The paper written by Rahmat Mulyana, Lazar Rusu, and Erik Perjons (members of IT Management and Governance group at DSV) and entitled “How Hybrid IT Governance Mechanisms Influence Digital Transformation and Organizational Performance in the Banking and Insurance Industry in Indonesia” has been nominated as a runner-up for best paper award at the 31st International Conference on Information Systems Development (ISD 2023) conference, Lisbon, Portugal.
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 written by Rahmat Mulyana, Lazar Rusu, and Erik Perjons (members of IT Management and Governance group at DSV) and entitled “Hybrid IT Governance Mechanisms that Influence Digital Transformation and Organizational Performance in Bank Rakyat Indonesia (BRI)” has received the best paper award at ICECH2022 – The 10th International Conference on Emerging Challenges: Strategic Adaptation in the World of Uncertainties, November 4-5, 2022, Ho Chi Minh City, Vietnam. The paper has been invited to be extended and published in a special issue of the Pacific Asia Journal of the Association for Information Systems (PAJAIS).
Welcome to the Predoc Seminar: Gideon Mekonnen Jonathan & Midterm seminar: Rahmat Mulyana
Predoc Seminar: IT Alignment in Public Organisations
Date: October 11, 2022 13:00-16:00 Room M10, DSV and Zoom
Respondent: Gideon Mekonnen Jonathan
More information about this seminar are at the following link: https://www.su.se/department-of-computer-and-systems-sciences/calendar/predoc-seminar-gideon-mekonnen-jonathan-1.630043
Midterm seminar: IT Governance Influence on Digital Transformation
Date: October 12, 2022 10:00-12:00 Room M10, DSV and Zoom
Respondent: Rahmat Mulyana
More information about this seminar are at the following
link: https://www.su.se/institutionen-for-data-och-systemvetenskap/kalender/halvtidsseminarium-rahmat-mulyana-1.629906
Alexander Dolk presented his and Hjalmar Davidsen master thesis in form of a scientific paper with the title Evaluation of LIME and SHAP in Explaining Automatic ICD-10 Classifications of Swedish Gastrointestinal Discharge Summaries at the 18th Scandinavian Conference on Health Informatics, SHI 2022, 22-23 Aug, 2022 i Tromsø, Norway, both supervisor Thomas Vakili and I were also part of the paper.
The research work were part of the ClinCode project in Tromsø. At the conference another paper also from the ClinCode project was presented with title The Influence of NegEx on ICD-10 Code Prediction in Swedish: How is the Performance of BERT and SVM Models Affected by Negations? by Andrius Budrionis, Taridzo Chomutare, Therese Olsen Svenning and Hercules Dalianis.
There is a conference report from SHI 2022 available upon request to Hercules.
Hello Everyone,
Greetings. I hope you are well. I would like to share some very good news with you.
I recently published a paper with two other co-authors at the IEEE 35th International Symposium on Computer Based Medical Systems (CBMS 2022, July 21-23, 2022, Shenzhen, China, Online Event). CBMS is the premier conference for computer-based medical systems, and one of the main conferences within the fields of medical informatics and biomedical informatics.
The title of my paper is “Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography“, (Mahbub Ul Alam, Jón Rúnar Baldvinsson and Yuxia Wang)”. In this paper, we tried to explain the decision process of deep neural networks to predict pathology (abnormality) in chest-X ray data using two popular interpretable methods. We investigated whether this explanation matches the clinical diagnosis or not. Interpretability is very crucial and it is emphasized in the recent European Union Artifical Intelligence Act. We hope that this paper will create a positive impact in this aspect.
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).
I am very honoured and would like to thank DSV for providing me with this opportunity. I am fortunate to be working here to get a second award for my research work. Previously I won the ‘best paper award’ at BIOSTEC HEALTHINF 2020 (you can read more about it here).
Want to know more about the paper? Please check out the following presentation video I made!
Abstract:
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.
