On May 11, 2023, our colleague Gideon Mekonnen Jonathan, PhD student in IT Management and Governance group at DSV/Stockholm University has defended successfully his PhD thesis entitled: “Information Technology Alignment: Towards Successful Digital Transformation”. His PhD thesis can be downloaded from the following link in DiVA: https://www.diva-portal.org/smash/get/diva2:1743531/FULLTEXT06.pdf. On behalf of research group in IT Management and Governance, I would like to congratulate Gideon Mekonnen Jonathan for this great achievement.
Congratulations for the Phd stipend from the PhD Visiting Program 2023 from Center for Mathematical Modeling (CMM) at the University of Chile, (Universidad de Chile) in Santiago, Chile. This will make it possible for Thomas Vakili to visit the center during three months the fall of 2023 and work with privacy preserving methods for Chilean patient records jointly with Dr. Jocelyn Dunstan that invited Thomas.
The 34th IEEE International Conference on Tools with Artificial Intelligence was held virtually from 31s of October to the 2nd of November. With Aron Henriksson and in collaboration with Karolinska Institutet we presented our paper “Improving the Timeliness of Early Prediction Models for Sepsis through Utility Optimization” and we are very happy to announce that we received the best paper award. In the paper that will be published in the proceedings of the conference, we explore the capabilities of using custom objective functions to develop a machine learning model that can perform sepsis prediction over time in a manner that will be useful for practitioners in assisting them to perform timely intervention and initiate treatment early, which is key to survival.
The paper was received well during the CBMS 2022 symposium presentation time. I am delighted to inform you that the paper received the ‘best student paper award’. The award was provided by the IEEE Technical Committee on Computational Life Science (TCCLS).
Want to know more about the paper? Please check out the following presentation video I made!
The area of interpretable deep neural networks has received increased attention in recent years due to the need for transparency in various fields, including medicine, healthcare, stock market analysis, compliance with legislation, and law. Layer-wise Relevance Propagation (LRP) and Gradient-weighted Class Activation Mapping (Grad-CAM) are two widely used algorithms to interpret deep neural networks. In this work, we investigated the applicability of these two algorithms in the sensitive application area of interpreting chest radiography images. In order to get a more nuanced and balanced outcome, we use a multi-label classification-based dataset and analyze the model prediction by visualizing the outcome of LRP and Grad-CAM on the chest radiography images. The results show that LRP provides more granular heatmaps than Grad-CAM when applied to the CheXpert dataset classification model. We posit that this is due to the inherent construction difference of these algorithms (LRP is layer-wise accumulation, whereas Grad-CAM focuses primarily on the final sections in the model’s architecture). Both can be useful for understanding the classification from a micro or macro level to get a superior and interpretable clinical decision support system.
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