This $174,371 National Science Foundation project grant supports research at Chapman University towards developing a personalized framework for real-time patient length of stay modeling and prediction. The framework aims to advance data fusion and time-to-event modeling techniques to enable proactive, data-driven hospital discharge scheduling. By integrating advanced tensor fusion and statistical learning approaches, the framework seeks to facilitate personalized length of stay predictions in a dynamic manner while accounting for inherent uncertainties. This work falls under the NSF's Computer and Information Science and Engineering program (CFDA 47.070), which supports investigator-initiated research and education across computing, communications, and information science and engineering fields. The two-year project commenced June 1, 2023 and aims to enhance healthcare resource planning, pandemic preparedness, and patient outcomes.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $174.4k | 3/15/23 |