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Daniel Stahl
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2020 – today
- 2024
- [c16]Henry Musto, Daniel Stamate, Doina Logofatu, Daniel Stahl:
Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling. ICANN (8) 2024: 359-372 - [i6]Henry Musto, Daniel Stamate, Doina Logofatu, Daniel Stahl:
Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling. CoRR abs/2409.16231 (2024) - 2023
- [j3]Diana Shamsutdinova, Jayati Das-Munshi, Mark Ashworth, Angus Roberts, Daniel Stahl:
Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population. Int. J. Medical Informatics 172: 105019 (2023) - [c15]Henry Musto, Daniel Stamate, Ida M. Pu, Daniel Stahl:
Predicting Alzheimer's Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort. ICCCI 2023: 700-712 - [i5]Henry Musto, Daniel Stamate, Ida M. Pu, Daniel Stahl:
Predicting Alzheimers Disease Diagnosis Risk over Time with Survival Machine Learning on the ADNI Cohort. CoRR abs/2306.10326 (2023) - [i4]Daniel Stamate, Henry Musto, Olesya Ajnakina, Daniel Stahl:
Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort. CoRR abs/2306.10330 (2023) - [i3]Henry Musto, Daniel Stamate, Ida M. Pu, Daniel Stahl:
A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease. CoRR abs/2306.10334 (2023) - [i2]Jaya Chaturvedi, Diana Shamsutdinova, Felix Zimmer, Sumithra Velupillai, Daniel Stahl, Robert Stewart, Angus Roberts:
Sample Size in Natural Language Processing within Healthcare Research. CoRR abs/2309.02237 (2023) - 2022
- [c14]Diana Shamsutdinova, Daniel Stamate, Angus Roberts, Daniel Stahl:
Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes. AIAI (2) 2022: 170-181 - [c13]Daniel Stamate, Henry Musto, Olesya Ajnakina, Daniel Stahl:
Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort. AIAI Workshops 2022: 436-447 - 2021
- [c12]Henry Musto, Daniel Stamate, Ida M. Pu, Daniel Stahl:
A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease. ICMLA 2021: 1443-1448 - [c11]Judith Dineley, Grace Lavelle, Daniel Leightley, Faith Matcham, Sara Siddi, Maria Teresa Peñarrubia-María, Katie M. White, Alina Ivan, Carolin Oetzmann, Sara Simblett, Erin Dawe-Lane, Stuart Bruce, Daniel Stahl, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Amos A. Folarin, Josep Maria Haro, Til Wykes, Richard J. B. Dobson, Vaibhav A. Narayan, Matthew Hotopf, Björn W. Schuller, Nicholas Cummins, RADAR-CNS Consortium:
Remote Smartphone-Based Speech Collection: Acceptance and Barriers in Individuals with Major Depressive Disorder. Interspeech 2021: 631-635 - [i1]Judith Dineley, Grace Lavelle, Daniel Leightley, Faith Matcham, Sara Siddi, Maria Teresa Peñarrubia-María, Katie M. White, Alina Ivan, Carolin Oetzmann, Sara Simblett, Erin Dawe-Lane, Stuart Bruce, Daniel Stahl, Josep Maria Haro, Til Wykes, Vaibhav A. Narayan, Matthew Hotopf, Björn W. Schuller, Nicholas Cummins:
Remote smartphone-based speech collection: acceptance and barriers in individuals with major depressive disorder. CoRR abs/2104.08600 (2021) - 2020
- [c10]Daniel Stamate, Richard Smith, Ruslan Tsygancov, Rostislav Vorobev, John Langham, Daniel Stahl, David Reeves:
Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment. AIAI (2) 2020: 308-319
2010 – 2019
- 2019
- [j2]Simon Riches, Philippa Garety, Mar Rus-Calafell, Daniel Stahl, Clare Evans, Nikolaos Sarras, Keren Yeboah, Lucia Valmaggia:
Using Virtual Reality to Assess Associations Between Paranoid Ideation and Components of Social Performance: A Pilot Validation Study. Cyberpsychology Behav. Soc. Netw. 22(1): 51-59 (2019) - [j1]Katya Rubia, Marion Criaud, M. Wulff, A. Alegria, H. Brinson, Gareth J. Barker, Daniel Stahl, Vincent Giampietro:
Functional connectivity changes associated with fMRI neurofeedback of right inferior frontal cortex in adolescents with ADHD. NeuroImage 188: 43-58 (2019) - 2018
- [c9]Daniel Stahl, Daniel Stamate:
Data Science Challenges in Computational Psychiatry and Psychiatric Research. DSAA 2018: 524-525 - [c8]Wajdi Alghamdi, Daniel Stamate, Daniel Stahl, Alexander Zamyatin, Robin M. Murray, Marta Di Forti:
A New Machine Learning Framework for Understanding the Link Between Cannabis Use and First-Episode Psychosis. eHealth 2018: 9-16 - [c7]Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Doina Logofatu, Alexander Zamyatin:
PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches. AIAI 2018: 273-284 - [c6]Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Alexander Zamyatin, Robin M. Murray, Marta Di Forti:
Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use? AIAI 2018: 311-322 - [c5]Daniel Stamate, Wajdi Alghamdi, Daniel Stahl, Ida M. Pu, Fionn Murtagh, Danielle Belgrave, Robin M. Murray, Marta Di Forti:
Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning. IPMU (3) 2018: 691-702 - 2017
- [c4]Daniel Stamate, Andrea Katrinecz, Wajdi Alghamdi, Daniel Stahl, Philippe Delespaul, Jim van Os, Sinan Guloksuz:
Predicting Psychosis Using the Experience Sampling Method with Mobile Apps. ICMLA 2017: 667-673 - 2016
- [c3]Wajdi Alghamdi, Daniel Stamate, Katherine Vang, Daniel Stahl, Marco Colizzi, Giada Tripoli, Diego Quattrone, Olesya Ajnakina, Robin M. Murray, Marta Di Forti:
A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use. ICMLA 2016: 825-830 - 2013
- [c2]Wolfgang Mathis, Daniel Stahl, Richard Mathis:
Oscillator synthesis based on Nambu mechanics with canonical dissipative damping. ECCTD 2013: 1-4 - [c1]Lina Lagerberg, Tor Skude, Pär Emanuelsson, Kristian Sandahl, Daniel Stahl:
The Impact of Agile Principles and Practices on Large-Scale Software Development Projects: A Multiple-Case Study of Two Projects at Ericsson. ESEM 2013: 348-356
Coauthor Index
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last updated on 2024-10-17 20:32 CEST by the dblp team
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