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Showing 3 results for Sheikhtaheri
Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran
Mahmoud Akbarian , Khadijeh Paydar, Sharareh R Ostam Niakan Kalhori , Abbas Sheikhtaheri ,
Volume 73, Issue 4 (July 2015)
Abstract
Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including spontaneous abortion and live birth in SLE. Methods: In a retrospective study, forty-five variables were identified as effective factors for prediction of pregnancy outcomes in systemic lupus erythematosus. Data of 104 pregnancies in women with systemic lupus erythematosus in Shariati Hospital and 45 pregnancies in a private specialized center in Tehran from 1982 to 2014 in August and September, 2014 were collected and analyzed. For feature selection, information of the 149 pregnancies was analyzed with a binary logistic regression model in SPSS software, version 20 (SPSS, Inc., Chicago, IL, USA). These selected variables were used for inputs of neural networks in MATLAB software, version R2013b (MathWorks Inc., Natick, MA, USA). A Multi-Layer Perceptron (MLP) network with scaled conjugate gradient (trainscg) back propagation learning algorithm has been designed and evaluated for this purpose. We used confusion matrix for evaluation. The accuracy, sensitivity and specificity were calculated from the confusion matrix. Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP) neural network with scaled conjugate gradient (trainscg) back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth) among pregnant women with lupus by using identified effective variables.
Design and implementation of a children vaccination reminder system based on short message service
Marjan Ghazisaeedi, Abbas Sheikhtaheri, Bahar Allahverdi, Bahareh Azizi,
Volume 74, Issue 6 (September 2016)
Abstract

Background: Most problems related to quality of care and patient safety are related to human negligence. One of the causes of these problems is forgetting to do something. This problem can be avoided with information technology in many cases. Some forgotten are very important. Among these is failure to comply with vaccination schedule by parents that can result in inappropriate outcomes. In this study, we developed and evaluated a SMS reminder system for regular and timely vaccination of children.

Methods: In this developmental-applied research, firstly, a child vaccination reminder system was designed and implemented to help parents reduce the forgetfulness. This system based on the child's vaccination history and the date of birth, offer time and type of future vaccines. Then the parents of 27 children, that their vaccination was between 22 June and 21 August 2015, referred to Children's Medical Center, were sent text messages by using this system. We evaluated the accuracy of the system logic by using some scenarios. In addition, we evaluated parents' satisfaction with the system using a questionnaire.

Results: In all cases but one, the system proposed the type and date of future children vaccines correctly. All the parents who have received text messages had good perception and satisfaction on the majority of questions (total mean score of 4.15 out of 5). Most parents (4.92 out of 5) stated that using the system to remind their visit for child immunization was helpful and willing to offer the system to their friends and other families.

Conclusion: Using the short message system is beneficial for parents to remind their children’s vaccination time and increases their satisfaction. So, it can be considered as an important and essential tool in providing healthcare services. SMS is an easy, cheap and effective way to improve the quality of care services.


A clinical decision support system for contact lens evaluation
Fatemeh Falahati Marvast , Hossein Arabalibeik, Fatemeh Alipour , Abbas Sheikhtaheri, Leila Nouri,
Volume 76, Issue 12 (March 2019)
Abstract
Background: Contact lenses are transparent, thin plastic disks that cover the surface of the cornea. Appropriate lens prescription should be performed properly by an expert to provide better visual acuity and reduce side effects. The lens administration is a multi-stage, complex and time-consuming process involving many considerations. The purpose of this study was to develop a decision support system in the field of contact lens prescription.
Methods: In this fundamental study, data were collected from 127 keratoconus patients referred to the contact lens clinic at Farabi Eye Hospital, Tehran, Iran during the period of March 2013 to July 2014. Five parameters in the contact lens prescribing process were investigated. Parameters were collected as follows. “Lens vertical position”, “vertical movement of the lens during blinking” and “width of the rim” in the fluorescein pattern were obtained by recording videos of the patients while wearing the lens. “Fluorescein dye concentration” under the lens was evaluated by the physician and “patient comfort” was obtained by asking the patient to fill a simple scoring system. Approved and disapproved lenses were judged and recorded based on the decision of an expert contact lens practitioner. The decision support system was designed using artificial neural networks with the mentioned variables as inputs. Approved and disapproved lenses are considered as system outputs. Artificial neural network was developed using MATLAB® software, version 8.3 (Mathworks Inc., Natick, MA, USA). Eighty percent of the data was used to train the support vector machine and the rest of the data (20%) to test the system's performance.
Results: Accuracy, sensitivity and specificity, calculated using the confusion matrix, were 91.3%, 89.8% and 92.6% respectively. The results indicate that the designed decision support system could assist contact lens prescription with high precision.
Conclusion: According to the results, we conclude that hard contact lens fitness could be evaluated properly using an artificial neural network as a decision support system. The proposed system detected approved and disapproved contact lenses with high accuracy.

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