healthcare

Babar Khan

Ph.D. (former), Bioscience
Ph.D. degree in King Abdullah University of Science and Technology (KAUST)

Ahsan Mehmood

Intern (former), Information Science Lab
Visiting student at King Abdullah University of Science and Technology (KAUST).

Deep Predictive Models for Mining Electronic Health Records

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There is an increasing growth in the amount of electronic health records (EHRs) being collected by healthcare facilities. Data mining techniques hold great potential to systematically use such data for identifying not only inefficiencies but also best practices that improve care and reduce costs. However, due to the complexity of EHR data, directly applying traditional machine learning techniques may yield unsatisfactory predictive performance. Recent advances in deep learning-based methods provide unprecedented ability to predict patients’ future health status, but they still suffer from the sparsity issue of EHR data.

Radio Frequency Sensing in Healthcare: Detection of Falls, Gait Abnormality and Vital Signs

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In this talk, we represent recent advances of radio frequency (RF) sensing technology for healthcare, specifically in monitoring human activities inside homes, retirement facilities, and hospitals. Sensing technologies and data analytics are considered powerful tools in efficient indoor monitoring of human activities. Monitoring of activities of daily living (ADL) can identify falls, which are considered as the leading cause of fatal and non-fatal injuries for people aged 65 and over. It can also detect variants in activity patterns and changes in routines and lifestyle as well as the state of physical, cognitive, and psychological health of the person. In addition to monitoring ADL, RF-based gesture recognition using hands and arms is shown to be an important contactless technology for Man-Machine-Interface (MMI). Adding to the indoor applications, RF-based vital sign monitoring has vast medical use, as respiration and heartbeats are essential diagnostic barometers for many health problems. More recently, RF sensors have also been proposed for gait analysis for rehabilitation and timely diagnosis of many neurological, orthopedic and medical conditions. Changes in gait patterns can also be precursors of falls. In this talk, we present successful examples in each of the above application areas and discuss pertinent open problems worthy of investigations.

Exploring the Potentials of EM Waves from Body-scale to Nano-communications for Healthcare and Next Generation Wireless Applications

Akram Alomainy, Reader, Antennas and Applied Electromagnetics, Queen Mary University of London

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KAUST

With the advent of commercial products, such as Apple iWatch and Samsung Galaxy Gear, body-centric communication has increasingly garnered the public attention and smoothly translated state-of-the-art research work into reality. However, challenges still remains and these are often fundamental physical hurdles that need to be further explored and investigated to come up with efficient and scalable solutions applicable to many fields and areas. This becomes an important research area when you look at the scale or rather the multiple scales it needs to work at from body-size or larger networks to the nano-scale where there have been lots of interest recently on how to get nano-devices inside tissues and even inside intelligent materials around us. The talk will present recent development in the area of antennas, RF devices and electromagnetic solutions for applications such as healthcare, biomedical engineering and next generation wireless communications. It will look at the challenges from theoretical, numerical and experimental prospective to ensure that proposed concepts and outcomes are of benefit not only to those domains but other beyond such as agricultural technology and smart home and cities.

Smart healthcare uses stats to spot a stumble

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Statistical monitoring technology can detect serious falls and immediately warn healthcare providers.

Zooming in on brain function

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Faster computations will allow researchers to see the finer details of brain activity in functional brain imaging.

Solving real-world problems

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A tool developed by Håvard Rue has transformed data analysis, interpretation and communication. It has been applied broadly: from modeling the spread of infectious diseases to mapping fish stocks.

Today we celebrate World Statistics Day!

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Let’s discover the world of the statisticians at KAUST and be ready to be amazed by their cutting-edge research!

Talented KAUST Ph.D. student aims to revolutionize healthcare with machine learning

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KAUST Ph.D. student, Yu Li, is a talented young computer scientist with an interest in developing novel computational methods and algorithms to solve and understand the principles behind the “bio-world.”

First joint publication between KAUST and MBSC seeks to improve treatment options for chronic kidney disease in Saudi Arabia

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Chronic kidney disease (CKD) is among the most common causes of death in Saudi Arabia. According to the 2018 edition of the Institute for Health Metrics and Evaluation’s annual Financing Global Health report, the prevalence of CKD among the Kingdom’s general population stands at 5.44% and accounts for a 4.04% mortality rate.

Safeguarding the right to be forgotten

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An open-source software can help align artificial intelligence applications in healthcare with data privacy regulations.

Sherjeel Khan

Sherjeel Khan did his Bachelors in Mechatronics Engineering from National University of Sciences and ‎Technology (NUST) Islamabad, Pakistan in 2013. After graduation, he worked as a Design Engineer in a ‎Pakistan-US based startup Cowlar©. He then availed his Master’s degree in electrical engineering from ‎Georgia Institute of Technology, USA in 2016 under Fulbright Scholarship. He has been a PhD student with Professor Muhammad Mustafa Hussain at KAUST since 2016. His research work is focused on making low-cost eco-friendly sensing solutions for healthcare based applications.

Sarah Alghamdi

Ph.D. Student, Computer Science
Sarah Alghamdi is a Ph.D. candidate in the Bio-Ontology Research Group (BORG) at King Abdullah University of Science and Technology (KAUST) under the supervision of Professor Robert Hoehndorf. Research Interests Sarah's research interests include applications of artificial intelligence and using statistical methods to genomics and healthcare. Professional Profile Program Committee for the International Conference of Biomedical Ontology (ICBO 2022) Program Committee for the International Society of Molecular Biology (ISMB 2022) - Bio-Ontology track 2018-2021: Teaching Assistant positions: CS

Sahika Inal

Associate Professor, Bioengineering
Professor Sahika Inal's research bridges the fields of organic electronics and biology, focusing on the development of bioelectronic materials for real-time health monitoring and intervention.

Adeeb Noor

Visiting Professor, Bio-Ontology Research Group
Adeeb Noor is an Associate Professor of Computer Science at King Abdulaziz University (KAU), where he is the founder of the King Abdulaziz University Hospital patients portal (SHIFAA). He is also a visiting professor and part of the Bio-Ontology Research Group in KAUST. Research Interests Dr. Noor areas of research include but are not limited to data and business governance, healthcare digital transformation, drug-drug interactions, and personalized medicine. Selected Publications​ Agent-enabled task offloading in UAV-aided mobile edge computing, R Wang, Y Cao, A Noor, TA Alamoudi, R Nour -