QUAKE: Quality control of medical performance with unstructured EMR data
- Prosjektnummer
- HST1194-14
- Ansvarlig person
- Robert Jenssen
- Institusjon
- Universitetssykehuset Nord-Norge HF
- Prosjektkategori
- flerårig forskningsprosjekt som omfatter flere forskere
- Helsekategori
- Cancer and neoplasms
- Forskningsaktivitet
- 4. Detection and Diagnosis, 6. Treatment Evaluation
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Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series.
IEEE J Biomed Health Inform 2020 Dec 07;PP(). Epub 2020 des 7
PMID: 33284756
Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data.
Comput Math Methods Med 2019;2019():2059851. Epub 2019 feb 19
PMID: 30915154
Using anchors from free text in electronic health records to diagnose postoperative delirium.
Comput Methods Programs Biomed 2017 Dec;152():105-114. Epub 2017 sep 19
PMID: 29054250
Analysis of free text in electronic health records for identification of cancer patient trajectories.
Sci Rep 2017 04 07;7():46226. Epub 2017 apr 7
PMID: 28387314
Data-driven approach for assessing utility of medical tests using electronic medical records.
J Biomed Inform 2015 Feb;53():270-6. Epub 2014 des 4
PMID: 25481626
Support Vector Feature Selection for Early Detection of Anastomosis Leakage from Bag-of-Words in Electronic Health Records.
IEEE J Biomed Health Inform 2014 Oct 8. Epub 2014 okt 8
PMID: 25312965
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs
Explainable AI in Healthcare and Medicine, Studies in Computational Intelligence, 2020
Learning representations of multivariate time series with missing data
Pattern Recognition 96, 106973, 2019
Noisy multi-label semi-supervised dimensionality reduction
Pattern Recognition 90, 257-270, 2019
Using multi-anchors to identify patients suffering from multimorbidities
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1514-1521), 2018
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks
IEEE EMBS International Conference on Biomedical & Health Informatics, 2018
Towards deep anchor learning
IEEE EMBS International Conference on Biomedical & Health Informatics, 2018
Learning compressed representations of blood samples time series with missing data
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018
Robust clustering using a kNN mode seeking ensemble
Pattern Recognition 76, 491-505, 2018
From unstructured EHR text to data-driven clinical decision support.
International Journal of Integrated Care 2015 ;Volum 15.2015
Data-driven approach for assessing utility of medical tests using electronic medical records
Journal of Biomedical Informatics 2015 ;Volum 53. s. 270-276
Data-driven Temporal Prediction of Surgical Site Infection
AMIA Annual Symposium Proceedings 2015
Long-term Readmissions and Complications in Open and Laparoscopic Colorectal Cancer Surgery. A Propensity Score Matched Analysis
Journal of American College of Surgeons, October 2014Volume 219, Issue 4, Supplement, Page e146
Advancing Unsupervised and Weakly Supervised Learning with Emphasis on Data-Driven Healthcare
- Disputert:
- februar 2019
- Hovedveileder:
- Robert Jenssen
- Jonas Nordhaug Myhre Prosjektdeltaker
- Michael C. Kampffmeyer Prosjektdeltaker
- Rolv-Ole Lindsetmo Prosjektleder
- Robert Jenssen Prosjektdeltaker
- Karl Øyvind Mikalsen Prosjektdeltaker
- Knut Magne Augestad Prosjektdeltaker
- Stein Olav Skrøvseth Prosjektdeltaker
- Arthur Revhaug Prosjektdeltaker
- Olav Magnus Ivar Liavåg Doktorgradsstipendiat
- Kim Erlend Mortensen Prosjektdeltaker
- Fred Godtliebsen Prosjektdeltaker
- Kristian Hindberg Prosjektdeltaker
eRapport er utarbeidet av Sølvi Lerfald og Reidar Thorstensen, Regionalt kompetansesenter for klinisk forskning, Helse Vest RHF, og videreutvikles av de fire RHF-ene i fellesskap, med støtte fra Helse Vest IKT
Alle henvendelser rettes til eRapport, Helse Nord