Development and Initial Validation of a Self-Scored COPD Population Screener Questionnaire (COPD-PS)

COPD has a profound impact on daily life, yet remains underdiagnosed and undertreated. We set out to develop a brief, reliable, self-scored questionnaire to identify individuals likely to have COPD. COPD-PS™ development began with a list of concepts identified for inclusion using expert opinion from a clinician working group comprised of pulmonologists (n = 5) and primary care clinicians (n = 5). A national survey of 697 patients was conducted at 12 practitioner sites. Logistic regression identified items discriminating between patients with and without fixed airflow obstruction (AO, postbronchodilator FEV1/FVC < 70%). ROC analyses evaluated screening accuracy, compared scoring options, and assessed concurrent validity. Convergent and discriminant validity were assessed via COPD-PS and SF-12v2 score correlations. For known-groups validation, COPD-PS differences between clinical groups were tested. Test-retest reliability was evaluated in a 20% sample. Of 697 patients surveyed, 295 patients met expert review criteria for spirometry performance; 38% of these (n = 113) had results indicating AO. Five items positively predicted AO (p < 0.0001): breathlessness, productive cough, activity limitation, smoking history, and age. COPD-PS scores accurately classified AO status (area under ROC curve = 0.81) and reliable (r = 0.91). Patients with spirometry indicative of AO scored significantly higher (6.8, SD = 1.9; p < 0.0001) than patients without AO (4.0, SD = 2.3). Higher scores were associated with more severe AO, bronchodilator use, and overnight hospitalization for breathing problems. With the prevalence of COPD in the studied cohort, a score on the COPD-PS of greater than five was associated with a positive predictive value of 56.8% and negative predictive value of 86.4%. The COPD-PS accurately classified physician-reported COPD (AUC = 0.89). The COPD-PS is a brief, accurate questionnaire that can identify individuals likely to have COPD

An outbreak of blastomycosis in Eastern Tennessee

Most cases of blastomycosis are sporadic and only nine outbreaks representing a total of 112 cases have previously been reported. Less than half of these have been culture proven cases. Outbreaks have previously occurred in North Carolina, Minnesota, Illinois, Wisconsin and Virginia. We report three culturally confirmed cases of blastomycosis from Elizabethton, Tennessee, who had onset of illness within a one-week span of time. The patients presented with fever, chest pain, weight loss, poor appetite and myalgia. Each initially had a dry cough which became productive of purulent sputum as the illness progressed. Mild hemoptysis occurred during each patient\u27s course. Serologic testing by immunodiffusion and enzyme immunoassay were positive and testing by complement fixation was negative in each case. The diagnosis was made by histopathology on transbronchial biopsy or transthoracic needle aspiration material. Each patient improved on ketoconazole therapy

Imitation Learning:A Survey of Learning Methods

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions