Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to predict the rate of treatment expenditure on an individual or family in a country. A number of factors have been identified that may affect treatment expenses. Factors such as age, grade level such as primary, preparatory, secondary or college, sex, size of disability, social status, and annual medical expenses in fixed dollars excluding dental and outpatient clinics among others, as input variables for the ANN model. A model based on the multi-layer Perceptron topology was developed and trained using data on 5574 cases. The evaluation of the test data shows that the ANN model is capable of predicting correctly Medical Expenses
Artificial neural networks (ANNs) have been developed, implemented and tested on the basis of a four...
Artificial Neural Networks (ANN) approach is an alternate way to classical methods. As a computation...
Our aim was to predict future high-cost patients with machine learning using healthcare claims data....
Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to pr...
Thesis (Ph.D.)--University of Washington, 2012During the course of care, patients frequently develop...
Accurate prediction of healthcare costs is important for optimally managing health costs. However, m...
The objective of this research was to compare the accuracy of two types of neural networks in identi...
This thesis presents an Artificial Neural Network Research Framework (ANN RFW) for predicting medica...
In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth...
How to estimate and predict the expenses incurred by diabetes treatment using Artificial Neural Netw...
This study is aiming at estimating the patient volumes of hospitals by using artificial neural netw...
In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth...
Background: Considering the prevalence of cardiovascular diseases in Iran and the high rate of death...
The analysis of cancer survival is used to determine the efficiency of treatment programmes and prot...
The latest and most interesting development in the field of Deep Learning (DL) is an artificial neur...
Artificial neural networks (ANNs) have been developed, implemented and tested on the basis of a four...
Artificial Neural Networks (ANN) approach is an alternate way to classical methods. As a computation...
Our aim was to predict future high-cost patients with machine learning using healthcare claims data....
Abstract: In this research, the Artificial Neural Network (ANN) model was developed and tested to pr...
Thesis (Ph.D.)--University of Washington, 2012During the course of care, patients frequently develop...
Accurate prediction of healthcare costs is important for optimally managing health costs. However, m...
The objective of this research was to compare the accuracy of two types of neural networks in identi...
This thesis presents an Artificial Neural Network Research Framework (ANN RFW) for predicting medica...
In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth...
How to estimate and predict the expenses incurred by diabetes treatment using Artificial Neural Netw...
This study is aiming at estimating the patient volumes of hospitals by using artificial neural netw...
In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth...
Background: Considering the prevalence of cardiovascular diseases in Iran and the high rate of death...
The analysis of cancer survival is used to determine the efficiency of treatment programmes and prot...
The latest and most interesting development in the field of Deep Learning (DL) is an artificial neur...
Artificial neural networks (ANNs) have been developed, implemented and tested on the basis of a four...
Artificial Neural Networks (ANN) approach is an alternate way to classical methods. As a computation...
Our aim was to predict future high-cost patients with machine learning using healthcare claims data....