AIMC Topic: Machine Learning

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Comparison between clinician and machine learning prediction in a randomized controlled trial for nonsuicidal self-injury.

BMC psychiatry
BACKGROUND: Nonsuicidal self-injury is a common health problem in adolescents and associated with future suicidal behavior. Predicting who will benefit from treatment is an urgent and a critical first step towards personalized treatment approaches. M...

Accuracy of symptom checker for the diagnosis of sexually transmitted infections using machine learning and Bayesian network algorithms.

BMC infectious diseases
BACKGROUND: A significant proportion of individuals with symptoms of sexually transmitted infection (STI) delay or avoid seeking healthcare, and digital diagnostic tools may prompt them to seek healthcare earlier. Unfortunately, none of the currently...

Predicting maternal risk level using machine learning models.

BMC pregnancy and childbirth
BACKGROUND: Maternal morbidity and mortality remain critical health concerns globally. As a result, reducing the maternal mortality ratio (MMR) is part of goal 3 in the global sustainable development goals (SDGs), and previously, it was an important ...

Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme.

Scientific data
Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and ...

Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images.

Scientific reports
Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processes in the healthcare industry. Earlier diagnosis and localization of BT provide timely options to select effective treatment plans for the doctors and ...

Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements.

Open heart
BACKGROUND: Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.

An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein.

eLife
The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defi...

Machine learning-assisted pattern recognition and imaging of multiplexed cancer cells a porphyrin-embedded dendrimer array.

Journal of materials chemistry. B
Early cancer detection plays a vital role in improving the survival rate of cancer patients, underscoring the importance of developing cancer detection methods. However, it is a great challenge to achieve simple, rapid, and accurate methods for simul...

Profiling the AI speaker user: Machine learning insights into consumer adoption patterns.

PloS one
The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting effective advertising and marketing strategies in the fast-evolving media technology landscape. To do s...

Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning.

PloS one
Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. VN are deployed to optimize traffic flow, enhance the driving experience, and ensure ...