A Computational Intelligence Framework for Length of Stay Prediction in Emergency Healthcare Services Department

Umoren, Imeh; Udonyah, Kingsley Christopher; Isong, Etebong

doi:10.1109/iccse.2019.8845332

Cited by 4 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such is the impacts of climate Change that there continue to be projected increases in annual temperatures, and rainfall with these extreme weather events predicted to have both direct and indirect repercussions on the environment [32] as well as organisms in that environment. For instance, the incidence of malaria is high in South-South Nigeria because of the heavy rainfall [46] that results in floods that in turn sponsors the breeding of mosquitoes that cause malaria and with the overcrowding in Emergency Departments in many Nigerian healthcare facilities [45], mitigating the risks of infectious diseases presents the most optimal pathway.…”

Section: Risk Factors Of Climate Changementioning

confidence: 99%

Classification of risk factors of climate change on infectious diseases using Bio-Inspired Algorithms

Kingsley Udonyah,

Imeh Umoren

2023

Global J. Eng. Technol. Adv.

View full text Add to dashboard Cite

A new index of investigative approaches is being developed to help identify emerging infections and to detect the increased risks factors of infectious disease (IDs) occurrences that are expected to occur with climate change. The impacts of Climate Change with direct effects on human health through climate extremes and indirectly through infectious diseases are enormous. Hence, it requires study to determine correlations between Climate Change and Infectious Diseases using clinically validated data to improve behavioural health plans and early warning of public health risks. This work employs Meteorological and Infectious Disease data from the Figshare Data Repository, NiMET, NIH, WHO and literature, processes these data using SciKit Learn Preprocessing Package in Python and characterizes the risk factors of Climate Change Features: Humidity, Minimum Temperature, Maximum Temperature and Rainfall on Infectious Disease: Malaria, Pneumonia and Diarrhea using Nature-Inspired Algorithm using Artificial Neural Network (ANN) and Random Forest (RF) Algorithm with the R Statistical Programming Language. The work adopts Design Science Research (DSR) Methodology to analyze and classifies the risk factors of climate change on infectious diseases as well as evaluates the performances of selected Nature-Inspired Algorithms in classifying selected climatic factors and associated impacts on infectious diseases. Results obtained demonstrated that the RF performed better than ANN Algorithms with 96.9% and 95% accuracies respectively. Both models indicated that Rainfall and Temperature variations were common risks factors that indicated highest weights impacting the emergence and incidence of Infectious Diseases in Nigeria.

show abstract

Section: Risk Factors Of Climate Changementioning

confidence: 99%

Classification of risk factors of climate change on infectious diseases using Bio-Inspired Algorithms

Kingsley Udonyah,

Imeh Umoren

2023

Global J. Eng. Technol. Adv.

View full text Add to dashboard Cite

show abstract

“…They were able to predict specific limits for the optimal values of the criteria to solve the problem of peaks of activity and overcrowding as well as improve system performance and patient satisfaction. Umoren, Udonyah, and Isong (2019) proposed a computational intelligence framework to predict patient's LOS in hospital ED. However, they analyzed several factors including Severity of Illness or Emergency Cases (SIC) to assess its performance but the ML framework was implemented using Intuitionistic Type-2 Fuzzy Logic System (IT2-FLS).…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Framework for Length of Stay Minimization in Healthcare Emergency Department

Asuquo,

Umoren,

Osang

et al. 2023

SET

View full text Add to dashboard Cite

The emergency departments (EDs) in most hospitals, especially in middle-and-low-income countries, need techniques for minimizing the waiting time of patients. The application and utilization of appropriate methods can enhance the number of patients treated, improve patients’ satisfaction, reduce healthcare costs, and lower morbidity and mortality rates which are often associated with poor healthcare facilities, overcrowding, and low availability of healthcare professionals. Modeling the length of stay (LOS) of patients in healthcare systems is a challenge that must be addressed for sound decision-making regarding capacity planning and resource allocation. This paper presents a machine learning (ML) framework for predicting a patient’s LOS within the ED. A study of the services in the ED of a tertiary healthcare facility in Uyo, Nigeria was conducted to gain insights into its operational procedures and evaluate the impact of certain parameters on LOS. Then, a computer simulation of the system was performed in R programming language using data obtained from records in the hospital. Finally, the performance of four ML classifiers involved in patients’ LOS prediction: Classification and Regression Tree (CART), Random Forest (RF), K-Nearest Neighbour (K-NN), and Support Vector Machine (SVM), were evaluated and results indicate that SVM outperforms others with the highest coefficient of determination (R2) score of 0.986984 and least mean square error (MSE) value of 0.358594. The result demonstrates the capability of ML techniques to effectively assess the performance of healthcare systems and accurately predict patients’ LOS to mitigate the low physician-patient ratio and improve throughput.

show abstract

“…There are numerous cases where both healthcare providers or managers and healthcare users going through critical deprivation -on one hand and healthcare managers are unable to do their jobs while on the other hand, the poor masses requiring healthcare services and other healthcare users suffer health challenges and even death. This critical challenge that has been recorded in this region can be traceable to unavailability of very required medical consumables, including medical professionals like nurses and doctors, often resulting from many factors, one of which is lack of logistic framework for Supply Chain in the south-south, Nigeria, resulting in many deaths recorded in the emergency units of the hospitals [19]. Again, a key characteristic of the south-south rural area of this region in terms of its geographical nature, is that it is swampy and thus provides enough breeding ponds for mosquitoes to thrive -as a result the outbreak of malaria is high and the deaths recorded from malaria in this region is higher than those obtained elsewhere within the country [6].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2023

EJCSIT

View full text Add to dashboard Cite

Most disease that affects the heart or blood vessels is referred to as cardiovascular disease (CVD). The main aim of this work is to build a system capable of modeling and predicting early syndromic cardiovascular diseases (CVD) based on electrocardiogram (ECG). The study considers the implementation of computationally intelligent system for detecting and classifying early syndromic assessment of CVD. The clinical and ECG recordings of patients diagnosed with pulmonary hypertension at the University of Uyo Teaching Hospital (UUTH) were obtained. The datasets were segmented into Demographic and ECG datasets. A quantitative research approach was used for the study with examination of several segments based on recommended framework. Three (3) classifier models were adopted to detect cardiac related problems using specified datasets. The classifiers such as; Random Forest Ensemble (RFE), Support Vector (SVM) Classifier and Artificial Neural Network (ANN) was employed for Machine Learning process. The models were implemented using a robust programming languages (Python and Jupyter notebook). The datasets were further segmented into two categories: training sets and testing in the ratio of 80:20 respectively. The test data reflects; precision, recall and sensitivity: Results show Radom Forest Model: 0.50 (50%) accuracy, 0.48 (48%) precision score and 0.65 (65%) recall sensitivity score (RSS), SVM classifier indicated 0.70 (70%) accuracy score, 0.47 (47) % precision score as well as 0.52 (52 %) sensitivity score. The ANN model illustrates 0.50 (50%) score for accuracy, precision and recall. Research Findings demonstrated that, RFE, SVM, ANN illustrate 100% accuracy in precision and recall sensitivity. The interaction effects of the various clinical factors influencing the CVD of patient was appraised and performance evaluation were further done using standard data science measures; Confusion Matrix (CM), MAP, MAPE, RMSE was deployed. The final results obtained shows that RFE, SVM, ANN models support satisfactorily the assessment and classification of early syndromic conditions of CVD.

show abstract

A Computational Intelligence Framework for Length of Stay Prediction in Emergency Healthcare Services Department

Cited by 4 publications

References 18 publications

Classification of risk factors of climate change on infectious diseases using Bio-Inspired Algorithms

Classification of risk factors of climate change on infectious diseases using Bio-Inspired Algorithms

A Machine Learning Framework for Length of Stay Minimization in Healthcare Emergency Department

Untitled

Contact Info

Product

Resources

About