Predictive systems using machine learning tools to forecast adverse events during medical stays = Ennustavad süsteemid, mis kasutavad masinõppe vahendeid kõrvalekallete prognoosimiseks haiglas viibimise ajal
author
supervisor
statement of authorship
Nzamba Bignoumba ; [supervisor: Sadok Ben Yahia, co-supervisor: Nedra Mellouli ; Tallinn University of Technology, School of Information of Technologies, Department of Software Science]
type of dissertation
doktoritöö
university/scientific institution
Tallinna Tehnikaülikool
location of publication
Tallinn
publisher
year of publication
pages
133 p. : ill
series
Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 49/2024
subject term
subject of form
ISSN
2585-6898
2585-6901 (PDF)
ISBN
978-9916-80-195-6 (PDF)
978-9916-80-194-9
notes
Autori publikatsioonide nimekiri leheküljel 7
Bibliogr. lk. 33-37
Kokkuvõte eesti keeles
Kättesaadav ka võrguteavikuna
Autori CV inglise ja eesti keeles, lk. 130-133
Thesis (Ph.D. (Computer Science)) : Tallinn University of Technology, 2024
url
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
TalTech department
language
inglise
- Evaluation of deep learning-based depression detection using medical claims data
- A new efficient ALignment-driven Neural Network for mortality prediction from irregular multivariate time series data [Formula presented]
- Deep magnitude management of clinical code embeddings to predict unplanned hospital readmissions
Bignoumba, N. Predictive systems using machine learning tools to forecast adverse events during medical stays = Ennustavad süsteemid, mis kasutavad masinõppe vahendeid kõrvalekallete prognoosimiseks haiglas viibimise ajal. Tallinn : TalTech Press, 2024. 133 p. : ill. (Tallinn University of Technology. Doctoral thesis = Tallinna Tehnikaülikool. Doktoritöö ; 49/2024).