Environmental assessment proposed license renewal of Nuclear Metals, Inc. Concord, Massachusetts

The US Nuclear Regulatory Commission (NRC) has prepared this Environmental Assessment (EA) to evaluate environmental issues associated with the renewal of licenses issued by NRC for facilities operated by Nuclear Metals, Inc. (NMI) in Concord, Massachusetts. By renewing the licenses, NRC proposes to allow the continuation of ongoing operations involving radioactive materials at NMI`s facilities. This EA focuses on the potential impacts related to air emissions at NMI during normal (incident-free) operations and accidental releases. Findings indicate that there are only two areas of potential concern. First, modeling results for sulfur dioxide (SO{sub 2}) emissions from the boilers during normal operations indicate that the potential exists for exceeding the short-term National Ambient Air Quality Standards (NAAQS). NMI is prepared to undertake mitigative action to prevent potential exceedances of the short-term SO{sub 2} NAAQS, and the Massachusetts Department of Environmental Protection is prepared to resolve the issue via a permit/approval change or through a Consent Order. Second, in the unlikely event of a severe fire, predicted sulfuric acid (H{sub 2}SO{sub 4}) concentrations based on conservative (upper bound) modeling exceed the Emergency Response Planning Guideline (ERPG) levels. NMI has committed to NRC to give a briefing for local emergency response officials regarding the potential for an accidental H{sub 2}SO{sub 4} release

What factors affect patients' recall of general practitioners' advice?

<p>Abstract</p> <p>Background</p> <p>In order for patients to adhere to advice, provided by family doctors, they must be able to recall it afterwards. However, several studies have shown that most patients do not fully understand or memorize it. The aim of this study was to determine the influence of demographic characteristics, education, amount of given advice and the time between consultations on recalled advice.</p> <p>Methods</p> <p>A prospective survey, lasting 30 months, was conducted in an urban family practice in Slovenia. Logistic regression analysis was used to identify the risk factors for poorer recall.</p> <p>Results</p> <p>250 patients (87.7% response rate) received at least one and up to four pieces of advice (2.4 ± 0.8). A follow-up consultation took place at 47.4 ± 35.2 days. The determinants of better recall were high school (OR 0.4, 95% CI 0.15-0.99, p = 0.049) and college education (OR 0.3, 95% CI 0.10-1.00, p = 0.050), while worse recall was determined by number of given instructions three or four (OR 26.1, 95% CI 3.15-215.24, p = 0.002; OR 56.8, 95% CI 5.91-546.12, p < 0.001, respectively) and re-test interval: 15-30 days (OR 3.3, 95% CI 1.06-10.13, p = 0.040), 31-60 days (OR 3.2, 95% CI 1.28-8.07, p = 0.013) and more than 60 days (OR 2.5, 95% CI 1.05-6.02, p = 0.038).</p> <p>Conclusions</p> <p>Education was an important determinant factor and warrants further study. Patients should be given no more than one or two instructions in a consultation. When more is needed, the follow-up should be within the next 14 days, and would be of a greater benefit to higher educated patients.</p

Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte

Predicting sample size required for classification performance

<p>Abstract</p> <p>Background</p> <p>Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target.</p> <p>Methods</p> <p>We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method.</p> <p>Results</p> <p>A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p < 0.05).</p> <p>Conclusions</p> <p>This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.</p