AI Medical Compendium Topic

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Leveraging interpretable machine learning algorithms to predict postoperative patient outcomes on mobile devices.

Surgery
Setting patient and family expectations for postoperative outcomes is an important aspect of care, a cornerstone of which is accurate, personalized, and explainable risk estimation. Modern machine learning offers a plethora of models that can effecti...

KecNet: A Light Neural Network for Arrhythmia Classification Based on Knowledge Reinforcement.

Journal of healthcare engineering
Acquiring electrocardiographic (ECG) signals and performing arrhythmia classification in mobile device scenarios have the advantages of short response time, almost no network bandwidth consumption, and human resource savings. In recent years, deep ne...

HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning.

Journal of medical Internet research
The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's health will evolve has been called digital phenotyping. In this paper, we describe the development of and...

An Overview of Machine Learning within Embedded and Mobile Devices-Optimizations and Applications.

Sensors (Basel, Switzerland)
Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include ...

Comparison of Acceptance and Knowledge Transfer in Patient Information Before an MRI Exam Administered by Humanoid Robot Versus a Tablet Computer: A Randomized Controlled Study.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
PURPOSE:  To investigate whether a humanoid robot in a clinical radiological setting is accepted as a source of information in conversations before MRI examinations of patients. In addition, the usability and the information transfer were compared wi...

An Unsupervised Learning-Based Spatial Co-Location Detection System from Low-Power Consumption Sensor.

Sensors (Basel, Switzerland)
Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused...

Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices.

Sensors (Basel, Switzerland)
Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accur...

Comparison of machine learning techniques for the identification of human activities from inertial sensors available in a mobile device after the application of data imputation techniques.

Computers in biology and medicine
Human activity recognition (HAR) is a significant research area due to its wide range of applications in intelligent health systems, security, and entertainment games. Over the past few years, many studies have recognized human daily living activitie...

UJAmI Location: A Fuzzy Indoor Location System for the Elderly.

International journal of environmental research and public health
Due to the large number of elderly people with physical and cognitive issues, there is a strong need to provide indoor location systems that help caregivers monitor as many people as possible and with the best quality possible. In this paper, a fuzzy...

GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices Based on Fine-Grained Structured Weight Sparsity.

IEEE transactions on pattern analysis and machine intelligence
It is appealing but challenging to achieve real-time deep neural network (DNN) inference on mobile devices, because even the powerful modern mobile devices are considered as "resource-constrained" when executing large-scale DNNs. It necessitates the ...