Name
Title | ||
|---|---|---|
A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay among Diabetic Patients |
Abstract | ||
|---|---|---|
Diabetes is a life-altering medical condition that affects millions of people and results in many hospitalizations per year. Consequently, predicting the length of stay of in-hospital diabetic patients has become increasingly important for staffing and resource planning. Although statistical methods have been used to predict length of stay in hospitalized patients, many powerful machine learning techniques have not yet been explored. In this paper, we compare and discuss the performance of various supervised machine learning algorithms (i.e., Multiple linear regression, support vector machines, multi-task learning, and random forests) for predicting long versus short-term length of stay of hospitalized diabetic patients. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICMLA.2014.76 | Machine Learning and Applications |
Keywords | Field | DocType |
diseases,hospitals,learning (artificial intelligence),medical computing,resource allocation,statistical analysis,diabetic patients,life-altering medical condition,resource planning,short-term in-hospital stay length prediction,staffing,statistical methods,supervised machine learning techniques,Diabetes,In-Hospital Length of Stay Prediction,Multi-Task Learning,Random Forests,Supervised Machine Learning,Support Vector Machines,Support Vector Machines Plus | Resource planning,Multi-task learning,Staffing,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Random forest,Machine learning,Linear regression | Conference |
Citations | PageRank | References |
5 | 0.73 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Morton, A. | 1 | 5 | 0.73 |
| Marzban, E. | 2 | 5 | 0.73 |
| Giannoulis, G. | 3 | 5 | 0.73 |
| Amit Patel | 4 | 8 | 3.88 |