Mahbub Ul Alam- Doctor of Philosophy
- Doctoral Researcher at Stockholm University
Mahbub Ul Alam
- Doctor of Philosophy
- Doctoral Researcher at Stockholm University
My University Profile: https://mahbub.blogs.dsv.su.se
About
14
Publications
1,394
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Citations
Introduction
My journey in pursuing artificial intelligence (AI) began with computational linguistics at the University of Stuttgart, Germany, and led to deep dives into healthcare AI at Stockholm University. My doctoral work is dedicated to advancing clinical decision support through machine learning and the Internet of Medical Things (IoMT), with a sharp focus on improving the detection of COVID-19 and early sepsis.
Current institution
Education
September 2018 - March 2024
Stockholm University
Field of study
- Computer and Systems Sciences
October 2015 - October 2017
January 2007 - November 2010
Publications
Publications (14)
Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest ra...
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 wide...
The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using...
The interpretability of deep neural networks has become a subject of great interest within the medical and healthcare domain. This attention stems from concerns regarding transparency, legal and ethical considerations, and the medical significance of predictions generated by these deep neural networks in clinical decision support systems. To addres...
This thesis presents a critical examination of the positive impact of Machine Learning (ML) and the Internet of Medical Things (IoMT) for advancing the Clinical Decision Support System (CDSS) in the context of COVID-19 and early sepsis detection.
It emphasizes the transition towards patient-centric healthcare systems, which necessitate personalize...
The interpretability of deep neural networks has become a subject of great interest within the medical and healthcare domain. This attention stems from concerns regarding transparency, legal and ethical considerations, and the medical significance of predictions generated by these deep neural networks in clinical decision support systems. To addres...
COVID-19 is a viral infectious disease that has created a global pandemic, resulting in millions of deaths and disrupting the world order. Different machine learning and deep learning approaches were considered to detect it utilizing different medical data. Thermal imaging is a promising option for detecting COVID-19 as it is low-cost, non-invasive...
The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challen...
Background
Surveillance for healthcare-associated infections such as healthcare-associated urinary tract infections (HA-UTI) is important for directing resources and evaluating interventions. However, traditional surveillance methods are resource-intensive and subject to bias.
Aim
To develop and validate a fully-automated surveillance algorithm fo...
Many natural language processing applications rely on the availability of domain-specific terminologies containing synonyms. To that end, semi-automatic methods for extracting additional synonyms of a given concept from corpora are useful, especially in low-resource domains and noisy genres such as clinical text, where nonstandard language use and...
Sepsis is a life-threatening complication to infections, and early treatment is key for survival. Symptoms of sepsis are difficult to recognize, but prediction models using data from electronic health records (EHRs) can facilitate early detection and intervention. Recently, deep learning architectures have been proposed for the early prediction of...
The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential,...
Currently deep neural network based models are showing state of the art results in automatic speech recognition domain. However, still plenty of questions are left undetermined to understand the computational data flow, and feature classification process of hidden layers in deep neural networks. Visualization in hidden layer mechanisms can provide...
