infection and/or antimicrobial resistance related data to automatically generate alerts for isolates that violate control parameters indicative of a nosocomial infection and/or antimicrobial resistance event and communicates the alert to a user.

Claims

1. A system for the automatic detection and communication of detection of nosocomial infection and any antimicrobial resistance events in a health care environment comprising:

an input unit that receives nosocomial infection and any antimicrobial resistance related data;

an event detection machine;

a knowledge discovery unit; and

a user interface;

wherein the event detection machine sorts and analyzes the nosocomial infection and any antimicrobial resistance related data to automatically generate alerts for isolates that violate control parameters indicative of a nosocomial infection and any antimicrobial resistance event so that effective antibiotics can be used to properly treat the nosocomial infection;

and wherein the user interface communicates the alerts to a user.

2. The system of claim 1, wherein the received nosocomial infection and any microbial resistance related data is stored in a persistence database which is used by the event detection machine.

3. The system of claim 1, wherein the user interface allows the user to use and interpret analysis results provided by the event detection machine and to define nosocomial infection and any microbial resistance detection parameters.

4. The system of claim 1, wherein the event detection machine comprises:

a plurality of filter banks that filter the received nosocomial infection and any antimicrobial resistance related data based on the control parameters;

a plurality of signal generators that work with the output the filter bank in encoding a data signal with attribute associations based on the control parameters;

a plurality of signal analysis modules which detect nosocomial infection and any antimicrobial resistance events in the data signal; and

a plurality of outputs displaying the results of the event detection.

5. The system of claim 4, wherein the plurality of signal analysis modules comprise implementations of simple control charts, event-interval analysis, moving average analysis, and/or binary cumulative sum analysis.

6. The system of claim 4, such that the plurality of filter banks comprise a phenotype grouping filter that sorts isolates into categories by measuring phenotype instability.

7. The system of claim 6, wherein the phenotype grouping filter is optimized by obtaining a fuzzy logic determination of resistance phenotype sets.

8. The system of claim 4, wherein the plurality of signal generators take an isolate record and convert it into a symbolic representation, generate a sequence using continuous values, or perform calculations using multiple parameters.

9. The system of claim 1, wherein the event detection machine uses simple control analysis, moving average analysis, event-integral analysis, cumulative sum analysis, scan statistics, empty cell analysis, Fourier and Wavelet transforms, and/or least squares regression to analyze the data and generate alerts.

10. The system of claim 4, such that the plurality of signal analysis modules are configured by the knowledge discovery unit that uses evolutionary algorithms to automatically program the event detection machine.

11. The system of claim 10, wherein the event detection machine is configured by implementing the following evolutionary algorithms steps:

a generation zero step wherein a zero generation graph is created by randomly connecting analysis modules to the graph;

a calculation of fitness step wherein the fitness is calculated iteratively using a fitness function wherein if at any time the fitness drops below a level that would prevent a calculated fitness from achieving a composite score above the mean of the last generation, testing is stopped; and

an apply selection, crossover, and mutation step wherein traits are carried forward from one generation to a next generation by deciding which trait has the highest chance of producing a viable solution.

12. The system of claim 11, wherein the apply selection, crossover, and mutation step comprises:

a ranking step wherein the solutions are ranked in the order of fitness;

an elimination step wherein the solutions are eliminated using a probability of rank divided by population size;

a crossover step wherein the empty spots created by the elimination step are filled by the crossover of the remaining solutions; and

a mutation step wherein parameter values may be changed or a vertex from the graph may be removed or graph vertex may be changed.

13. The system of claim 4, wherein the knowledge discovery unit comprises statistical process control nodules that monitor for outbreaks caused by a single organism by monitoring for phenotypically similar strains.

14. A method of automatically detecting nosocomial infection and/or microbial resistance events in a healthcare environment comprising the steps of:

receiving a nosocomial infection and any antimicrobial resistance related data;

developing an event detection machine that automatically sorts and analyzes the nosocomial infection and any antimicrobial related data and automatically generates an alert when an isolate violates control parameters indicative of a nosocomial infection and any microbial resistance; and

communicating the generated alert automatically to a user.

15. The method according to claim 14, further comprising storing the received nosocomial infection and any antimicrobial resistance related data in a persistence database which is accessible to the event detection machine.

16. The method according to claim 14, further comprising:

providing a plurality of filter banks that filter the received nosocomial infection and any antimicrobial resistance data based on control parameters;

providing a plurality of signal generators that work with the output of the filter banks to encode a data signal with attribute associations based on the control parameters; and

providing a plurality of signal analysis modules which detect the nosocomial infection and any antimicrobial resistance events in the data signal.

17. The method according to claim 16, further comprising:

providing a knowledge discovery unit that uses evolutionary algorithms to configure the signal analysis modules in the event detection machine.

18. A computer readable medium having program code recorded thereon that, when executed on a computing system causes the performance of the steps comprising:

receiving a nosocomial infection and any antimicrobial resistance related data;

developing an event detection machine that automatically sorts and analyzes the nosocomial infection and any antimicrobial related data and automatically generates an alert when an isolate violates control parameters indicative of a nosocomial infection and any microbial resistance; and

communicating the generated alert automatically to a user.

19. The computer readable medium according to claim 18, wherein the program code is further configured to store the received nosocomial infection and any antimicrobial resistance related data in a persistence database which is accessible to the event detection machine.

20. The computer readable medium according to claim 18, wherein the program code is further configured to use evolutionary algorithms in the development of the event detection machine.

21. A method of detecting nosocomial infection and any associated antimicrobial resistance events in a healthcare environment and communicating any antimicrobial confirmation or re-prescription information comprising the steps of:

receiving nosocomial infection and any antimicrobial resistance related data in real-time;

sorting and analyzing the nosocomial infection and any antimicrobial resistance related data;

determining whether one or more nosocomial infections have any antimicrobial resistance data associated therewith;

providing at least one notification with regard to at least one of the patients infected with a particular nosocomial infection that includes information related to the antimicrobial resistance of an antimicrobial currently in use by the at least one patient; and

including with the notification at least one of confirming usage of the current antimicrobial or recommending at least one other antimicrobial for treating the particular nosocomial infection.