AIMC Topic: Machine Learning

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Comparing human text classification performance and explainability with large language and machine learning models using eye-tracking.

Scientific reports
To understand the alignment between reasonings of humans and artificial intelligence (AI) models, this empirical study compared the human text classification performance and explainability with a traditional machine learning (ML) model and large lang...

Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.

Translational psychiatry
Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to u...

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review.

European journal of hospital pharmacy : science and practice
OBJECTIVES: The emergence of artificial intelligence (AI) is catching the interest of hospital pharmacists. A massive collection of health data is now available to train AI models and hold the promise of disrupting codes and practices. The objective ...

Predicting Depression, Anxiety, and Their Comorbidity among Patients with Breast Cancer in China Using Machine Learning: A Multisite Cross-Sectional Study.

Depression and anxiety
Depression and anxiety are highly prevalent among patients with breast cancer. We tested the capacity of personal resources (psychological resilience, social support, and process of recovery) for predicting depression, anxiety, and comorbid depressio...

High-performing cross-dataset machine learning reveals robust microbiota alteration in secondary apical periodontitis.

Frontiers in cellular and infection microbiology
Multiple research groups have consistently underscored the intricate interplay between the microbiome and apical periodontitis. However, the presence of variability in experimental design and quantitative assessment have added a layer of complexity, ...

Impact of economic indicators on rice production: A machine learning approach in Sri Lanka.

PloS one
Rice is a crucial crop in Sri Lanka, influencing both its agricultural and economic landscapes. This study delves into the complex interplay between economic indicators and rice production, aiming to uncover correlations and build prediction models u...

A hybrid human recognition framework using machine learning and deep neural networks.

PloS one
Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus pandemic (COVID-19) significantly affected how we process faces. To prevent viral spread, many gover...

A machine learning approach for differentiating bipolar disorder type II and borderline personality disorder using electroencephalography and cognitive abnormalities.

PloS one
This study addresses the challenge of differentiating between bipolar disorder II (BD II) and borderline personality disorder (BPD), which is complicated by overlapping symptoms. To overcome this, a multimodal machine learning approach was employed, ...

The role of artificial intelligence and data science in nanoparticles development: a review.

Nanomedicine (London, England)
Artificial intelligence has revolutionized many sectors with unparalleled predictive capabilities supported by machine learning (ML). So far, this tool has not been able to provide the same level of development in pharmaceutical nanotechnology. This ...

Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics.

Computer methods in biomechanics and biomedical engineering
Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using info...