11. M.U. Alam, J. Hollmén, J. R. Baldvinsson and R. Rahmani. SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction. Nordic Machine Intelligence. 2023; vol. 3, no. 1, pp. 27–47. https://doi.org/10.5617/nmi.10471. (download link, codes & results)
10. M.U. Alam, J. Hollmén and R. Rahmani. COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning. IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 2023, pp. 646-653, https://doi.org/10.1109/CBMS58004.2023.00294. (codes & results, presentation slides)
9. Alam MU, Rahmani R. FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices. Sensors. 2023; 23(2):970. https://doi.org/10.3390/s23020970. PubMed ID: 36679766 (download link, presentation slides, codes)
8. M. U. Alam, J. R. Baldvinsson and Y. Wang. Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography. IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), 2022, pp. 258-263, https://doi.org/10.1109/CBMS55023.2022.00052. (codes & results, presentation slides, presentation video, best student paper award)
7. Alam MU, Rahmani R. Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application. Sensors. 2021; 21(15):5025. https://doi.org/10.3390/s21155025. PubMed ID: 34372262 (download link, poster presentation)
6. Alam M.U., Rahmani R. (2021) Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_18
5. Van Der Werff, S., Thiman, E., Tanushi, H., Valik, J.K, Henriksson, A., Ul Alam, M., Dalianis, H., Ternhag, A., Naucler, P (2021). The accuracy of fully-automated algorithms for surveillance of healthcare-associated urinary tract infections in hospitalized patients. Journal of Hospital Infection, Volume 110, April 2021, Pages 139-147, ISSN: 0195-6701. https://doi.org/10.1016/j.jhin.2021.01.023
4. Alam, Mahbub Ul; Henriksson, A.; Tanushi, H.; Thiman, E.; Naucler, P. and Dalianis, H. (2021). Terminology Expansion with Prototype Embeddings: Extracting Symptoms of Urinary Tract Infection from Clinical Text. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies – Volume 4: HEALTHINF, ISBN 978-989-758-490-9, pages 47-57. https://doi.org/10.5220/0010190200470057 (download link, presentation slides)
3. Mahbub Ul Alam, Rahim Rahmani, Intelligent context-based healthcare metadata aggregator in internet of medical things platform, Procedia Computer Science, Volume 175, 2020, Pages 411-418, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2020.07.058 (download link, presentation video)
2. Alam, M. U., Henriksson, A., Karlsson Valik, J., Ward, L., Naucler, P., & Dalianis, H. (2020). Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (Vol. 5, pp. 45–55). SciTePress. https://doi.org/10.5220/0008911400450055 (download link, presentation slides, presentation video, best paper award)
1. Alam, Mahbub Ul. (2017). From speech to image; a novel approach to understand the hidden layer mechanisms of deep neural networks in automatic speech recognition Master’s Thesis (Grade: 1.0) in Computational Linguistics at the Institute for Natural Language Processing, University of Stuttgart, Germany. Advisor: Prof. Dr. Ngoc Thang Vu. http://dx.doi.org/10.13140/RG.2.2.18693.01765 (download link, bibliography link, codes, presentation slides)