AIMC Topic: Machine Learning

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A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment.

BMC medical informatics and decision making
BACKGROUND: The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classifica...

Machine Learning Analysis of Image Data Based on Detailed MR Image Reports for Nasopharyngeal Carcinoma Prognosis.

BioMed research international
We aimed to assess the use of automatic machine learning (AutoML) algorithm based on magnetic resonance (MR) image data to assign prediction scores to patients with nasopharyngeal carcinoma (NPC). We also aimed to develop a 4-group classification sys...

ChronoMID-Cross-modal neural networks for 3-D temporal medical imaging data.

PloS one
ChronoMID-neural networks for temporally-varying, hence Chrono, Medical Imaging Data-makes the novel application of cross-modal convolutional neural networks (X-CNNs) to the medical domain. In this paper, we present multiple approaches for incorporat...

Use of Stratified Cascade Learning to predict hospitalization risk with only socioeconomic factors.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: Published models predicting health related outcomes rely on clinical, claims and social determinants of health (SDH) data. Addressing the challenge of predicting with only SDH we developed a novel framework termed Stratified...

Machine learning helps identifying volume-confounding effects in radiomics.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Highlighting the risk of biases in radiomics-based models will help improve their quality and increase usage as decision support systems in the clinic. In this study we use machine learning-based methods to identify the presence of volume-confounding...

Standing-Posture Recognition in Human-Robot Collaboration Based on Deep Learning and the Dempster-Shafer Evidence Theory.

Sensors (Basel, Switzerland)
During human-robot collaborations (HRC), robot systems must accurately perceive the actions and intentions of humans. The present study proposes the classification of standing postures from standing-pressure images, by which a robot system can predic...

Machine learning improves our knowledge about miRNA functions towards plant abiotic stresses.

Scientific reports
During the last two decades, human has increased his knowledge about the role of miRNAs and their target genes in plant stress response. Biotic and abiotic stresses result in simultaneous tissue-specific up/down-regulation of several miRNAs. In this ...

IoT Based Predictive Maintenance Management of Medical Equipment.

Journal of medical systems
Technological advancements are the main drivers of the healthcare industry as it has a high impact on delivering the best patient care. Recent years witnessed unprecedented growth in the number of medical equipment manufactured to aid high-quality pa...

Separation of HCM and LQT Cardiac Diseases with Machine Learning of Ca2+ Transient Profiles.

Methods of information in medicine
BACKGROUND:  Modeling human cardiac diseases with induced pluripotent stem cells not only enables to study disease pathophysiology and develop therapies but also, as we have previously showed, it can offer a tool for disease diagnostics. We previousl...