Master Assignment
[M] (Multimodal) Admission prediction for patients in the cardiac emergency department
Type: Master
Period: TBD
Student: (Unassigned)
If you are interested, please contact :
Data type(s): Tabular, time-series (high sample rate)
Description: The cardiac emergency department plays a vital role in healthcare for cardiac patients who are in need of urgent medical attention. After their visit, patients may either be discharged or admitted to the hospital. For those who are admitted, medication verification is a mandatory process. Pharmacy assistants, responsible for this task, often face difficulties in prioritizing the patients most likely to be admitted. By leveraging machine learning, alongside a wide range of data—including demographic information, laboratory results, vital signs, and ECG readings—there is potential to create an accurate predictive model for hospital admissions.
Goal: Develop a model that predicts the likelihood of hospital admission for patients visiting the cardiac emergency department, helping pharmacy assistants prioritize patients more effectively.
Supervisors: dr. RLG Nijhuis (Cardiologist ZGT),Jorn-Jan van de Beld MSc. (UT)