AIMC Topic: Machine Learning

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Activity recognition in patients with tremor: Integrating time-length windows for enhanced detection.

Computers in biology and medicine
Pathological tremor significantly impairs daily activities and quality of life, particularly in conditions such as essential tremor and Parkinson's disease. The assessment and evaluation of tremor, along with its evolution with medication dosages, po...

Distinct processing stages of cross-modal conflict in schizophrenia: The role of auditory cortex underactivation.

Schizophrenia research
BACKGROUND: The cross-modal conflict deficit is a key feature of schizophrenia. However, it remains largely unknown whether cross-modal conflict in schizophrenia diverges at distinct processing stages and its potential association with the auditory c...

Improving human brucellosis susceptibility mapping using effective and simultaneously metaheuristic-based feature selection and hyperparameter tuning.

Acta tropica
Human Brucellosis, a neglected zoonotic disease, affects 1.6 to 2.1 million people globally each year. In Iran, it has become a significant health concern, with an average annual incidence rate of 19.91 cases per 100,000 people. This study aims to cr...

Critical intelligence: Computing defensive behaviour.

Neuroscience and biobehavioral reviews
Characterising the mechanisms underlying naturalistic defensive behavior remains a significant challenge. While substantial progress has been made in unravelling the neural basis of tightly constrained behaviors, a critical gap persists in our compre...

AttriMIL: Revisiting attention-based multiple instance learning for whole-slide pathological image classification from a perspective of instance attributes.

Medical image analysis
Multiple instance learning (MIL) is a powerful approach for whole-slide pathological image (WSI) analysis, particularly suited for processing gigapixel-resolution images with slide-level labels. Recent attention-based MIL architectures have significa...

Computational modeling of breast tissue mechanics and machine learning in cancer diagnostics: enhancing precision in risk prediction and therapeutic strategies.

Expert review of anticancer therapy
INTRODUCTION: Breast cancer remains a significant global health issue. Despite advances in detection and treatment, its complexity is driven by genetic, environmental, and structural factors. Computational methods like Finite Element Modeling (FEM) h...

REPAIR: Reciprocal assistance imputation-representation learning for glioma diagnosis with incomplete MRI sequences.

Medical image analysis
The absence of MRI sequences is a common occurrence in clinical practice, posing a significant challenge for prediction modeling of non-invasive diagnosis of glioma (GM) via fusion of multi-sequence MRI. To address this issue, we propose a novel unif...

Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors.

Breast cancer research and treatment
PURPOSE: Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist treatment strategies. The aim of this study was to explore the application...

Machine Learning-Based Prediction to Support ICU Admission Decision Making among Very Old Patients with Respiratory Infections: A Proof of Concept on a Nationwide Population-Based Cohort Study.

Medical decision making : an international journal of the Society for Medical Decision Making
BackgroundIntensive care unit (ICU) hospitalizations of very old patients with acute respiratory infection have risen. The decision-making process for ICU admission is multifaceted, and the prediction of long-term survival outcome is an important com...

Machine learning applications related to suicide in military and Veterans: A scoping literature review.

Journal of biomedical informatics
OBJECTIVE: Suicide remains one of the main preventable causes of death among service members and veterans. Early detection and accurate prediction are essential components of effective suicide prevention strategies. Machine learning techniques have b...