Public Defence: Chi Zhang
MSc Chi Zhang at Institute of Basic Medical Sciences will be defending the thesis “Representation and Utilization of Hospital Electronic Health Records Data” for the degree of PhD (Philosophiae Doctor).
Photo: Robert Smith.
Due to copyright issues, an electronic copy of the thesis must be ordered from the faculty. For the faculty to have time to process the order, the order must be received by the faculty at the latest 2 days before the public defence. Orders received later than 2 days before the defence will not be processed. After the public defence, please address any inquiries regarding the thesis to the candidate.
Trial Lecture – time and place
See Trial Lecture.
- First opponent: Associate Professor Cristina Soguero Ruiz, Universidad Rey Juan Carlos, Spain
- Second opponent: Professor Hugo Lewi Hammer, Oslomet
- Third member and chair of the evaluation committee: Professor II Åslaug Helland, University of Oslo
Chair of the Defence
Professor Rune Blomhoff, University of Oslo
Professor Magne Thoresen, Faculty of Medicine, University of Oslo
Electronic Health Records (EHR) data contain the medical and treatment history of patients and have become widely adopted in hospitals in the last decade. Hospital EHR data collected during patient visits contain rich information covering their disease history and progression, medication, procedures, and diagnoses. The availability of large amounts of patient data has brought new opportunities in several research fields, including medicine, epidemiology and method developments using statistical and artificial intelligence tools. Despite the exciting opportunities, using EHR data for research is challenging. The effective extraction and representation of temporal hospital EHR data is a first step to understand the complexity of hospital environment and improve quality of care.
There are two objectives of this thesis. The first objective is to explore different statistical and computational methods to extract, integrate and represent information from temporal and sequential hospital EHR data. In this thesis I explored data mining algorithms (dynamic time warping), machine learning classification algorithms, network analysis on sequential relational data, regression models and regularization, prediction, and variable selection algorithms. The second objective is to demonstrate the broad scope of potential applications of EHR data in the clinical setting. I used two very different hospital EHR datasets (MIMIC-III data from US, AHUS data from Norway) to illustrate the potential applications in patient risk stratification and hospital management and logistic efficiency.
Contact the research support staff.