Modelling behavioral syndromes using Bayesian networks

Information Technology in Health Care - What the Future Holds

In 1998, Malaysia opened its first hospital based on the "paperless and filmless" concept. Two are now in operation, with more to follow. Telemedicine is now being used in some hospitals and is slated to be the technology to watch. Future use of technology in health care will centre on the use of centralised patient databases and more effective use of artificial intelligence. Stumbling blocks include the enormous capital costs involved and difficulty in getting sufficient bandwidth to support applications on a national scale. Problems with the use of information technology in developing countries still remain; mainly inadequate skilled resources to operate and maintain the technology, lack of home-grown technology, insufficient experience in the use of information technology in health care and the attitudes of some health staff. The challenge for those involved in this field will not be in building new "paperless and filmless" institutions but in transforming current "paper and film-based" institutions to "paperless and filmless" ones and changing the mindset of health staff. Universities and medical schools must be prepared to respond to this new wave by incorporating elements of medical/health informatics in their curriculum and assisting governments in the planning and implementation of these projects. The experience of the UMMC is highlighted as an example of the difficulty of transforming a paper-based hospital to a "paperless and filmless" hospital. Asia Pac J Public Health 2004; 16(1): 64-71.

Understanding disease processes by partitioned dynamic Bayesian networks

For many clinical problems in patients the underlying pathophysiological process changes in the course of time as a result of medical interventions. In model building for such problems, the typical scarcity of data in a clinical setting has been often compensated by utilizing time homogeneous models, such as dynamic Bayesian networks. As a consequence, the specificities of the underlying process are lost in the obtained models. In the current work, we propose the new concept of partitioned dynamic Bayesian networks to capture distribution regime changes, i.e. time non-homogeneity, benefiting from an intuitive and compact representation with the solid theoretical foundation of Bayesian network models. In order to balance specificity and simplicity in real-world scenarios, we propose a heuristic algorithm to search and learn these non-homogeneous models taking into account a preference for less complex models. An extensive set of experiments were ran, in which simulating experiments show that the heuristic algorithm was capable of constructing well-suited solutions, in terms of goodness of fit and statistical distance to the original distributions, in consonance with the underlying processes that generated data, whether it was homogeneous or non-homogeneous. Finally, a study case on psychotic depression was conducted using non-homogeneous models learned by the heuristic, leading to insightful answers for clinically relevant questions concerning the dynamics of this mental disorder.

Big data analysis using modern statistical and machine learning methods in medicine

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

The BUGS project: Evolution, critique and future directions

BUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instrumental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives.