AIMC Topic: Machine Learning

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Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines.

Theranostics
Diagnosis and treatment efficacy of major depressive disorder (MDD) currently lack stable and reliable biomarkers. Previous research has suggested a potential association between immune cells, cytokines, and the pathophysiology and treatment of MDD....

A new fusion neural network model and credit card fraud identification.

PloS one
Credit card fraud identification is an important issue in risk prevention and control for banks and financial institutions. In order to establish an efficient credit card fraud identification model, this article studied the relevant factors that affe...

Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.

Oral diseases
OBJECTIVES: Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes.

Tapping Into Awareness: Assessing Nursing Students' Water Consumption Behaviors and Sustainability Perceptions Through a Cross-Sectional Study With Machine Learning Approach.

Public health nursing (Boston, Mass.)
INTRODUCTION: Investigating water consumption behaviors and perceptions of water sustainability among nursing students is crucial for effective resource management. This study employs machine learning (ML) techniques to analyze these factors in detai...

A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS-Based Microparticles in a Breast Cancer Model.

ACS nano
Multimodal cancer therapies are often required for progressive cancers due to the high persistence and mortality of the disease and the negative systemic side effects of traditional therapeutic methods. Thus, the development of less invasive modaliti...

Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems.

Sensors (Basel, Switzerland)
There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins...

Using machine learning methods to identify trajectories of change and predict responders and non-responders to short-term dynamic therapy.

Psychotherapy research : journal of the Society for Psychotherapy Research
Predicting therapy responders can significantly improve clinical outcomes. This study aims to identify predictors of response to short-term dynamic therapy. Data from 95 patients who underwent 16-session therapy were analyzed using machine learning...

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

Abdominal radiology (New York)
PURPOSE: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.

Machine learning algorithms translate big data into predictive breeding accuracy.

Trends in plant science
Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability...

Machine learning driven bioequivalence risk assessment at an early stage of generic drug development.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V
BACKGROUND: Bioequivalence risk assessment as an extension of quality risk management lacks examples of quantitative approaches to risk assessment at an early stage of generic drug development. The aim of our study was to develop a model-based approa...