Mattias Ohlsson
- Computational Biology and Biological Physics - Undergoing reorganization
- Department of Astronomy and Theoretical Physics - Undergoing reorganization
- 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)
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- 2023
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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
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Mark
Interpretable AI diagnostics for dyspnea in the emergency department by deep learning and a massive regional health care dataset
2023) Swedish Emergency Medicine Talks - SWEETS23(
- Contribution to conference › Poster
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Mark
A machine learning model for prediction of 30-day primary graft failure after heart transplantation
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- Contribution to journal › Article
- 2022
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Mark
The NILS Study Protocol : A Retrospective Validation Study of an Artificial Neural Network Based Preoperative Decision-Making Tool for Noninvasive Lymph Node Staging in Women with Primary Breast Cancer (ISRCTN14341750)
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- Contribution to journal › Article
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Mark
Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality
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- Contribution to journal › Article
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Mark
Delayed traumatic intracranial haemorrhage : The prevalence is very low in patients on oral anticoagulation
2022) Swedish Emergency Medicine Talks 2022(
- Contribution to conference › Poster
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Mark
Characteristics and severe outcomes of patients presenting to the Emergency Department with dizziness
2022) Swedish Emergency Medicine Talks 2022(
- Contribution to conference › Poster
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Mark
The implementation of a noninvasive lymph node staging (NILS) preoperative prediction model is cost effective in primary breast cancer
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- Contribution to journal › Article
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Mark
Deterministic annealing with Potts neurons for multi-robot routing
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- Contribution to journal › Article
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Mark
A review of explainable AI in the satellite data, deep machine learning, and human poverty domain
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- Contribution to journal › Scientific review