scholarly journals Design and evaluation of analytical tools for emergency department management based on machine learning techniques

2016 ◽  
Author(s):  
Alexander Zlotnik Enaliev
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Emergency Department Management ◽  
Analytical Tools ◽  
Department Management

Machine Learning for Emergency Department Management

2019 ◽  
Vol 11 (3) ◽  
pp. 19-36 ◽  
Author(s):  
Sofia Benbelkacem ◽  
Farid Kadri ◽  
Baghdad Atmani ◽  
Sondès Chaabane
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Length Of Stay ◽  
Prediction Models ◽  
Pediatric Emergency ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Emergency Department Management ◽  
Increasing Demand ◽  
Set Up

Nowadays, emergency department services are confronted to an increasing demand. This situation causes emergency department overcrowding which often increases the length of stay of patients and leads to strain situations. To overcome this issue, emergency department managers must predict the length of stay. In this work, the researchers propose to use machine learning techniques to set up a methodology that supports the management of emergency departments (EDs). The target of this work is to predict the length of stay of patients in the ED in order to prevent strain situations. The experiments were carried out on a real database collected from the pediatric emergency department (PED) in Lille regional hospital center, France. Different machine learning techniques have been used to build the best prediction models. The results seem better with Naive Bayes, C4.5 and SVM methods. In addition, the models based on a subset of attributes proved to be more efficient than models based on the set of attributes.

Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software

2015 ◽  
Vol 33 (8) ◽  
pp. 368-377 ◽  
Author(s):  
ALEXANDER ZLOTNIK ◽  
ASCENSIÓN GALLARDO-ANTOLÍN ◽  
MIGUEL CUCHÍ ALFARO ◽  
MARÍA CARMEN PÉREZ PÉREZ ◽  
JUAN MANUEL MONTERO MARTÍNEZ
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Open Source ◽  
Emergency Department Visit ◽  
Nursing Staff ◽  
Open Source Software ◽  
Machine Learning Techniques ◽  
Staff Allocation ◽  
Learning Techniques

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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