AIMC Topic: Humans

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Accurate semi-supervised automatic speech recognition for ordinary and characterized speeches via multi-hypotheses-based curriculum learning.

PloS one
How can we build accurate transcription models for both ordinary speech and characterized speech in a semi-supervised setting? ASR (Automatic Speech Recognition) systems are widely used in various real-world applications, including translation system...

MSDC: Aspect-level sentiment analysis model based on multi-scale dual-channel feature fusion.

PloS one
Aspect-level sentiment analysis is a significant task in the field of natural language processing. It can process text in a fine-grained manner to predict the sentiment polarity of a specific aspect word in a sentence. However, existing single-channe...

Development and validation of a bedside-available machine learning model to predict discrepancies between SaO₂ and SpO₂: Exploring factors related to the discrepancies.

PloS one
In critically ill patients, a discrepancy frequently exists between percutaneous oxygen saturation (SpO₂) and arterial blood oxygen saturation (SaO₂), which can lead to potential hypoxemia being overlooked. The aim of this study was to explore the fa...

Ethical and legal considerations of artificial intelligence applications in psychiatric violence risk assessment: A scoping review protocol.

PloS one
Violence risk assessment is a critical component of psychiatric practice, with significant clinical, ethical, and legal implications. Psychiatric patients at high risk of violence often face interventions including restraints, intramuscular injection...

Multilingual voice-enabled informatics tools: Catalyst for equitable AI in HIV and HIV-comorbidity healthcare management.

PloS one
Human Immunodeficiency Virus (henceforth HIV) is a global health problem, presently with no known cure. Africa has one of the highest incidences of HIV. Nigeria, within the West African (WA) region, is one of the largest economies on the continent. H...

Machine learning detects hidden treatment response patterns only in the presence of comprehensive clinical phenotyping.

PloS one
Inferential statistics traditionally used in clinical trials can miss relationships between clinical phenotypes and treatment responses. We simulated a randomised clinical trial to explore how gradient boosting (XGBoost) machine learning compares wit...

Engineering enhanced signal peptides: A high-throughput computational pipeline for optimizing therapeutic protein production in CHO cells.

New biotechnology
Rational design of signal peptides (SPs), crucial for efficient therapeutic protein secretion in Chinese hamster ovary (CHO) cells, remains challenging due to their context-dependency activity. To overcome this limitation and enable the discovery of ...

Subvisual imaging signals as biomarkers of impending lung metastasis: A multicenter pan-cancer study.

European journal of cancer (Oxford, England : 1990)
STUDY AIM: Early detection of distant metastases is crucial, but current imaging detects them only when radiographically visible. This study reported subvisual chest CT signals could serve as early biomarkers for impending lung metastasis before radi...

Chemically Labeled Exposome Analysis (CLEAN): A Strategy for Nontargeted Identification of Urinary Metabolites.

Environmental science & technology
Urinary exposome analysis faces analytical challenges due to the lack of reference standards for biotransformed products and the wide structural diversity of metabolites. This study developed a chemically labeled exposome analysis (CLEAN) strategy fo...

Machine Learning Modeling for ABC Transporter Efflux and Inhibition: Data Curation, Model Development, and New Compound Interaction Predictions.

Molecular pharmaceutics
In recent years, multiple computational studies have used machine learning models to predict substrate binding and inhibition of ATP-binding cassette (ABC) transporters. However, many of these studies relied on relatively small training sets with lim...