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2020 – today
- 2024
[j13]David Rojas-Velazquez, Sarah Kidwai, Aletta D. Kraneveld
, Alberto Tonda, Daniel L. Oberski, Johan Garssen, Alejandro Lopez Rincon:
Methodology for biomarker discovery with reproducibility in microbiome data using machine learning. BMC Bioinform. 25(1): 26 (2024)- [c6]Qixiang Fang, Zhihan Zhou, Francesco Barbieri, Yozen Liu, Leonardo Neves, Dong Nguyen, Daniel L. Oberski, Maarten W. Bos, Ron Dotsch:
General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study. SIGIR 2024: 2431-2436 - [i11]Qixiang Fang, Daniel L. Oberski, Dong Nguyen:
PATCH - Psychometrics-AssisTed benCHmarking of Large Language Models: A Case Study of Mathematics Proficiency. CoRR abs/2404.01799 (2024) - 2023
- [c5]Qixiang Fang, Anastasia Giachanou, Ayoub Bagheri, Laura Boeschoten, Erik-Jan van Kesteren, Mahdi Shafiee Kamalabad, Daniel L. Oberski:
On Text-based Personality Computing: Challenges and Future Directions. ACL (Findings) 2023: 10861-10879 - [c4]Alejandro Lopez Rincon, David Rojas-Velazquez, Johan Garssen, Sander W. Van der Laan, Daniel L. Oberski, Alberto Tonda:
Bayesian Optimization for the Inverse Problem in Electrocardiography. SSCI 2023: 1593-1598 - [i10]Qixiang Fang, Zhihan Zhou, Francesco Barbieri, Yozen Liu, Leonardo Neves, Dong Nguyen, Daniel L. Oberski, Maarten W. Bos, Ron Dotsch:
Designing and Evaluating General-Purpose User Representations Based on Behavioral Logs from a Measurement Process Perspective: A Case Study with Snapchat. CoRR abs/2312.12111 (2023) - 2022
- [j12]Anastasia Giachanou, Bilal Ghanem, Esteban A. Ríssola, Paolo Rosso, Fabio Crestani, Daniel L. Oberski:
The impact of psycholinguistic patterns in discriminating between fake news spreaders and fact checkers. Data Knowl. Eng. 138: 101960 (2022) - [j11]Qixiang Fang, Dong Nguyen, Daniel L. Oberski:
Evaluating the construct validity of text embeddings with application to survey questions. EPJ Data Sci. 11(1): 39 (2022) - [j10]Richard Bartels, Jeroen Dudink, Saskia Haitjema, Daniel L. Oberski, Annemarie van 't Veen:
A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care. Frontiers Digit. Health 4 (2022) - [j9]Laura Boeschoten, Adriënne Mendrik, Emiel van der Veen, Jeroen Vloothuis, Haili Hu, Roos Voorvaart, Daniel L. Oberski:
Privacy-preserving local analysis of digital trace data: A proof-of-concept. Patterns 3(3): 100444 (2022) - [i9]Qixiang Fang, Dong Nguyen, Daniel L. Oberski:
Evaluating the Construct Validity of Text Embeddings with Application to Survey Questions. CoRR abs/2202.09166 (2022) - [i8]Qixiang Fang, Anastasia Giachanou, Ayoub Bagheri, Laura Boeschoten, Erik-Jan van Kesteren, Mahdi Shafiee Kamalabad, Daniel L. Oberski:
On Text-based Personality Computing: Challenges and Future Directions. CoRR abs/2212.06711 (2022) - 2021
- [j8]Laura Boeschoten, Erik-Jan van Kesteren, Ayoub Bagheri, Daniel L. Oberski:
Achieving Fair Inference Using Error-Prone Outcomes. Int. J. Interact. Multim. Artif. Intell. 6(5): 9-15 (2021) - [j7]Rens van de Schoot, Jonathan de Bruin, Raoul Schram, Parisa Zahedi, Jan de Boer, Felix Weijdema, Bianca Kramer, Martijn Huijts, Maarten Hoogerwerf, Gerbrich Ferdinands, Albert Harkema, Joukje Willemsen, Yongchao Ma, Qixiang Fang, Sybren Hindriks, Lars Tummers, Daniel L. Oberski:
An open source machine learning framework for efficient and transparent systematic reviews. Nat. Mach. Intell. 3(2): 125-133 (2021) - [j6]Arjan Sammani, Ayoub Bagheri, Peter G. M. van der Heijden, Anneline S. J. M. te Riele, Annette F. Baas, C. A. J. Oosters, Daniel L. Oberski, Folkert W. Asselbergs:
Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks. npj Digit. Medicine 4 (2021) - [i7]Laura Boeschoten, Adriënne Mendrik, Emiel van der Veen, Jeroen Vloothuis, Haili Hu, Roos Voorvaart, Daniel L. Oberski:
Privacy preserving local analysis of digital trace data: A proof-of-concept. CoRR abs/2110.05154 (2021) - 2020
- [j5]Ayoub Bagheri, Arjan Sammani, Peter G. M. van der Heijden, Folkert W. Asselbergs, Daniel L. Oberski:
ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients' disease history. J. Intell. Inf. Syst. 55(2): 329-349 (2020) - [j4]Daniel L. Oberski:
Human Data Science. Patterns 1(4): 100069 (2020) - [c3]Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis, Pim A. de Jong, Folkert W. Asselbergs, Daniel L. Oberski:
Multimodal Learning for Cardiovascular Risk Prediction using EHR Data. BCB 2020: 78:1 - [c2]Ayoub Bagheri, Arjan Sammani, Peter G. M. van der Heijden, Folkert W. Asselbergs, Daniel L. Oberski:
Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters. BIOINFORMATICS 2020: 281-289 - [c1]Laura Boeschoten, Irene I. van Driel, Daniel L. Oberski, Loes J. Pouwels:
Instagram Use and the Well-Being of Adolescents: Using Deep Learning to Link Social Scientific Self-reports with Instagram Data Download Packages. ICMI Companion 2020: 523 - [i6]Laura Boeschoten, Erik-Jan van Kesteren, Ayoub Bagheri, Daniel L. Oberski:
Fair inference on error-prone outcomes. CoRR abs/2003.07621 (2020) - [i5]Paulina Pankowska, Daniel L. Oberski:
The effect of measurement error on clustering algorithms. CoRR abs/2005.11743 (2020) - [i4]Rens van de Schoot, Jonathan de Bruin, Raoul Schram, Parisa Zahedi, Jan de Boer, Felix Weijdema, Bianca Kramer, Martijn Huijts, Maarten Hoogerwerf, Gerbrich Ferdinands, Albert Harkema, Joukje Willemsen, Yongchao Ma, Qixiang Fang, Lars Tummers, Daniel L. Oberski:
ASReview: Open Source Software for Efficient and Transparent Active Learning for Systematic Reviews. CoRR abs/2006.12166 (2020) - [i3]Ayoub Bagheri, T. Katrien J. Groenhof, Wouter B. Veldhuis, Pim A. de Jong, Folkert W. Asselbergs, Daniel L. Oberski:
Multimodal Learning for Cardiovascular Risk Prediction using EHR Data. CoRR abs/2008.11979 (2020) - [i2]Laura Boeschoten, Jef Ausloos, J. E. Möller, Theo B. Araujo, Daniel L. Oberski:
A framework for digital trace data collection through data donation. CoRR abs/2011.09851 (2020)
2010 – 2019
- 2019
- [j3]Laura Boeschoten, Marcel A. Croon, Daniel L. Oberski:
A Note on Applying the BCH Method Under Linear Equality and Inequality Constraints. J. Classif. 36(3): 566-575 (2019) - [i1]Erik-Jan van Kesteren, Chang Sun, Daniel L. Oberski, Michel Dumontier, Lianne Ippel:
Privacy-Preserving Generalized Linear Models using Distributed Block Coordinate Descent. CoRR abs/1911.03183 (2019) - 2016
- [j2]Daniel L. Oberski:
Beyond the number of classes: separating substantive from non-substantive dependence in latent class analysis. Adv. Data Anal. Classif. 10(2): 171-182 (2016) - 2013
- [j1]Daniel L. Oberski, Geert H. van Kollenburg, Jeroen K. Vermunt:
A Monte Carlo evaluation of three methods to detect local dependence in binary data latent class models. Adv. Data Anal. Classif. 7(3): 267-279 (2013)
Coauthor Index
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