| 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, ... Health Informatics Meets eHealth, 32-39, 2017 | 26 | 2017 |
| 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 27 (9), 4548-4558, 2023 | 19 | 2023 |
| 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 | 17 | 2018 |
| Machine learning based risk prediction for major adverse cardiovascular events M Schrempf, D Kramer, S Jauk, SPK Veeranki, W Leodolter, PP Rainer Navigating Healthcare Through Challenging Times, 136-143, 2021 | 13 | 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 | 10 | 2022 |
| 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 | 10 | 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 | 10 | 2019 |
| Information adapted machine learning models for prediction in clinical workflow S Jauk, D Kramer, F Quehenberger, SPK Veeranki, D Hayn, G Schreier, ... dHealth 2019–From eHealth to dHealth, 65-72, 2019 | 9 | 2019 |
| Introduction and comparison of novel decentral learning schemes with multiple data pools for privacy-preserving ECG classification M Baumgartner, SPK Veeranki, D Hayn, G Schreier Journal of healthcare informatics research 7 (3), 291-312, 2023 | 8 | 2023 |
| Effect of nursing assessment on predictive delirium models in hospitalised patients SPK Veeranki, D Hayn, D Kramer, S Jauk, G Schreier Health Informatics Meets eHealth, 124-131, 2018 | 8 | 2018 |
| 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 | 7 | 2018 |
| Long-term risk of recurrent cerebrovascular events after patent foramen ovale closure: Results from a real-world stroke cohort M Kneihsl, S Horner, I Hatab, N Schöngrundner, D Kramer, G Toth-Gayor, ... European stroke journal 8 (4), 1021-1029, 2023 | 6 | 2023 |
| 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 | 6 | 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 | 5 | 2021 |
| 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 2022, 93-100, 2022 | 4 | 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 2019–From eHealth to dHealth, 210-217, 2019 | 4 | 2019 |
| 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 2019–From eHealth to dHealth, 186-191, 2019 | 4 | 2019 |
| Machine learning-based delirium prediction in surgical in-patients: a prospective validation study S Jauk, D Kramer, S Sumerauer, SPK Veeranki, M Schrempf, P Puchwein JAMIA open 7 (3), ooae091, 2024 | 3 | 2024 |
| 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 | 3 | 2022 |
| Prediction of Readmissions in the German DRG System Based on § 21 Datasets A Eggerth, D Hayn, S Veeranki, J Stieg, G Schreier German Medical Data Sciences: A Learning Healthcare System, 170-174, 2018 | 3 | 2018 |
Dieter HaynSenior Scientist at the AIT Austrian Institute of TechnologyDirección de correo verificada de ait.ac.at