Behavioral Analytics for Medical Decision Support: Supporting dementia diagnosis through outlier detection

We live in a world that is ageing at a rapid pace and where life expectancy is growing. This is a phenomena commonly known as demographic change and has been a focus of various fields of study. With the number of elders rapidly increasing, their problems, in particular health related ones, are a major concern. As many of our elders live alone at their homes tracking their health and behaviours is essential to diagnose and address several health problems that may arise. Dementia is a syndrome that can be caused by several diseases being the most well-known Alzheimer’s disease.

It is characterized by a progressive loss of cognitive abilities that ultimately lead to a complete dependence on others to carry out even the most basic tasks of everyday living.

Although there is no known cure for dementia there are some treatments that have shown interesting results in retarding its evolution. But all the results that any treatment can get are greatly dependent on an as early as possible diagnose because treatments starting at later stages have little or no effect in stopping the progress of dementia.

The first symptoms of dementia are small changes and difficulties experienced by the elder person. Those details will easily go unnoticed by the elder, family members or other caregivers and may not be duly reported to the health professional that could give a diagnosis. Therefore it is important to find other ways of detecting those small changes. It is here where ubiquitous collection of data through the usage of sensors plays an important part. With a well-structured sensing platform it is possible to collect detailed information.

But the information itself is not enough. Therefore that data needs to be treated and analyzed in order to present pertinent information, that might be valuable to the diagnosis process.

This project proposes to design a system that receives information from various different sources and analyses that data to find possible dementia signs. But before trying to find these possible signs the system establishes the normal pattern of behaviour of a person as a baseline for future analysis. In order to find those signs of dementia, outlier detection techniques and algorithms are used during the analysis process.

All the information that enters the system and resultant from its analysis is then stored to be available for consultation by health professionals, caregivers and elders. This information is then presented in a web visualization that focuses in showing the information in a meaningful way, without any unnecessary and distracting elements.

The system was tested using real world data sets and data produced by Java programmes created specifically for each usage scenario. With the conducted tests was possible to see that this system is able to detect outliers in different types of data, after comparing the data that continuously arrived with the normal patterns established for a user. With these results it is possible to infer that such a system could be in fact a valuable tool in the diagnosis process of dementia and in monitoring its development as well.

 

For any additional information regarding this project, please contact us using the inquiries form.