AIMC Topic: Machine Learning

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Constructing a predictive model for acute mastitis in lactating women based on machine learning.

Scientific reports
Acute lactational mastitis is a frequently occurring complication for lactating women, exerting a certain degree of influence on their physical condition, breastfeeding, mental health, and daily life. The etiology of this disease is complex, and the ...

Machine learning-optimized dual-band wearable antenna for real-time remote patient monitoring in biomedical IoT systems.

Scientific reports
This work presents a machine learning (ML)-optimized dual-band wearable antenna designed specifically for biomedical applications in healthcare monitoring. Fabricated on a Rogers substrate of 40 × 41 mm, the antenna operates at 2.4 GHz and 5.8 GHz wi...

Machine learning enhanced expert system for detecting heart failure decompensation using patient reported vitals and electronic health records.

Scientific reports
Heart failure (HF) is a condition with periods of stability interrupted by periods of worsening symptoms, known as decompensation episodes. Digital interventions are promising tools to alleviate burdens on HF management through automated alerts at th...

Altered brain structure age gap estimation in major depressive disorder patients with and without anhedonia: a machine learning-based study.

Translational psychiatry
Previous studies have found that major depressive disorder (MDD) may accelerate overall structural brain aging. Nevertheless, it still remains unknown whether anhedonia, a critical negative prognostic indicator in MDD, further leads to advanced brain...

Machine-learning approach to atrial fibrillation prediction among individuals without prior cardiovascular diseases.

Open heart
BACKGROUND: There is a lack of atrial fibrillation (AF) prediction models tailored for individuals without prior cardiovascular diseases (CVDs) to facilitate early intervention. This study aimed to develop and validate an AF prediction model using ma...

Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach.

JMIR formative research
BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic disease characterized by a broad spectrum of involved organs, including neurological, renal, and vascular domains, with disease activity manifesting through unpredictable patterns that vary ...

TomoGRAF: An X-ray physics-driven generative radiance field framework for extremely sparse view CT reconstruction.

PloS one
OBJECTIVES: Computed tomography (CT) provides high spatial-resolution visualization of 3D structures for various applications. Traditional analytical/iterative CT reconstruction algorithms require hundreds of angular samplings, a condition may not be...

Unveiling the hidden burden of COVID-19 in Brazil's obstetric population with severe acute respiratory syndrome: A machine learning model.

PloS one
OBJECTIVE: To predict the actual number of COVID-19 cases in Brazilian pregnant and postpartum women diagnosed with Severe Acute Respiratory Syndrome using a predictive model created based on data from Brazilian database.

Integrative Computational Approaches for TRPV1 Ion Channel Inhibitor Discovery: An Integrated Machine Learning, Drug Repurposing and Molecular Simulation Approach.

Journal of chemical information and modeling
The transient receptor potential vanilloid 1 (TRPV1) ion channel is a key mediator of pain and inflammation, making it a crucial target for developing new analgesics. Despite progress in understanding TRPV1's role, novel modulators that effectively i...

Computationally Designed Nanobinders as Affinity Ligands in Diagnostic and Therapeutic Applications.

Journal of the American Chemical Society
Detecting protein biomarkers is critical in fundamental research and clinical investigations of extracellular vesicles (EVs). Despite the prevalent use of antibodies as recognition elements, their application is often limited by challenges such as cr...