Medical decision support systems utilizing gene expression and clinical information and method for use

• US 20060129034A1
• Filed: 08/15/2003
• Published: 06/15/2006
• Est. Priority Date: 08/15/2002
• Status: Abandoned Application

• Current Assignee: Pacific Edge Biotechnology Limited
• Original Assignee: Pacific Edge Biotechnology Limited
• Inventors: Reeve, Anthony Edmund, Kasabov, Nikola Kirilov, Sullivan, Michael James, Futschik, Matthias Erwin
• Application Number:
  US10/524,754
• Publication Number:
  US 20060129034A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/20 Supervised data analysis
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Embodiments of this invention provide improved medical decision support systems and methods for using such systems to simultaneously consider two or more different types of information along with estimates of accuracies of the information to produce a combined predictor. Such predictors have greater accuracy compared to use of the individual types of information alone. Increased accuracy can increase the likelihood of correct diagnosis and/or evaluation of clinical condition or outcome, and can decrease the frequency of false negative results, including misdiagnosis. Embodiments of medical decision support systems can include EFuNN, Bayesian or other statistical estimators to produce a combined predictor. The systems can be used to extract relationship rules between sets of genes and clinical variables common for patients of a group, thus making a personalized gene-based treatment possible. Such systems are incorporated into computer-based devices and are run using suitable computer programs. Outputs can be directed to hard-copy devices for printing, or can be transmitted remotely to a terminal at a location where a practitioner is interacting with a patient.

Claims

1. A medical decision support system, comprising:

a processor;

a memory device;

an input for acquiring gene expression data, said input associated with a first classifier/predictor module;

an input for acquiring clinical information, said input associated with a second classifier/predictor module; and

a program available to said processor comprising a combination algorithm.

2. The system of claim 1, further comprising an output device.

3. A method for support a medical decision on a computer system, comprising the steps of:

(a) classifying genetic expression information using a first classifier/predictor module to provide classified gene expression information;

(b) classifying clinical information into a second classifier/predictor module to provide classified clinical information; and

(c) combining said classified genetic information and said classified clinical information into a predicted outcome.

4. The method of claim 3, wherein said steps (a) and (b) include at least one of an EFuNN process and a Bayesian process.

5. A computer system to support a medical decision, comprising:

a processor; and

a memory device having classified gene expression information and classified clinical information stored thereon.

6. The computer system of claim 5, further comprising a predicted outcome based on combined classified gene expression information and clinical information stored on said memory device.

7. A method for extracting relationship rules between sets of genes and clinical variables common for patients of a group substantially as herein described.