AIMC Topic: Machine Learning

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Searching for peripheral proteomic markers of primary aldosteronism.

Endocrine
PURPOSE: Primary aldosteronism (PA) is prevalent among hypertensive patients, and associated with worsened cardiovascular outcomes compared to essential hypertension (HT). Screening and diagnostics for PA are currently complicated and invasive, why n...

Assessment of outcomes and machine Learning-based models to predict local failure risk following stereotactic radiosurgery for small brain metastases.

Journal of neuro-oncology
INTRODUCTION: We assessed the outcomes of stereotactic radiosurgery (SRS) for small intact brain metastases (SBM) (≤ 2 cm) and developed machine learning (ML) algorithms to predict the probability of local failure (LF).

Optimizing Dementia Diagnosis Through Distance-Correlation Feature Space and Dimensionality Reduction.

International journal of neural systems
The reduction of dimensionality in machine learning and artificial intelligence problems constitutes a pivotal element in the simplification of models, significantly enhancing both their performance and execution time. This process enables the genera...

Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone.

Neurobiology of disease
BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains abundant epileptogenic information, which aids in exploring the mechanism of seizures and localizing the epileptogenic zone (EZ). This study aims to i...

Combining structural equation modeling analysis with machine learning for early malignancy detection in Bethesda Category III thyroid nodules.

Artificial intelligence in medicine
Atypia of Undetermined Significance (AUS), classified as Category III in the Bethesda Thyroid Cytopathology Reporting System, presents significant diagnostic challenges for clinicians. This study aims to develop a clinical decision support system tha...

Interpretable machine learning models based on body composition and inflammatory nutritional index (BCINI) to predict early postoperative recurrence of colorectal cancer: Multi-center study.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) ranks among the most prevalent cancers worldwide, with early postoperative recurrence remaining a major cause of mortality. Body composition and inflammatory-nutritional indices (BCINI) have demonstra...

Time series data analysis to predict the status of mastitis in dairy cows by applying machine learning models to automated milking systems data.

Preventive veterinary medicine
Mastitis in dairy cows is one of the most important issues that not only pose risk to animal health and welfare but also cause huge direct and indirect economic losses to the dairy sector. In recent times, automated milking systems (AMS) have gained ...

Classifying fungi biodiversity using hybrid transformer models.

Journal of microbiological methods
Fungi are essential members of ecosystems, playing key roles in nutrient cycling, agriculture, and medicine. Their classification into proper species helps us to understand their biodiversity, allowing us to leverage their ecological and practical be...

Classification of epilepsy seizure types in pediatrics based on Turkish EEG reports.

Epilepsy research
This study focuses on the binary classification of pediatric epilepsy seizure types as focal or generalized using Turkish electroencephalography (EEG) reports, leveraging natural language processing (NLP) and machine learning methodologies. A novel d...