AIMC Topic: Machine Learning

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A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach.

Computers in biology and medicine
BACKGROUND: Changes in voice are a symptom of Parkinson's disease and used to assess the progression of the condition. However, natural differences in the voices of people can make this challenging. Computerized binary speech classification can ident...

Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review.

Digestive diseases and sciences
BACKGROUND: Major society guidelines recommend transarterial chemoembolization (TACE) as the standard of care for intermediate-stage hepatocellular carcinoma (HCC) patients. However, predicting treatment response remains challenging.

Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.

Sensors (Basel, Switzerland)
Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to...

Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.

International journal of molecular sciences
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the large coronavirus family with high infectivity and pathogenicity and is the primary pathogen causing the global pandemic of coronavirus disease 2019 (COVID-19). Phosphory...

Predicting preterm birth using electronic medical records from multiple prenatal visits.

BMC pregnancy and childbirth
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation ...

Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis.

BMC infectious diseases
BACKGROUND: Predicting mortality in sepsis-related acute kidney injury facilitates early data-driven treatment decisions. Machine learning is predicting mortality in S-AKI in a growing number of studies. Therefore, we conducted this systematic review...

Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning.

BMC pregnancy and childbirth
PURPOSE: This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.

Using clinical data to reclassify ESUS patients to large artery atherosclerotic or cardioembolic stroke mechanisms.

Journal of neurology
PURPOSE: Embolic stroke of unidentified source (ESUS) represents 10-25% of all ischemic strokes. Our goal was to determine whether ESUS could be reclassified to cardioembolic (CE) or large-artery atherosclerosis (LAA) with machine learning (ML) using...

Evaluation of Pregnancy Risks in Women with Subchorionic Hematoma Using Machine Learning Models.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Subchorionic hematoma (SCH) can lead to blood accumulation and potentially affect pregnancy outcomes. Despite being a relatively common finding in early pregnancy, the effects of SCH on pregnancy outcomes such as miscarriage, stillbirth, a...

NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes.

Cell biology and toxicology
BACKGROUND: Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to morbidity and mortality. Recent studies suggest a pathological link between PE and ferroptosis. We a...