Configurational Approach to Identify Concept Networks in selected Clinical Safety Incident Classes

Classifying clinical safety incidents (CSI) in their correct classes depends on the multiple concepts used to describe them. Machine learning based classification case study presented in this paper shows that it fails to identify the underlying complex concepts associations between the CSI classes. Two pairs of classes, each having high and low confused classes (as determined by the classifier), were further investigated by applying the set-theoretic-based logical synthesis methodology. The aim is to identify the relationships between concept networks for selected classes. The concept networks were identified using a set of 117 terms and measures taken included degree-centrality and in-betweenness centrality. In this study, using deterministic configurational approach, it is feasible to draw a meaningful relationship between concepts using the complex medical dataset sourced from the Incident Information Management System. The study is proof of concept that it is possible to identify concept networks and concept configuration rules for CSI classes

Health Risk associated by Traditional and Complementary Medicines (T&CM) with Special Reference to Herbal Medicines either used alone or Concomitant with Conventional Pharmaceuticals

Traditional and Complementary Medicines (T&CM) are an integral part of health care having deep cited roots in history for the treatment and prophylaction of various mental and physical diseases and to maintain health in good conditions. Although T&CM includes herbal medicines, acupuncture, yoga, and some other indigenous practices this review is mainly focusing on the safety issues associated with the use of herbal medicines (including herbs, herbal materials, herbal preparations, and finished herbal products) either used alone or combined with conventional pharmaceuticals. The trend of using T&CM is on the rise currently as these products are branded as completely safe and free from any kind of adverse effects. This misperception is wrong because a lot of intrinsic and extrinsic factors are responsible to affect the quality of these drugs resulting in severe health consequences. Misidentification of herbs, overdosing, adulteration, and the presence of environmental contaminants like pesticides, heavy metals, and microbial and fungal contaminants are some issues that account for the risks associated with herbal medicines. Another alarming aspect is the concurrent use of both herbal and conventional medicines resulting in interactions of natural phytochemicals with synthetic constituents of conventional medicines resulting in potentiating or antagonizing the pharmacological effects of drugs. The need for time to ensure the quality, safety, and efficacy of T&CM according to the standards of the World Health Organization to make rational use of herbal medicines safe and sound

A Network-Based Deterministic Model for Causal Complexity

Despite the widespread use of techniques and tools for causal analysis, existing methodologies still fall short as they largely regard causal variables as independent elements, thereby failing to appreciate the significance of the interactions of causal variables. The prospect of inferring causal relationships from weaker structural assumptions compels for further research in this area. This study explores the effects of the interactions of variables in the context of causal analysis, and introduces new advancements to this area of research. In this study, we introduce a new approach for the causal complexity with the goal of making the solution set closer to deterministic by taking into consideration the underlying patterns embedded within a dataset; in particular, the interactions of causal variables. Our model follows the configurational approach, and as such, is able to account for the three major phenomena of conjunctural causation, equifinality, and causal asymmetry

Novel Two-Stage Analytic Approach in Extraction of Strong Herb-Herb Interactions in TCM Clinical Treatment of Insomnia

2nd International Conference on Medical Biometrics, ICMB 2010, Hong Kong, China, Jun 28-30, 2010In this paper, we aim to investigate strong herb-herb interactions in TCM for effective treatment of insomnia. Given that extraction of herb interactions is quite similar to gene epistasis study due to non-linear interactions among their study factors, we propose to apply Multifactor Dimensionality Reduction (MDR) that has shown useful in discovering hidden interaction patterns in biomedical domains. However, MDR suffers from high computational overhead incurred in its exhaustive enumeration of factors combinations in its processing. To address this drawback, we introduce a two-stage analytical approach which first uses hierarchical core sub-network analysis to pre-select the subset of herbs that have high probability in participating in herb-herb interactions, which is followed by applying MDR to detect strong attribute interactions in the pre-selected subset. Experimental evaluation confirms that this approach is able to detect effective high order herb-herb interaction models in high dimensional TCM insomnia dataset that also has high predictive accuracies.Department of Health Technology and InformaticsRefereed conference pape