Earticle

초록

영어

The duration of an inpatient stay affects hospital administration and improves hospital effectiveness in terms of controlling expenses and raising patient standards. It also assists in identifying the correlations among illnesses requiring hospitalization. For our study, we took 24,150 records from of the Open Data database pertaining to inpatient admissions in 2023. We used a number of methods, including Neural Networks, Deep Learning, Linear Regression, and Support Vector Machines, to predict the Length of Stay (LOS). We converted the data to numerical form for predictive purposes, dividing the dataset into 70% for training and 30% for testing. We assessed the model's performance using Root Mean Squared Error (RMSE) and split the forecast into four LOS categories: 0-2, 3-4, 5-7, and 8 days or more. The study also employed the Apriori algorithm to identify illness association rules that could impact LOS estimates. The results showed that identifying illness correlations is one element that might aid in enhancing the capacity to predict LOS.

목차

Abstract
I. INTRODUCTION
II. DATA PROCESSING
A. Data Set and Summary Statistics
B. Data Integrity
C. Data Selection
D. Data Transformation
III. PREDICTIVE METHODS
A. Neural Network
B. Deep Learning
C. Linear Regression
D. Support Vector Machines
E. Apriori Algorithm
IV. EXPERIMENTS
V. RESULTS AND DISCUSSION
A. Prediction
B. Association Rule
VI. CONCLUSION
REFERENCES

저자

• Sahatas Chatnopakun [ Faculty of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok Bangkok, Thailand ]
• Kant Panyavanich [ Faculty of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok Bangkok, Thailand ]
• Maleerat Maliyaem [ Faculty of Information Technology and Digital Innovation King Mongkut's University of Technology North Bangkok Bangkok, Thailand ]

참고문헌