Performance of Drug and Poison Information Center within a Referral University Hospital in Southwest of Iran

Background: Drug and Poison Information Centers (DPICs) provide quick, easy, valid and reliable access to medication and toxin information for professionals, health workers and the public. The purpose of this study is to report the services provided by a university hospital DPIC within 1 year. Methods: This descriptive study reports all scientific questions asked from DPIC of Namazi hospital in Shiraz from the September 2016 to the August 2017. The information include the number of questions, the ward that ask the question, the questioner's profession, the method of asking questions, the method of responding, the type of question, and the resources used to answer them. After extraction of duplicates, data were analyzed by using the Excel software. Results: The total number of contacts registered during the study period was 485. The most number of questions were received in July and the lowest in November. Major questions were asked from the health-care team working in Namazi hospital and mostly from the nursing group (44.7%). Most of the questions (79.6%) were asked and responses were provided (67.1%) by the telephone device. Of all incoming inquiries, drug indication (13.3%), adverse drug reactions (ADR) (13.3%), storage (11.8%), and the method of preparation as well as administration (11.7%) were among the most common types of questions. The most frequent ward in asking questions was the pediatric intensive care unit (13.1%). The most widely used drug information resource to answer questions was the UptoDate® (47.5%). Conclusion: DPIC services in the hospital settings can decrease or prevent ADRs as well as medication errors, improve the pattern of medication use, and result in cost saving

Evaluating Adherence of Health-Care Team to Standard Guideline of Colistin Use at Intensive Care Units of a Referral Hospital in Shiraz, Southwest of Iran

Purpose: To evaluate colistin use according to global standard drug consumption in intensive care units of a referral hospital in Shiraz, Iran Methods: A prospective, interventional study was performed during an 11 month period on 100 patients admitted to ICUs of a teaching hospital being treated with colistin for at least 3 subsequent doses. Required demographic, clinical, and paraclinical data were gathered by a pharmacist. Fifteen indexes were considered to evaluate colistin use. A clinical pharmacist reviewed indication and dose of colistin at the time of prescribing this agent. Results: In our study population, pneumonia (69%) was the main indication of colistin. In 87% of patients, colistin administration was based on microbiological laboratory evidence. Continuation of therapy was inappropriate in 5% of cases. By the intervention of the clinical pharmacist, colistin was discontinued in all patients in whom empirical therapy was continued incorrectly. None of the patients received loading dose of colistin. The maintenance dose, dose interval, and duration of treatment of colistin were appropriate in 76%, 71%, and 100% of patients, respectively. For none of the patients, the pharmacokinetic dosing method was used. In all patients, serum creatinine and WBC count were evaluated on daily basis. The sum indexes of colistin use were relevant to standard guidelines in 67.33% of the cases.Conclusion: The results of this study highlight the necessity of the pharmaceutical care team participation in all stages of treatment with antibiotics. After pharmacist interventions, some criteria of colistin utilization were corrected and brought closer to standard values

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age

Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine

This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM) and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m × 1.5 m × 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP) and Artificial Neural Networks (ANNs) models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge

Category numbers of samples used for evaluation.

<p>Ethnicity is denoted by</p><p><i>A</i>: Asian</p><p><i>C</i>: Caucasian</p><p><i>AA</i>: African American</p><p><i>H</i>: Hispanic; and Gender is</p><p>F: Female</p><p><i>M</i>: Male</p><p>Category numbers of samples used for evaluation.</p

CBIR layout in the bone age assessment system.

<p>CBIR layout in the bone age assessment system.</p

Bone age result displayed with the gender and ethnicity features.

<p>Bone age result displayed with the gender and ethnicity features.</p

User-defined parameters for the ELM, ANN and GP models.

<p>User-defined parameters for the ELM, ANN and GP models.</p

Comparison of error rate for the soft computing models.

<p>Comparison of error rate for the soft computing models.</p