Ming DK, Hernandez B, Sangkaew S, Vuong NL, Lam PK, Nguyet NM, Tam DTH, Trung DT, Tien NTH, Tuan NM, Chau NVV, Tam CT, Chanh HQ, Trieu HT, Simmons CP, Wills B, Georgiou P, Holmes AH, Yacoub S, Vietnam ICU Translational Applications Laboratory (VITAL) investigators} {. Applied Machine Learning for the Risk-Stratification and Clinical Decision Support of Hospitalised Patients with Dengue in Vietnam. PLOS Digital Health. 2022 Jan.; 1(1):e0000005.      [DOI] 

Lu P, Ghiasi S, Hagenah J, Hai HB, Hao NV, Khanh PNQ, Khoa LDV,  VC, Thwaites L, Clifton DA, Zhu T. Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing. Sensors. 2022 Aug.; 22(17):6554.      [DOI] 

Schwab P, Karlen W. A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data. IEEE Journal of Biomedical and Health Informatics. 2021 Apr.; 25(4):1284--1291.      [DOI]      [Datei] 

Ferretti A, Ienca M, Sheehan M, Blasimme A, Dove ES, Farsides B, Friesen P, Kahn J, Karlen W, Kleist P, Liao SM, Nebeker C, Samuel G, Shabani M, Rivas Velarde M, Vayena E. Ethics review of big data research: What should stay and what should be reformed?. BMC Medical Ethics. 2021 Dez.; 22(1):51.      [DOI]      [Datei] 

Muroi C, Meier S, De Luca V, Mack DJ, Strässle C, Schwab P, Karlen W, Keller E. Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection. Neurocritical Care. 2020; 32(2):419--426.      [DOI]      [Datei] 

Hüser M, Kündig A, Karlen W, De Luca V, Jaggi M. Forecasting intracranial hypertension using multi-scale waveform metrios. Physiological Measurement. 2020; 41(1):014001.      [DOI]      [Datei] 

Schwab P, Linhardt L, Bauer S, Buhmann JM, Karlen W. Learning Counterfactual Representations for Estimating Individual Dose-Response Curves. Proceedings of the AAAI Conference on Artificial Intelligence. 2020 Apr.; 34(04):5612--5619.      [DOI]      [Datei] 

Brogli L, Karlen W. A Review of Deep Learning for Automatic Sleep Stage Scoring. In: International Conference on Advanced Sleep Modulation Technologies. Monte Verita, Ascona, Switzerland: 2019.

Schwab P, Karlen W. CXPlain: Causal Explanations for Model Interpretation under Uncertainty}. In: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019. Vancouver, CA: 2019. p. accepted.     [Datei] 

Schwab P, Miladinovic D, Karlen W. Granger-Causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 2019 Jul.; 334846--53.      [DOI]      [Datei] 

Schwab P, Karlen W. PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data. Proceedings of the AAAI Conference on Artificial Intelligence. 2019 Jul.; 331118--25.      [DOI]      [Datei] 

Rodriguez Orefice H, Scebba G, Catanzaro S, Berli M, Karlen W. Wound image segmentation with deep neural networks. In: Annual Meeting of the Swiss Society for Biomedical Engineering (SSBE) 2019. 2019.

Schwab P, Keller E, Muroi C, Mack DJ, Strässle C, Karlen W. Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care. In: Proceedings of the 35th International Conference on Machine Learning, ICML 2018. Stockholm, Sweden: 2018 Feb.. p. 4525----34.      [DOI]      [Datei] 

Schwab P, Linhardt L, Karlen W. Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks. ArXiv Preprint. 2018;     [Datei] 

Schwab P, Khashkhashi Moghaddam MA, Karlen W. Automated Extraction of Digital Biomarkers for Parkinson ' s Disease. In: 10th Annual RECOMB/ISCB Conference on Regulatory & Systems Genomics with DREAM Challenges. New York, USA: Sage Bionetworks; 2017.     [Datei] 

Schwab P, Scebba GC, Zhang J, Delai M, Karlen W. Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks. Computing in Cardiology (CinC). 2017 Sep.; 441--4.      [DOI]      [Datei] 

Luca VD, Jaggi M, Karlen W, Keller E. Temporal prediction of cerebral hypoxia in neurointensive care patients: a feasibility study. In: International Symposium on Intracranial Pressure and Neuromonitoring. 2016. p. 86--87.

Jaggi M, Karlen W, Keller E. Forecasting intracranial hypertension using waveform and time series features. In: Vasospasm 2015 - 13th International Conference on Neurovascular Events after Subarachnoid Hemorrhage. Nagano, Japan: 2015.

Jaggi M, Luca VD, Karlen W. Predicting intracranial pressure elevation using multiparameter summaries of physiological channels. In: Annual Meeting of the Swiss Society for Biomedical Engineering (SSBE). Neuchatel, Switzerland: 2015.

Duerr P, Karlen W, Guignard J, Mattiussi C. Evolutionary Selection of Features for Neural Sleep/Wake Discrimination. Journal of Artificial Evolution and Applications. 2009; 20091--10.      [DOI]      [Datei] 

Karlen W, Mattiussi C, Floreano D. Sleep and Wake Classification With ECG and Respiratory Effort Signals. IEEE Transactions on Biomedical Circuits and Systems. 2009; 3(2):71--8.      [DOI] 

Karlen W, Mattiussi C, Floreano D. Improving actigraph sleep/wake classification with cardio-respiratory signals. In: Annual Int. Conference of the IEEE Engineering in Medicine and Biology Society.. Vancouver: IEEE Engineering in Medicine and Biology Society; 2008 Jan.. p. 5262--5.      [DOI]      [Datei] 

Karlen W, Mattiussi C, Floreano D. Human Sleep/Wake Classification. In: BMES Annual Chapter Conference. Lausanne, CH: 2007.