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Showing 2 results for Clinical Decision Support System
A decision support system for boosting warfarin maintenance dose using fuzzy logic
Zahra Qaempanah , Hossein Arab-Alibeik , Marjan I Ghazi Saeed, Mohammad Ali Sadr-Ameli,
Volume 73, Issue 4 (7-2015)
Abstract
Background: Warfarin is the most common oral anticoagulant. This drug is used for the prevention and treatment of thromboembolic patients. It is difficult for physician to predict the results of warfarin prescriptions because there is narrow boundary between therapeutic range and complications of warfarin. Therefore drug dose adjustment is normally performed by an expert physician. Decision support systems that use extracted knowledge from experts in the field of drug dose adjustment would be useful in reducing medical errors, especially in the clinics with limited access to experts. The aim of this study was to propose a method for boosting the maintenance dose of warfarin for a maximum period of three days to eliminate disruptions in International Normalized Ratio (INR). Methods: In a retrospective study, from December 2013 to February 2014 in Shahid Rajaee Heart Center, Tehran, Iran, 84 patients with International Normalized Ratio below (INR) the therapeutic range was selected who was undergone a boosting dose during three days. Patients with unstable maintenance dose were excluded from the study. In this study, data from 75 patients receiving warfarin therapy were used for developing and evaluation of the proposed model. The INR target range for 37 patients out of remaining 75 cases was between 2.5 and 3.5, while for 38 patients the intended INR range was between 2 and 3. A separate fuzzy model was designed for each of the above-mentioned therapeutic ranges. Results: The recommended dose for 37 patients having INR therapeutic range of 2.5 to 3.5 has mean absolute error and root mean squared error of 1.89 and 2.78 respectively for three days. These error rates are 1.97 and 2.88 respectively for 38 patients who are in therapeutic range 2 to 3. Conclusion: The results are promising and encourage one to consider this system for more study with the aim of possible use as a decision support system in the future.
A clinical decision support system for contact lens evaluation
Fatemeh Falahati Marvast , Hossein Arabalibeik, Fatemeh Alipour , Abbas Sheikhtaheri, Leila Nouri,
Volume 76, Issue 12 (3-2019)
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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