AIMC Topic: Machine Learning

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Intelligence prediction of microfluidically prepared nanoparticles.

Scientific reports
Developing poly(lactic-co-glycolic) acid (PLGA) nanoparticles with optimized drug encapsulation and loading is crucial for effective drug delivery. However, controlling the physicochemical properties of these nanoparticles remains challenging. In thi...

Using multiple machine learning algorithms to predict spinal cord injury in patients with cervical spondylosis: a multicenter study.

Scientific reports
Degenerative cervical spondylosis, a chronic and progressive condition, has a considerable impact on global health. Spinal cord injury, a severe sequela of this disease, can result from this disease. Machine learning (ML) has emerged as a valuable to...

Enhancing Urdu hate speech detection through differential transfer learning and adaptive loss functions.

Scientific reports
Hate speech detection is a challenging task due to complexities such as language ambiguity, limited context, cultural nuances, and situational factors. This challenge is further amplified in low-resource languages, i.e. Urdu. Most research on hate sp...

Machine learning applied to global scale species distribution models.

Scientific reports
Species Distribution Models (SDMs) are widely used in ecology to analyze historical and future patterns of marine species distributions. Given the growing impact of climate change, predicting potential shifts in species ranges has become a key challe...

The diagnostic potential of proteomics and machine learning in Lyme neuroborreliosis.

Nature communications
Lyme neuroborreliosis (LNB), a nervous system infection caused by tick-borne spirochetes of the Borrelia burgdorferi sensu lato complex, is among the most frequent bacterial infections of the nervous system in Europe. Early diagnosis and continuous m...

Artificial Intelligence in Ambulatory Surgery: Current Applications, Challenges, and Future Directions.

Journal of medical systems
Ambulatory surgery enhances resource utilization through reduced hospital stays and costs without compromising clinical outcomes. However, existing workflows are labor-intensive and repetitive, necessitating optimization in patient selection, assessm...

K-operator for Modelling Neurodegeneration: Simulations, fMRI Application, Eigenvalue Analysis and Recurrence Plots.

Journal of medical systems
The brain network damage provoked by a neurological disease can be modelled as the result of the action of an operator, K, acting on the brain, inspired by physics. Here, we explore the matrix formulation of K, analysing eigenvalues and eigenvectors,...

Evaluation of postoperative bleeding risk after dental extractions in patients on antithrombotic medication: A comparison of machine learning and clinical experience.

Clinical oral investigations
OBJECTIVES: The aim of this study was to identify high-risk dental extractions in patients taking antiplatelet (AP) medication or anticoagulants (ACs) and to compare an experienced surgeon's decisions with machine learning (ML) algorithms.

Detection of cognitive load using EEG signal and lifting wavelet transform with specific lead selection.

Biomedical physics & engineering express
Solving an arithmetic task is a complex assignment that includes sequencing, memory, fact retrieval, and decision making. Observation of the human brain's response to such activities is quite essential as it helps in the diagnosis of various diseases...

Neglected brucellosis in pediatric populations from non-endemic regions: Clinical manifestations and prediction of severe disease in Yunnan Province, China.

PLoS neglected tropical diseases
BACKGROUND: Although Yunnan Province is not an endemic region for brucellosis, the disease remains a diagnostic and therapeutic challenge in children due to its atypical clinical manifestations and potential for severe complications.