Gideon Mekonnen Jonathan, a member of the Digital Transformation research group, has received best paper award for the papers presented at the Annual Conference of the Midwest Association for Information Systems(MWAIS) and the ACM SIGMIS Computers and People Research 2024. For more information see the link: https://www.linkedin.com/posts/gideonmekonnen_awards-workforceagility-digitaltransformation-activity-7247694291463004160-vmIW?utm_source=share&utm_medium=member_desktop.
Papers accepted for publication in CENTERIS–International Conference on ENTERprise Information Systems 2024, and European, Mediterranean and Middle Eastern Conference on Information Systems (EMCIS) 2024
The paper written by Müjen Kavak and Lazar Rusu, and entitled: “Challenges and Opportunities of Artificial Intelligence in Digital Transformation: A Systematic Literature Review”, has been accepted for publication in Procedia Computer Science, Elsevier of CENTERIS – International Conference on ENTERprise Information Systems 2024, Funchal, Madeira, Portugal
The paper written by Destelia Ngwenya, Jamilus Siam, and Lazar Rusu, and entitled: “Success Factors in Digital Transformation in a Medium-sized Swedish IT Company” has been accepted for publication in Procedia Computer Science, Elsevier of CENTERIS – International Conference on ENTERprise Information Systems 2024, Funchal, Madeira, Portugal
The paper written by Anouar Aboulkacim, Dimitrios Lemperos, and Lazar Rusu and entitled: “Structural and Cultural Barriers to Digital Transformation in Swedish Public Organizations” has been accepted for publication in Procedia Computer Science, Elsevier of CENTERIS – International Conference on ENTERprise Information Systems 2024, Funchal, Madeira, Portugal
The paper written by Erotokritos Chairistanidis and Lazar Rusu and entitled: “Barriers to Digital Transformation in Public Organizations: A Case Study of a Greek Public Organization” has been accepted for publication in Lecture Notes in Business Information Processing (LNBIP), Springer of European, Mediterranean and Middle Eastern Conference on Information Systems (EMCIS) 2024, Athens, Greece
The paper written by Ryan Adhitya Nugraha, Lazar Rusu, and Erik Perjons and entitled: “Organizational Culture Values for a Successful Digital Transformation: A Systematic Literature Review” has been accepted for publication in Lecture Notes in Business Information Processing (LNBIP), Springer of European, Mediterranean and Middle Eastern Conference on Information Systems (EMCIS) 2024, Athens, Greece
Runner-up for best paper award at the 31st International Conference on Information Systems Development (ISD 2023) conference
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.
Early sepsis detection – Best paper award – ICTAI 2022
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.
Best paper award at ICECH2022-The 10th International Conference on Emerging Challenges: Strategic Adaptation in the World of Uncertainties
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).
Master thesis presentation at SHI 2022, Tromsø
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.
PhD Student Mahbub Ul Alam Received the Best Student Paper Award at the IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS 2022)
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.
LREC 2022 in Marseille, France
The 13th Language Resources and Evaluation Conference (LREC 2022) was held in Marseille, France with over 1000 participants. Four of us from DSV were there to present our recent findings and learn about the state of the NLP field. Anastasios Lamproudis, Aron Henriksson, Hercules Dalianis and I (Thomas Vakili) had a total of four papers for the conference and its workshops.
All four of us presented a paper about continued pre-training BERT models using automatically de-identified clinical data. We showed that pre-training with safer de-identified clinical data works just as well as using sensitive data. During the conference, we also received ethical approval to share one of the models with academic researchers.
I also presented two workshop papers co-written with researchers from Linköping University, Linköping University Hospital and RISE. The first paper was about using a clinical BERT model to conduct terminology extraction to find terms associated with medical implants in electronic health records. The other paper investigated how well the de-identification system developed at DSV using the Health Bank performs on data from clinics not present in our datasets.
Anastasios, Aron and Hercules presented a paper in which they evaluated various strategies for creating clinical BERT models. They compared initializing the model from a general-domain model versus pre-training from scratch, and whether adapting the general-domain vocabulary to the clinical domain helps or not. They found that all strategies lead to improvements on clinical tasks, but that all strategies ultimately lead to similarly performing models. However, initializing from a general-domain model decreased the amount of training needed.
We had many fruitful discussions and returned home full of ideas to try out. If you are interested in seeing our posters, then you can find them here and here.