AIMC Topic: Machine Learning

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Early detection of mental health disorders using machine learning models using behavioral and voice data analysis.

Scientific reports
People of all demographics are impacted by mental illness, which has become a widespread and international health problem. Effective treatment and support for mental illnesses depend on early discovery and precise diagnosis. Notably, delayed diagnosi...

Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization.

Scientific reports
The complexity and variability of biological data has promoted the increased use of machine learning methods to understand processes and predict outcomes. These same features complicate reliable, reproducible, interpretable, and responsible use of su...

Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory.

Nature communications
Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefi...

Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis.

Journal of medical Internet research
BACKGROUND: The opioid epidemic in the United States remains a major public health concern, with opioid-related deaths increasing more than 8-fold since 1999. Chronic pain, affecting 1 in 5 US adults, is a key contributor to opioid use and misuse. Wh...

Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a critical condition commonly encountered in the intensive care unit (ICU), characterized by a high incidence and substantial mortality rate. Early detection and accurate prediction of ARDS ca...

Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.

JMIR mental health
BACKGROUND: Shame and stigma often prevent individuals with social anxiety disorder (SAD) from seeking and attending costly and time-intensive psychotherapies, highlighting the importance of brief, low-cost, and scalable treatments. Creating prescrip...

Predicting health literacy in Brazil: a machine learning approach.

Health promotion international
Health literacy is essential for promoting well-being and the ability to make informed decisions. We investigated the level of health literacy in Brazil and identified the predictive factors that influence it. Our data contribute to the international...

Characteristics of lung sounds in early infants using automated analysis.

European journal of pediatrics
UNLABELLED: A new lung sound analysis software program has been developed. It can automatically select a typical lung sound spectrogram and calculate lung sound parameters using machine learning programs. This study aimed to clarify lung sound charac...

Discovering Ultra-Stable Metal-Organic Frameworks for CO Capture from A Wet Flue Gas: Integrating Machine Learning and Molecular Simulation.

Environmental science & technology
The rapid increase in atmospheric CO, arising from anthropogenic sources, has posed a severe threat to global climate and raised widespread environmental concern. Metal-organic frameworks (MOFs) are promising adsorbents to potentially reduce CO emiss...

Machine Learning-Assisted Molecular Structure Embedding for Accurate Prediction of Emerging Contaminant Removal by Ozonation Oxidation.

Environmental science & technology
Ozone has demonstrated high efficacy in depredating emerging contaminants (ECs) during drinking water treatment. However, traditional quantitative structure-activation relationship (QSAR) models often fall short in effectively normalizing and charact...