Automatic simplification of models in a hierarchical, modular discrete event simulation environment.

This research is concerned with the simplification of discrete event simulation models. Specifically, the necessary concepts and tools are investigated for the automatic generation of simplified versions of a discrete event model. The simplification process developed utilizes observations of model behavior. It is based on Zeigler's DEVS formalism developed in his book "Multifacetted Modelling and Discrete Event Simulation". Therefore, the simplified models that are generated adhere to all the canons of this formalism. The implementation is designed as an extension to DEVS-Scheme, a hierarchical modular discrete event modelling and simulation environment. It consists of two layers: the lower layer observes the model behavior and the higher layer generates the code from the observation data obtained at the lower layer. The resulting system allows one to generate simplified models at any level of the model structure hierarchy. A local area network model base is developed and used to demonstrate the validity and complexity reduction of the simplified models relative to the models from which they are extracted

Algorithms for scheduling of chemotherapy plans

WOS: 000329413800014PubMed ID: 24290927Chemotherapy is used to control and cure cancer by using drugs to destroy cancer cells. Treatment schedules for chemotherapy may vary depending on the type of cancer, the goals of treatment, the type of chemotherapy and the patient's state of health. Chemotherapy is usually given in cycles of a treatment-period followed by a rest-period. An oncologist decides the choice of a particular regimen; however, modifications to drug dose and schedule are often necessary because of variabilities in the health of an individual patient. Therefore an orderly execution of chemotherapy regimens requires management, scheduling and allocation of the resources available. Chemotherapy scheduling is an optimization problem. In this paper, a two-phase approach has been adopted to deal with the problem. An adaptive negative-feedback scheduling algorithm is proposed for the first phase to control the load on the system. Two heuristics based on the 'Multiple Knapsack Problem' have been evaluated for the second phase to assign patients to specific infusion seats. The overall design has been put to test at a local chemotherapy center and has yielded good results for patient waiting times, orderly execution of chemotherapy regimen and utilization of infusion chairs. (C) 2013 Elsevier Ltd. All rights reserved

THE ROLE OF ROUTINE LABORATORY TESTING IN THE MANAGEMENT OF CHRONIC URTICARIA

Objective: The diagnosis of chronic spontaneous urticaria (CSU) is mainly based on a thorough patient history and physical examination. Although limited laboratory tests are recommended, detailed screening laboratory tests are frequently required by specialists to exclude underlying diseases or to identify causative factors. The aim of this study was to evaluate laboratory findings of patients with CSU and its impact on the diagnosis and management of the disease

Predicting Patient Waiting Time in Phlebotomy Units Using a Deep Learning Method

Phlebotomy units are one of the places with the highest patient density in a hospital. Because the patients from different outpatient clinics of the hospital come to the phlebotomy units to have phlebotomies performed on them. Accurately predicting waiting times can increase patient satisfaction and enable staff members to more accurately evaluate and respond to patient flow. In this study, we examined the applicability of a machine learning model to estimate waiting times in a phlebotomy unit. We used the waiting times in the Phlebotomy Unit of Izmir University of Health Sciences Tepecik Training And Research Hospital as our data set. In our study, our model predicted patient waiting times using an artificial Neural Network algorithm. As a result, we succeeded in predicting how long the patient would wait in the waiting room with 88% accuracy

Artificial Neural Network Approach in Laboratory Test Reporting Learning Algorithms

Objectives: In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would efficiently and rapidly evaluate the results of biochemical tests with critical values by evaluating multiple factors concurrently

Multicentric ipilimumab experience in Turkish patients with metastatic melanoma: MIPI-TURK at 30 months of follow-up.

49th Annual Meeting of the American-Society-of-Clinical-Oncology (ASCO) -- MAY 31-JUN 04, 2013 -- Chicago, ILWOS: 000335419604732…Amer Soc Clin Onco

Multicentric ipilimumab experience in Turkish patients with metastatic melanoma: MIPI-TURK.

48th Annual Meeting of the American-Society-of-Clinical-Oncology (ASCO) -- JUN 01-06, 2012 -- Chicago, ILWOS: 000318009801881…Amer Soc Clin Onco