Mattias Ohlsson
- Computational Biology and Biological Physics - Has been reorganised
- Department of Astronomy and Theoretical Physics - Has been reorganised
- Artificial Intelligence in CardioThoracic Sciences (AICTS)
- eSSENCE: The e-Science Collaboration
- Faculty Office
- LU Profile Area: Natural and Artificial Cognition
- Centre for Environmental and Climate Science (CEC)
- Computational Science for Health and Environment
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- 2024
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Mark
The skåne emergency medicine (SEM) cohort
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- Contribution to journal › Article
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Mark
Human bias and CNNs’ superior insights in satellite based poverty mapping
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- Contribution to journal › Article
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Mark
Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms
2024) 34th Medical Informatics Europe Conference, MIE 2024 In Studies in Health Technology and Informatics 316. p.1916-1920(
- Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
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Mark
How does an AI diagnose dyspnoea in ED triage without human guidance?
2024) Swedish Emergency Medicine Talks - SWEETS24(
- Contribution to conference › Poster
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Mark
The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models
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- Contribution to journal › Article
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Mark
Retrospective validation study of an artificial neural network-based preoperative decision-support tool for noninvasive lymph node staging (NILS) in women with primary breast cancer (ISRCTN14341750)
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- Contribution to journal › Article
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Mark
Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients
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- Contribution to journal › Article
- 2023
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Mark
Investigating Ancient Agricultural Field Systems In Sweden From Airborne Lidar Data By Using Convolutional Neural Network
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- Contribution to journal › Article
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Mark
Design of an AI Support for Diagnosis of Dyspneic Adults at Time of Triage in the Emergency Department
2023) European Emergency Medicine Congress 2023(
- Contribution to conference › Poster
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Mark
Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation
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- Contribution to journal › Article