Mahbub Ul Alam

Orcid: 0000-0002-1101-3793

Affiliations:
  • Stockholm University, Department of Computer and System Sciences, Sweden


According to our database1, Mahbub Ul Alam authored at least 10 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
Advancing Clinical Decision Support Using Machine Learning & the Internet of Medical Things: Enhancing COVID-19 & Early Sepsis Detection.
PhD thesis, 2024

2023
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, January, 2023

SHAMSUL: Simultaneous Heatmap-Analysis to investigate Medical Significance Utilizing Local interpretability methods.
CoRR, 2023

COVID-19 detection from thermal image and tabular medical data utilizing multi-modal machine learning.
Proceedings of the 36th IEEE International Symposium on Computer-Based Medical Systems, 2023

2022
Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography.
Proceedings of the 35th IEEE International Symposium on Computer-Based Medical Systems, 2022

2021
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

Terminology Expansion with Prototype Embeddings: Extracting Symptoms of Urinary Tract Infection from Clinical Text.
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, 2021

2020
Intelligent context-based healthcare metadata aggregator in internet of medical things platform.
Proceedings of the 17th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2020) / The 15th International Conference on Future Networks and Communications (FNC-2020) / The 10th International Conference on Sustainable Energy Information Technology, 2020

Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning.
Proceedings of the Biomedical Engineering Systems and Technologies, 2020

Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis.
Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020), 2020


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