Profiles

Mahbub Ul Alam

Mahbub Alam

Doktorand

View page in English
Arbetar vid Institutionen för data- och systemvetenskap
Telefon 08-16 13 19
E-post mahbub@dsv.su.se
Besöksadress Nodhuset, Borgarfjordsgatan 12
Postadress Institutionen för data- och systemvetenskap 164 07 Kista

Om mig

I am a PhD student at the Department of Computer and System Sciences, Stockholm University.

My research interest is in ‘Cognitive internet of things (IoT) based smart health-care systems using deep learning and advanced machine learning’.

  • Potential research problem areas include:
    1. Machine learning framework for IoT.
    2. Intelligent data, i.e., machine-understandable, resource-recognition, knowledge representation, big data, deep learning, and advanced machine learning.
    3. Clinical decision support systems in health-IoT.
    4. Patient-centric personalized health-care systems and applications.
    5. Distributed intelligent data processing in IoT.

I have a master’s degree in Computational Linguistics at the Institute for Natural Language Processing (IMS), University of Stuttgart, Germany. In my master’s thesis, I worked to understand the hidden layer mechanisms of deep neural networks in natural language processing (automatic speech recognition) domain.

I have more than three years of work experience in software engineering at the Samsung Research and Development Institute Bangladesh.

I believe in diversity, and love to explore new and fresh technological innovations. I like to think that every aspect of my previous experiences is helping me to move forward for my future career. I always keep in mind that, the central principle of my life is ‘Let all of us prosper together.’

 

Publikationer

I urval från Stockholms universitets publikationsdatabas
  • 2020. Mahbub Ul Alam, Rahim Rahmani. Procedia Computer Science 175, 411-418

    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, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

  • 2020. Mahbub Ul Alam (et al.). Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, 45-55, 2020, Valletta, Malta, 45-55

    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 sepsis. However, most efforts rely on high-resolution data from intensive care units (ICUs). Prediction of sepsis in the non-ICU setting, where hospitalization periods vary greatly in length and data is more sparse, is not as well studied. It is also not clear how to learn effectively from longitudinal EHR data, which can be represented as a sequence of time windows. In this article, we evaluate the use of an LSTM network for early prediction of sepsis according to Sepsis-3 criteria in a general hospital population. An empirical investigation using six different time window sizes is conducted. The best model uses a two-hour window and assume s data is missing not at random, clearly outperforming scoring systems commonly used in healthcare today. It is concluded that the size of the time window has a considerable impact on predictive performance when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis.

Visa alla publikationer av Mahbub Alam vid Stockholms universitet

Senast uppdaterad: 10 augusti 2020

Bokmärk och dela Tipsa