Neural Networks for Optimization Problems with Inequality Constraints: The Knapsack Problem. - Mattias Ohlsson; Carsten Peterson; Bo Söderberg (Neural Comput., 1993)

A Study of the Mean Field Approach to Knapsack Problems. - Mattias Ohlsson; Hong Pi (Neural Networks, 1997)

WWW Based Service for Automated Interpretation of Diagnostic Imgages: The AIDI-Heart Project. - Mattias Ohlsson; Andreas Järund; Lars Edenbrandt (HPCN Europe, 1999)

A New Artificial Neural Network Method for the Interpretation of ECGs. - Holger Holst; Lars Edenbrandt; Mattias Ohlsson; Hans Öhlin (ANNIMAB, 2000)

Internet Based Artificial Neural Networks for the Interpretation of Medical Images. - Andreas Järund; Lars Edenbrandt; Mattias Ohlsson; Erik Borälv (ANNIMAB, 2000)

Acute Myocardial Infarction: Analysis of the ECG Using Artificial Neural Networks. - Mattias Ohlsson; Holger Holst; Lars Edenbrandt (ANNIMAB, 2000)

An efficient mean field approach to the set covering problem. - Mattias Ohlsson; Carsten Peterson; Bo Söderberg (Eur. J. Oper. Res., 2001)

A novel approach to local reliability of sequence alignments. - Maximilian Schlosshauer; Mattias Ohlsson (Bioinform., 2002)

Pervasive Self-Care Solutions in Telecardiology. Typical use cases from the EPI-MEDICS project. - Flore Gouaux; Lucas Simon Chautemps; Jocelyne Fayn; Stefano Adami; Mohammad Arzi; Deodato Assanelli; Marie-Claire Forlini; Cesare Malossi; Antonio Martinez; Mattias Ohlsson; Joël Placide; Gian-Luca Ziliani; et al. (MIE, 2003)

Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks. - Henrik Haraldsson; Lars Edenbrandt; Mattias Ohlsson (Artif. Intell. Medicine, 2004)

WeAidU - a decision support system for myocardial perfusion images using artificial neural networks. - Mattias Ohlsson (Artif. Intell. Medicine, 2004)

Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room. - Michael Green; Jonas Björk; Jakob Lundager Forberg; Ulf Ekelund; Lars Edenbrandt; Mattias Ohlsson (Artif. Intell. Medicine, 2006)

A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department. - Jonas Björk; Jakob Lundager Forberg; Mattias Ohlsson; Lars Edenbrandt; Hans Öhlin; Ulf Ekelund (BMC Medical Informatics Decis. Mak., 2006)

Automated decision support for bone scintigraphy. - Mattias Ohlsson; Reza Kaboteh; May Sadik; Madis Suurkula; Milan Lomsky; Peter Gjertsson; Karl Sjöstrand; Jens Richter; Lars Edenbrandt (CBMS, 2009)

Statistical Regularization of Deformation Fields for Atlas-Based Segmentation of Bone Scintigraphy Images. - Karl Sjöstrand; Mattias Ohlsson; Lars Edenbrandt (MICCAI (1), 2009)

Exploring new possibilities for case-based explanation of artificial neural network ensembles. - Michael Green; Ulf Ekelund; Lars Edenbrandt; Jonas Björk; Jakob Lundager Forberg; Mattias Ohlsson (Neural Networks, 2009)

An Automated System for the Detection and Diagnosis of Kidney Lesions in Children from Scintigraphy Images. - Matilda Landgren; Karl Sjöstrand; Mattias Ohlsson; Daniel Ståhl; Niels Chr. Overgaard; Kalle Åström; Rune Sixt; Lars Edenbrandt (SCIA, 2011)

Ensembles of genetically trained artificial neural networks for survival analysis. - Jonas Kalderstam; Patrik Edén; Mattias Ohlsson (ESANN, 2013)

Training artificial neural networks directly on the concordance index for censored data using genetic algorithms. - Jonas Kalderstam; Patrik Edén; Pär-Ola Bendahl; Carina Strand; Mårten Fernö; Mattias Ohlsson (Artif. Intell. Medicine, 2013)

Transcriptional Regulation of Lineage Commitment - A Stochastic Model of Cell Fate Decisions. - Jose Teles; Cristina Pina; Patrik Edén; Mattias Ohlsson; Tariq Enver; Carsten Peterson (PLoS Comput. Biol., 2013)

Analysis of regional bone scan index measurements for the survival of patients with prostate cancer. - Jonas Kalderstam; May Sadik; Lars Edenbrandt; Mattias Ohlsson (BMC Medical Imaging, 2014)

Automatic Gleason grading of H and E stained microscopic prostate images using deep convolutional neural networks. - Anna Gummeson; Ida Arvidsson; Mattias Ohlsson; Niels Christian Overgaard; Agnieszka Krzyzanowska; Anders Heyden; Anders Bjartell; Kalle Åström (Digital Pathology, 2017)

Variational auto-encoders with Student's t-prior. - Najmeh Abiri; Mattias Ohlsson (ESANN, 2019)

Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems. - Najmeh Abiri; Björn Linse; Patrik Edén; Mattias Ohlsson (Neurocomputing, 2019)

Corrupted Contextual Bandits with Action Order Constraints. - Alexander Galozy; Slawomir Nowaczyk; Mattias Ohlsson (CoRR, 2020)

Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation. - Alexander Galozy; Slawomir Nowaczyk; Anita Pinheiro Sant'Anna; Mattias Ohlsson; Markus Lingman (Int. J. Medical Informatics, 2020)

SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP. - Abdallah Alabdallah; Sepideh Pashami; Thorsteinn S. Rögnvaldsson; Mattias Ohlsson (DSAA, 2022)

Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain. - Ola Hall; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson (CoRR, 2022)

Deterministic annealing with Potts neurons for multi-robot routing. - Jennifer David; Thorsteinn S. Rögnvaldsson; Bo Söderberg; Mattias Ohlsson (Intell. Serv. Robotics, 2022)

A review of explainable AI in the satellite data, deep machine learning, and human poverty domain. - Ola Hall; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson (Patterns, 2022)

Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks. - Hamid Sarmadi; Thorsteinn S. Rögnvaldsson; Nils Roger Carlsson; Mattias Ohlsson; Ibrahim Wahab; Ola Hall (DSAA, 2023)

A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning. - Ali Amirahmadi; Mattias Ohlsson; Kobra Etminani; Olle Melander; Jonas Björk (MIE, 2023)

Deep learning prediction models based on EHR trajectories: A systematic review. - Ali Amirahmadi; Mattias Ohlsson; Kobra Etminani (J. Biomed. Informatics, 2023)

Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation. - Pontus Olsson de Capretz; Anders Björkelund; Jonas Björk; Mattias Ohlsson; Arash Mokhtari; Axel Nyström; Ulf Ekelund (BMC Medical Informatics Decis. Mak., 2023)

Improving Concordance Index in Regression-based Survival Analysis: Evolutionary Discovery of Loss Function for Neural Networks. - Mohammed Ghaith Altarabichi; Abdallah Alabdallah; Sepideh Pashami; Thorsteinn S. Rögnvaldsson; Slawomir Nowaczyk; Mattias Ohlsson (GECCO Companion, 2024)

Understanding Survival Models Through Counterfactual Explanations. - Abdallah Alabdallah; Jakub Jakubowski; Sepideh Pashami; Szymon Bobek; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson; Grzegorz J. Nalepa (ICCS (4), 2024)

Surveillance of Disease Outbreaks Using Unsupervised Uni-Multivariate Anomaly Detection of Time-Series Symptoms. - Atiye Sadat Hashemi; Mirfarid Musavian Ghazani; Mattias Ohlsson; Jonas Björk; Dominik Dietler (MIE, 2024)

The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models. - Abdallah Alabdallah; Mattias Ohlsson; Sepideh Pashami; Thorsteinn S. Rögnvaldsson (Artif. Intell. Medicine, 2024)

CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis. - Abdallah Alabdallah; Omar Hamed; Mattias Ohlsson; Thorsteinn S. Rögnvaldsson; Sepideh Pashami (CoRR, 2024)

Utilizing artificial intelligence and medical experts to identify predictors for common diagnoses in dyspneic adults: A cross-sectional study of consecutive emergency department patients from Southern Sweden. - Ellen T. Heyman; Awais Ashfaq; Ulf Ekelund; Mattias Ohlsson; Jonas Björk; Alexander Marcel Schubert; Markus Lingman; Ardavan M. Khoshnood (Int. J. Medical Informatics, 2025)

Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer. - Daqu Zhang; Looket Dihge; Pär-Ola Bendahl; Ida Arvidsson; Magnus Dustler; Julia Ellbrant; Kim Gulis; Malin Hjärtström; Mattias Ohlsson; Cornelia Rejmer; David Schmidt; Sophia Zackrisson; et al. (npj Digit. Medicine, 2025)

Cycle Conditioning for Robust Representation Learning from Categorical Data. - Mohsen Tabejamaat; Farzaneh Etminani; Mattias Ohlsson (Trans. Mach. Learn. Res., 2025)

Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research. - Hamid Sarmadi; Ola Hall; Thorsteinn S. Rögnvaldsson; Mattias Ohlsson (CoRR, 2025)

A new bandit setting balancing information from state evolution and corrupted context. - Alexander Galozy; Slawomir Nowaczyk; Mattias Ohlsson (Data Min. Knowl. Discov., 2025)