| Development and Validation of a Multivariable Prediction Model for the Occurrence of Delirium in Hospitalized Gerontopsychiatry and Internal Medicine Patients. D Kramer, S Veeranki, D Hayn, F Quehenberger, W Leodolter, C Jagsch, ... eHealth, 32-39, 2017 | 26 | 2017 |
| Predictive analytics for data driven decision support in health and care D Hayn, S Veeranki, M Kropf, A Eggerth, K Kreiner, D Kramer, G Schreier it-Information Technology 60 (4), 183-194, 2018 | 16 | 2018 |
| Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events. M Schrempf, D Kramer, S Jauk, SPK Veeranki, W Leodolter, PP Rainer dHealth, 136-143, 2021 | 9 | 2021 |
| An improvised classification model for predicting delirium SPK Veeranki, D Hayn, S Jauk, F Quehenberger, D Kramer, W Leodolter, ... Medinfo 2019: Health and Wellbeing E-Networks for All, 1566-1567, 2019 | 8 | 2019 |
| Effect of Nursing Assessment on Predictive Delirium Models in Hospitalised Patients. SPK Veeranki, D Hayn, D Kramer, S Jauk, G Schreier eHealth, 124-131, 2018 | 8 | 2018 |
| Information Adapted Machine Learning Models for Prediction in Clinical Workflow. S Jauk, D Kramer, F Quehenberger, SPK Veeranki, D Hayn, G Schreier, ... dHealth, 65-72, 2019 | 7 | 2019 |
| How to improve information technology to support healthcare to address the COVID-19 pandemic: an international survey with health informatics experts M Topaz, LM Peltonen, J Mitchell, D Alhuwail, SS Barakati, A Lewis, ... Yearbook of medical informatics 30 (01), 061-068, 2021 | 6 | 2021 |
| On the representation of machine learning results for delirium prediction in a hospital information system in routine care S Veeranki, D Hayn, A Eggerth, S Jauk, D Kramer, W Leodolter, ... Data, Informatics and Technology: An Inspiration for Improved Healthcare, 97-100, 2018 | 6 | 2018 |
| A transformer-based model trained on large scale claims data for prediction of severe COVID-19 disease progression M Lentzen, T Linden, S Veeranki, S Madan, D Kramer, W Leodolter, ... IEEE Journal of Biomedical and Health Informatics, 2023 | 5 | 2023 |
| ECG classification combining conventional signal analysis, random forests and neural networks-a stacked learning scheme M Baumgartner, M Kropf, L Haider, S Veeranki, D Hayn, G Schreier 2021 Computing in Cardiology (CinC) 48, 1-4, 2021 | 4 | 2021 |
| Explaining machine learning predictions of decision support systems in healthcare S Polat Erdeniz, S Veeranki, M Schrempf, S Jauk, T Ngoc Trang Tran, ... Current Directions in Biomedical Engineering 8 (2), 117-120, 2022 | 3 | 2022 |
| Is regular re-training of a predictive delirium model necessary after deployment in routine care? SPK Veeranki, D Kramer, D Hayn, S Jauk, A Eggerth, F Quehenberger, ... dHealth, 186-191, 2019 | 3 | 2019 |
| Prediction of Readmissions in the German DRG System Based on § 21 Datasets. A Eggerth, D Hayn, S Veeranki, J Stieg, G Schreier GMDS, 170-174, 2018 | 3 | 2018 |
| Evaluation of a machine learning-based dysphagia prediction tool in clinical routine: A prospective observational cohort study S Jauk, D Kramer, SPK Veeranki, A Siml-Fraissler, A Lenz-Waldbauer, ... Dysphagia 38 (4), 1238-1246, 2023 | 2 | 2023 |
| External Validation of a Machine Learning Based Delirium Prediction Software in Clinical Routine. S Jauk, SPK Veeranki, D Kramer, S Högler, D Mühlecker, E Eberhartl, ... dHealth, 93-100, 2022 | 2 | 2022 |
| Towards automated screening of literature on artificial intelligence in nursing H Moen, D Alhuwail, J Björne, L Block, S Celin, E Jeon, K Kreiner, ... MEDINFO 2021: One World, One Health–Global Partnership for Digital …, 2022 | 2 | 2022 |
| Development of an Architecture to Implement Machine Learning Based Risk Prediction in Clinical Routine: A Service-Oriented Approach PP RAINER DHealth 2022: Proceedings of the 16th Health Informatics Meets Digital …, 2022 | 1 | 2022 |
| Patient Record Linkage for Data Quality Assessment Based on Time Series Matching. A Eggerth, D Hayn, K Kreiner, S Veeranki, H Traninger, R Modre-Osprian, ... dHealth, 210-217, 2019 | 1 | 2019 |
| Utilising Information of the Case Fee Catalogue to Enhance 30-Day Readmission Prediction in the German DRG System. A Eggerth, D Hayn, S Veeranki, J Stieg, G Schreier EFMI-STC, 40-44, 2018 | 1 | 2018 |
| Health Informatics Meets EHealth: Digital Insight–Information-Driven Health & Care. Proceedings of the 11th EHealth2017 Conference D Hayn, G Schreier IOS Press, 2017 | 1 | 2017 |
Dieter HaynSenior Scientist at the AIT Austrian Institute of TechnologyCorreu electrònic verificat a ait.ac.at
