Salivary and lacrimal dysfunction after radioactive iodine for differentiated thyroid cancer: American Head and Neck Society Endocrine Surgery Section and Salivary Gland Section joint multidisciplinary clinical consensus statement of otolaryngology, ophthalmology, nuclear medicine and endocrinology

BackgroundPostoperative radioactive iodine (RAI) administration is widely utilized in patients with differentiated thyroid cancer. While beneficial in select patients, it is critical to recognize the potential negative sequelae of this treatment. The prevention, diagnosis, and management of the salivary and lacrimal complications of RAI exposure are addressed in this consensus statement.MethodsA multidisciplinary panel of experts was convened under the auspices of the American Head and Neck Society Endocrine Surgery and Salivary Gland Sections. Following a comprehensive literature review to assess the current best evidence, this group developed six relevant consensus recommendations.ResultsConsensus recommendations on RAI were made in the areas of patient assessment, optimal utilization, complication prevention, and complication management.ConclusionSalivary and lacrimal complications secondary to RAI exposure are common and need to be weighed when considering its use. The recommendations included in this statement provide direction for approaches to minimize and manage these complications.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163491/2/hed26417.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163491/1/hed26417_am.pd

Recurrent type-1 fuzzy functions approach for time series forecasting

Tak, Nihat/0000-0001-8796-5101; Egrioglu, Erol/0000-0003-4301-4149WOS: 000419006000005Forecasting the future values of a time series is a common research topic and is studied using probabilistic and non-probabilistic methods. For probabilistic methods, the autoregressive integrated moving average and exponential smoothing methods are commonly used, whereas for non-probabilistic methods, artificial neural networks and fuzzy inference systems (FIS) are commonly used. There are numerous FIS methods. While most of these methods are rule-based, there are a few methods that do not require rules, such as the type-1 fuzzy function (T1FF) approach. While it is possible to encounter a method such as an autoregressive (AR) model integrated with a T1FF, no method that includes T1FF and the moving average (MA) model in one algorithm has yet been proposed. The aim of this study is to improve forecasting by taking the disturbance terms into account. The input dataset is organized using the following variables. First, the lagged values of the time series are used for the AR model. Second, a fuzzy c-means clustering algorithm is used to cluster the inputs. Third, for the MA, the residuals of fuzzy functions are used. Hence, AR, MA, and the degree of memberships of the objects are included in the input dataset. Because the objective function is not derivative, particle swarm optimization is preferable for solving it. The results on several datasets show that the proposed method outperforms most of the methods in literature