Hospital Admission Prediction Using Pre-hospital Variables

With the rapid outstripping of healthcare resources by the demands on hospital care, it is important to find more effective and efficient ways for managing care. This research is aimed at developing new admission prediction models using various pre-hospital variables to help hospital estimate the patients to be admitted. We developed a framework of hospital admission prediction and proposed two novel approaches to capture semantics of chief complaints to enhance prediction. Our experiments on a hospital dataset demonstrated that our proposed models outperformed several benchmark methods.