Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification algorithm. Such techniques, called oversamplers, modify the training data, allowing any classifier to be used with class-imbalanced datasets. Many algorithms have been proposed for this task, but most are complex and tend to generate unnecessary noise. This work presents a simple and effective oversampling method based on k-means clustering and SMOTE oversampling, which avoids the generation of noise and effectively overcomes imbalances between and within classes. Empirical results of extensive experiments with 71 datasets show that training data oversampled with the proposed method improves classification results. Moreover, k-means SMOTE consistently outperforms other popular oversampling methods. An implementation is made available in the python programming language.Comment: 19 pages, 8 figure

Contemporary NSTEMI management: the role of the hospitalist.

Non-ST-segment elevation myocardial infarction (NSTEMI) is defined as elevated cardiac biomarkers of necrosis in the absence of persistent ST-segment elevation in the setting of anginal symptoms or other acute event. It carries a poorer prognosis than most ST-segment elevation events, owing to the typical comorbidity burden of the older NSTEMI patients as well as diverse etiologies that add complexity to therapeutic decision-making. It may result from an acute atherothrombotic event (\u27Type 1\u27) or as the result of other causes of mismatch of myocardial oxygen supply and demand (\u27Type 2\u27). Regardless of type and other clinical factors, the hospital medicine specialist is increasingly responsible for managing or coordinating the care of these patients. Following published guidelines for risk stratification and basing anti-anginal, anticoagulant, antiplatelet, other pharmacologic therapies, and overall management approach on that individualized patient risk assessment can be expected to result in better short- and long-term clinical outcomes, including near-term readmission and recurrent events. We present here a review of the evidence basis and expert commentary to assist the hospitalist in achieving those improved outcomes in NSTEMI. Given that the Society for Hospital Medicine cites care of patients with acute coronary syndrome as a core competency for hospitalists, it is essential that those specialists stay current on optimal NSTEMI care

Classification of Humans into Ayurvedic Prakruti Types using Computer Vision

Ayurveda, a 5000 years old Indian medical science, believes that the universe and hence humans are made up of five elements namely ether, fire, water, earth, and air. The three Doshas (Tridosha) Vata, Pitta, and Kapha originated from the combinations of these elements. Every person has a unique combination of Tridosha elements contributing to a person’s ‘Prakruti’. Prakruti governs the physiological and psychological tendencies in all living beings as well as the way they interact with the environment. This balance influences their physiological features like the texture and colour of skin, hair, eyes, length of fingers, the shape of the palm, body frame, strength of digestion and many more as well as the psychological features like their nature (introverted, extroverted, calm, excitable, intense, laidback), and their reaction to stress and diseases. All these features are coded in the constituents at the time of a person’s creation and do not change throughout their lifetime. Ayurvedic doctors analyze the Prakruti of a person either by assessing the physical features manually and/or by examining the nature of their heartbeat (pulse). Based on this analysis, they diagnose, prevent and cure the disease in patients by prescribing precision medicine. This project focuses on identifying Prakruti of a person by analysing his facial features like hair, eyes, nose, lips and skin colour using facial recognition techniques in computer vision. This is the first of its kind research in this problem area that attempts to bring image processing into the domain of Ayurveda

Microbial imbalance in inflammatory bowel disease patients at different taxonomic levels

Background Inflammatory bowel disease (IBD), is a debilitating group of chronic diseases including Crohn’s Disease (CD) and ulcerative colitis (UC), which causes inflammation of the gut and affects millions of people worldwide. At different taxonomic levels, the structure of the gut microbiota is significantly altered in IBD patients compared to that of healthy individuals. However, it is unclear how these IBD-affected bacterial groups are related to other common bacteria in the gut, and how they are connected across different disease conditions at the global scale. Results In this study, using faecal samples from patients with IBD, we show through diversity analysis of the microbial community structure based on the 16S rRNA gene that the gut microbiome of IBD patients is less diverse compared to healthy individuals. Furthermore, we have identified which bacterial groups change in abundance in both CD and UC compared to healthy controls. A substantial imbalance was observed across four major bacterial phyla including Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria, which together constitute >98% of the gut microbiota. Next, we reconstructed a bacterial family co-abundance network based on the correlation of abundance profiles obtained from the public gut microbiome data of >22000 samples of faecal and gut biopsies taken from both diseased and healthy individuals. The data was compiled using the EBI metagenomics database [1]. By mapping IBD-altered bacterial families to the network, we show that the bacterial families which exhibit an increased abundance in IBD conditions are not well connected to other groups, implying that these families generally do not coexist together with common gut organisms. Whereas, the bacterial families whose abundance is reduced or did not change in IBD conditions compared to healthy conditions are very well connected to other bacterial groups, suggesting they are highly important groups of bacteria in the gut that can coexist with other bacteria across a range of conditions. Conclusions IBD patients exhibited a less diverse gut microbiome compared to healthy individuals. Bacterial groups which changed in IBD patients were found to be groups which do not co-exist well with common commensal gut bacteria, whereas bacterial groups which did not change in patients with IBD were found to commonly co-exist with commensal gut microbiota. This gives a potential insight into the dynamics of the gut microbiota in patients with IBD

Do antidepressants cure or create abnormal brain states?

Moncrieff and Cohen argue that psychotropic drugs create abnormal states that may co-incidentally relieve symptoms of mental illness

Pharmacology of Antiparkinsonian Agents

The following is a summary of a two hour class on the basic pharmacology of antiparkinsonian agents. It is presented to fourth-year pharmacy students in pharmacotherapeutics III, a course structured using team-taught modules. Faculty from the Department of Pharmacy Sciences provide instruction on the basic pharmacology of therapeutic agents and faculty from the Department of Pharmacy Practice follow up with a discussion of the therapeutic applications of these agents. This course is lecture-based with opportunities for in-class discussion. One week prior to the lecture sequence on the basic pharmacology of antiparkinsonian drugs, students are provided a handout that includes the reading assignment (1), learning objectives and a topic outline. The topic outline contains the chemical structures of the agents to be discussed as well as the figures, patient scenarios and study questions appearing in this manuscript. During each 50- minute period, material is presented as a lecture tied to patient scenarios. The scenarios are presented in class immediately after covering the pharmacological concepts to which they apply. Students are asked to discuss in small groups potential solutions to the scenarios and to offer their answers to the rest of the class on a volunteer basis. The study questions are geared for preparing for exams and are not discussed in class unless students request. At the end of these two lectures, a homework problem is assigned that introduces the 6-hydroxydopamine rat model of Parkinson’s disease. The following week, a live demonstration related to the homework is presented in class with a short discussion afterwards

Minimal Model for an Unbalanced Holographic Superconductor

We describe the simplest holographic model for an s-wave unbalanced superconductor in 2+1 dimensions. We study its phase diagram and linear response features with particular attention to the possibility of spatially modulated phases (LOFF) and mixed spin-electric properties. The normal phase of the model at hand allows us to analyze a strong-coupling generalization of Mott two-current model for spintronic systems; the superconducting phase features an interesting DC spin-superconductivity without spin-symmetry breaking .Comment: 14 pages, 12 figures, Proceedings of the Corfu Summer Institute 2012 "School and Workshops on Elementary Particle Physics and Gravity", September 8-27, 2012, Corfu, Greec