پیش بینی الگوی ورود بیمار به بخش اورژانس بیمارستان با استفاده از تکنیک داده کاوی و مدل شبکه عصبی

زمینه و هدف: بخش اورژانس ، اولین مکان ارائه خدمات تشخیصی و درمانی به بیماران اورژانسی می باشد. با توجه به اهمیت سرعت و دقت در ارائه خدمات، تخصیص صحیح منابع در این بخش اهمیت ویژه ای دارد. برنامه ریزی منابع بخش اورژانس، بدون توجه به ازدحام و تراکم بیمار در زمان های مختلف صورت می گیرد، بنابراین ممکن است بخش با کمبود منابع روبرو شده و این امر منجر به معطلی بیماران، بی نظمی در انجام کارها و در نتیجه افت کیفیت خدمات گردد. در این مطالعه به منظور رفع مشکلات فوق، الگوی پیش بینی تعداد بیمار مراجعه کننده به بخش اورژانس ارائه شده است. مواد و روش ها: تعداد بیمار بر مبنای داده های ورود بیماران به اورژانس و با استفاده از تکنیک داده کاوی و شبکه عصبی پرسپترون چند لایه(Multi-layer Perceptron) MLP پیش بینی شده است. یافته ها: تعداد بیمار ورودی در روزهای مختلف هفته و ساعات مختلف شبانه روز برای هر یک از اولویت های سه گانه 1 ، 2 و3 تعیین شده، بیشترین تعداد بیمار در روز شنبه و کمترین در روز جمعه بوده است. روزهای تعطیل و غیر تعطیل از لحاظ تعداد بیمار با هم متفاوت و تعداد بیمار در روزهای تعطیل رسمی مانند اعیاد برابر تعداد بیمار در روزهای جمعه بوده است. بیشترین تعداد بیمار در ساعات 9 الی 11 صبح و 20 الی 23 شب و کمترین تعداد در ساعات بین 2 الی 7 صبح میباشد. نتیجه گیری: پیش بینی تعداد بیمار بخش اورژانس می تواند در برآورد منابع مورد نیاز و توزیع مناسب آنها مورد استفاده قرار گرفته و کیفیت خدمات را بهبود بخشد

Dermatological Detection and Classification using Machine Learning Techniques

Dermatology is the medical field that focuses on the study and treatment of skin conditions. It is a specialized branch of medicine that encompasses both diagnostic and surgical procedures related to the skin. It is a widespread disease among them. The researchers have shows a lot of attention to the early detection of lesions. Because of their proliferation ability to other parts of the body, death rates are quite high. A system that can distinguish between benign and malignant lesions is essential because melanoma can be cured with an early and accurate diagnosis. Dermoscopic skin lesion images are first segmented using data mining techniques, to identify the area of interest of the lesion part. When compared to individual classifier algorithms, dermatology datasets benefit from the various data mining techniques and feature selection methods. The SVM provides more accurate and effective skin disease prediction in terms of accuracy, precision, and Specificity

Classification of hypoglycemic episodes for Type 1 diabetes mellitus based on neural networks

Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based classification unit can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis including statistical regression, fuzzy regression and genetic programming

A hypoglycemic episode diagnosis system based on neural networks for Type 1 diabetes mellitus

Hypoglycemia (or low blood glucose) is dangerous for Type 1 diabetes mellitus (T1DM) patients, as this can cause unconsciousness or even death. However, it is impossible to monitor the hypoglycemia by measuring patients’ blood glucose levels all the time, especially at night. In this paper, a hypoglycemic episode diagnosis system is proposed to determine T1DM patients’ blood glucose levels based on these patients’ physiological parameters which can be measured online. It can be used not only to diagnose hypoglycemic episodes in T1DM patients, but also to generate a set of rules, which describe the domains of physiological parameters that lead to hypoglycemic episodes. The hypoglycemic episode diagnosis system addresses the limitations of the traditional neural network approaches which cannot generate implicit information. The performance of the proposed hypoglycemic episode diagnosis system is evaluated by using real T1DM patients’ data sets collected from the Department of Health, Government of Western Australia, Australia. Results show that satisfactory diagnosis accuracy can be obtained. Also, explicit knowledge can be produced such that the deficiency of traditional neural networks can be overcome. A clear understanding of how they perform diagnosis can be indicated

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Diagnosis of hypoglycemic episodes using a neural network based rule discovery system

Hypoglycemia or low blood glucose is dangerous and can result in unconsciousness, seizures and even death for Type 1 diabetes mellitus (T1DM) patients. Based on the T1DM patients’ physiological parameters, corrected QT interval of the electrocardiogram (ECG) signal, change of heart rate, and the change of corrected QT interval, we have developed a neural network based rule discovery system with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed neural network based rule discovery system is built and is validated by using the real T1DM patients’ data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based rule discovery system can achieve more accurate results on both trained and unseen T1DM patients’ data sets compared with those developed based on the commonly used classification methods for medical diagnosis, statistical regression, fuzzy regression and genetic programming. Apart from the achievement of these better results, the proposed neural network based rule discovery system can provide explicit information in the form of production rules which compensate for the deficiency of traditional neural network method which do not provide a clear understanding of how they work in prediction as they are in an implicit black-box structure. This explicit information provided by the product rules can convince medical doctors to use the neural networks to perform diagnosis of hypoglycemia on T1DM patients

Comparación entre regresión logística y redes neuronales para predecir cáncer de piel en perros

Determinar si un perro tiene la predisposición de desarrollar cáncer a la piel es uno de los desafíos tanto de los veterinarios como de los dueños de las mascotas. Los modelos de regresión logística y redes neuronales han sido ampliamente utilizados para realizar predicciones en el ámbito de la medicina humana, el presente estudio aborda la comparación de éstas dos técnicas para la predicción de cáncer de piel en perros. Las características que se han analizado son la edad, el sexo, raza, exposición al sol, albinismo y la aparición de dermatitis. Dichas características fueron validadas por el método de coeficiente de correlación y el análisis de componente principal. Los resultados obtenidos demostraron que la red neuronal backpropagation con validación cruzada supera al modelo de regresión logística. El valor de predicción generado por la red neuronal fue de 89.6% mientras que la regresión logística obtuvo un 84%.To ascertain if a dog has the predisposition to develop skin cancer is a challenge for both veterinarians and pet owners. Logistic regression models and neural networks have been used widely in the field of human medicine to make predictions; the present study approaches the comparison between these two technics to predict skin cancer in dogs. The variables we analyzed were age, sex, breed, sun exposition, albinism and, dermatitis. These variables were validated by the correlation coefficient and the principal component analysis. The obtained results showed that the backpropagation neural network technique with a cross validation is better than the logistic regression. The neural network’s accuracy value was 89.6% while only 84% for the logistic regression