AIMC Topic: Machine Learning

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Development and application of an AI-empowered acoustic monitoring system for misuse detection in dry powder inhalers.

International journal of pharmaceutics
Misuse and poor inhalation techniques remain persistent issues in pulmonary drug delivery via dry powder inhalation. While acoustic-based monitoring has been a feasible strategy, existing approaches often depend on smartphones for signal collection, ...

Integrating machine learning and metabolomics to uncover new biomarkers for predicting pesticide exposure among patients with kidney function decline.

The Science of the total environment
Although pesticide application is indispensable for agricultural productivity, improper use can pose significant health risks, particularly for vulnerable populations. This study investigated the effects of pesticide exposure on metabolic pathways an...

A computational pipeline for predicting distal hotspots in an artificial enzyme.

International journal of biological macromolecules
Targeting distal mutations holds promising implications for enzyme engineering. Here, we present an open-source computational workflow designed to explore the functional impact of distal sites, demonstrated on an artificial enzyme built on the widely...

Can GPT-4 provide human-level emotion support? Insights from machine learning-based evaluation framework.

Computers in biology and medicine
The global shortage of mental health services has sparked considerable interest in leveraging generative artificial intelligence (AI) to address psychological health challenges. This study systematically evaluates the emotional support capabilities o...

A practical approach to predicting long-term outcomes in traumatic brain injury: Enhancing clinical decision-making with machine learning.

Computers in biology and medicine
BACKGROUND: Traumatic brain injury (TBI) is among the most prevalent causes of emergency department visits globally. TBI leads to high morbidity and mortality rates, which poses a noteworthy burden on the medical system regarding both patients and ec...

Multilevel Hydrangea-like Heterogeneous Oxides Enabling COVID-19 Progression Surveillance via Metabolic Fingerprints.

Analytical chemistry
Coronavirus disease 2019 (COVID-19), a global pandemic infectious disease, requires early diagnosis and dynamic monitoring to enable timely intervention and reduce the risks of adverse outcomes. To support these needs, we developed an advanced metabo...

Unveiling the Role of Wetland Strategies in Antibiotic Risk Reduction across China by Machine Learning.

Environmental science & technology
Pervasive antibiotic pollution in water environments has emerged as a serious threat to global ecosystem functions and public health. While wetland expansion─including protection, restoration, and construction, is widely promoted for sustainable wate...

Untangling the Postmortem Metabolome: A Machine Learning Approach for Accurate PMI Estimation.

Analytical chemistry
Accurate estimation of the postmortem interval (PMI) is crucial for medico-legal investigations, providing critical timelines for criminal cases. Current PMI methods, however, often lack precision, limiting their forensic utility. In this study, we d...

Learning the sequence code of protein expression in human immune cells.

Science advances
Accurate protein expression in human immune cells is essential for appropriate cellular function. The mechanisms that define protein abundance are complex and are executed on transcriptional, posttranscriptional, and posttranslational levels. Here, w...

Development and validation of risk prediction models for acute kidney disease in gout patients: a retrospective study using machine learning.

European journal of medical research
BACKGROUND: Limited research has been conducted on the prevalence of acute kidney injury (AKI) and acute kidney disease (AKD) in gout patients, as well as the impact of these renal complications on patient outcomes. This study aims to develop machine...