Exploring ant-based algorithms for gene expression data analysis

Objective: Recently, much research has been proposed using nature inspired algorithms to perform complex machine learning tasks. Ant colony optimization (ACO) is one such algorithm based on swarm intelligence and is derived from a model inspired by the collective foraging behavior of ants. Taking advantage of the ACO in traits such as self-organization and robustness, this paper investigates ant-based algorithms for gene expression data clustering and associative classification. Methods and material: An ant-based clustering (Ant-C) and an ant-based association rule mining (Ant-ARM) algorithms are proposed for gene expression data analysis. The proposed algorithms make use of the natural behavior of ants such as cooperation and adaptation to allow for a flexible robust search for a good candidate solution. Results: Ant-C has been tested on the three datasets selected from the Stanford Genomic Resource Database and achieved relatively high accuracy compared to other classical clustering methods. Ant-ARM has been tested on the acute lymphoblastic leukemia (ALL)/acute myeloid leukemia (AML) dataset and generated about 30 classification rules with high accuracy. Conclusions: Ant-C can generate optimal number of clusters without incorporating any other algorithms such as K-means or agglomerative hierarchical clustering. For associative classification, while a few of the well-known algorithms such as Apriori, FP-growth and Magnum Opus are unable to mine any association rules from the ALL/AML dataset within a reasonable period of time, Ant-ARM is able to extract associative classification rules.

Developing an intervention to equip nurses for acute life threatening events (ALTEs) in hospital:a phenomenological approach to healthcare research

Objectives: To understand staff's experiences of acute life threatening events (ALTEs) in a pediatric hospital setting. These data will inform an intervention to equip nurses with clinical and emotional skills for dealing with ALTEs. Method: A mixed design was used in the broader research program; this paper focuses on phenomenon-focused interviews analyzed using interpretative phenomenological analysis (IPA). Results: Emerging themes included staff's relationships with patients and the impact of personhood on their ability to perform competently in an emergency. More experienced nurses described "automatic" competence generated through increased exposure to ALTEs and were able to recognize "fumbling and shaking" as a normal stress response. Designating a role was significant to staff experience of effectiveness. Key to nurses' learning experience was reflection and identifying experiences as "teachable moments." Findings were considered alongside existing theories of self-efficacy, reflective thought, and advocacy inquiry to create an experiential learning intervention involving a series of clinical and role-related scenarios. Conclusion: The phenomenological work facilitated an in-depth reading of experience. It accentuated the importance of exposure to ALTEs giving nurses experiential knowledge to prepare them for the impact of these events. Challenges included bracketing the personhood of child patients, shifting focus to clinical tasks during the pressured demands of managing an ALTE, normalizing the physiological stress response, and the need for a forum and structure for reflection and learning. An intervention will be designed to provide experiential learning and encourage nurses to realize and benefit from their embodied knowledge.