AIMC Topic: Machine Learning

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Machine Learning for Prediction of Drug Concentrations: Application and Challenges.

Clinical pharmacology and therapeutics
With the advancements in algorithms and increased accessibility of multi-source data, machine learning in pharmacokinetics is gaining interest. This review summarizes studies on machine learning-based pharmacokinetics analysis up to September 2024, i...

Integrated machine learning-based optimization framework for surface water quality index comparing coastal and non-coastal cases of Guangxi, China.

Marine pollution bulletin
In this study, an optimized comprehensive water quality index (WQI) model framework is developed, which combines advanced machine learning technology to compare different types of surface water quality assessment. The proposed framework enhancement e...

Machine Learning-Enabled Drug-Induced Toxicity Prediction.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of drug discovery failures. Traditional toxicity assessment through animal testing is costly and time-consuming. Big data and artificial intellige...

Bioinspired Super-Robust Conductive Hydrogels for Machine Learning-Assisted Tactile Perception System.

Advanced materials (Deerfield Beach, Fla.)
Conductive hydrogels have attracted significant attention due to exceptional flexibility, electrochemical property, and biocompatibility. However, the low mechanical strength can compromise their stability under high stress, making the material susce...

HiRXN: Hierarchical Attention-Based Representation Learning for Chemical Reaction.

Journal of chemical information and modeling
In recent years, natural language processing (NLP) techniques, including large language modeling (LLM), have contributed significantly to advancements in organic chemistry research. Chemical reaction representations provide a link between NLP models ...

Federated Learning for IoMT-Enhanced Human Activity Recognition with Hybrid LSTM-GRU Networks.

Sensors (Basel, Switzerland)
The proliferation of wearable sensors and mobile devices has fueled advancements in human activity recognition (HAR), with growing importance placed on both accuracy and privacy preservation. In this paper, the author proposes a federated learning fr...

Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

JAMA network open
IMPORTANCE: Decreasing mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children...

Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample.

Reproductive health
BACKGROUND: Psychologically traumatic childbirth leads to short and long-term negative impacts on a woman's health and impacts future reproductive decisions. Considering the importance of fertility growth and strengthening positive fertility motivati...

Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning.

BMC medical informatics and decision making
BACKGROUND: Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD incidence and mortality. As both conditions rise, early identification and intervention in risk populations...

Developing clinical prognostic models to predict graft survival after renal transplantation: comparison of statistical and machine learning models.

BMC medical informatics and decision making
INTRODUCTION: Renal transplantation is a critical treatment for end-stage renal disease, but graft failure remains a significant concern. Accurate prediction of graft survival is crucial to identify high-risk patients. This study aimed to develop pro...