Personalised Modelling Software for Static Data
The Brain-Like Artificial Intelligence (BLAI) is pioneered by Prof.Nikola Kasabov and here it is applied to a specific application.
This project develops methods and systems for personalised modelling (PM). Personalised modelling aims to create a unique computational diagnostic model for each individual, taking into considering the fact that each individual is different and the most effective treatment for an individual can be achieved by a detailed analysis of the data available for that particular individual. The rationale behind personalised modelling is that each person is different and hence they have different requirements and responses to the same treatment. The most effective treatment for an individual can thus be achieved only by the detailed analysis of data available for the patient.
FIGURE. Conceptual Framework for Personalised Modelling for Medical Decision Support.
Related Papers, Patents and Benchmarking
The proposed methods and systems, when compared with traditional statistical and machine learning methods, showed superior results in the following aspects:
- Better data analysis and classification accuracy (~10%);
- Better prognostic accuracy and a computed personalised profile;
- It enables better visualisation of the risk factors for a particular sample.
See also some of the related papers:
Kasabov, N., Feigin, V., Hou, Z., Chen, Y. (2016). Improved method and system for predicting outcomes based on spatio/spectro-temporal data, PCT patent WO2015/030606 A2, US2016/0210552 A1. Granted/Publication date: 21 July 2016.
Kasabov, N. (2015). Data Analysis and Predictive Systems and Related Methodologies, US patent 9,002,682 B2, 7 April 2015.
Kasabov, N. (2007). Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach. Pattern Recognition Letters, 28(6), 673-685.
Kasabov, N., & Hu, Y. (2010). Integrated optimisation method for personalised modelling and case studies for medical decision support. International Journal of Functional Informatics and Personalised Medicine, 3(3), 236-256.
The software architecture mentioned above is being implemented in Python language as Open Source Software which is currently under development and will be available subject to licensing in 2018.
The developer of this project is: