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Machine learning-based diagnostic model for stroke in non-neurological intensive care unit patients with acute neurological manifestations.

Scientific reports
Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to increased mortality and prolonged hospital stays. The incidence of stroke in ICU settings is notabl...

Improving the closing sequences of interaction between human and robot through conversation analysis.

Scientific reports
This study employs Conversation Analysis to create a recursive model that improves the quality of human-robot interaction. Our research goal is to create a dialogue robot that offers pleasant experiences for users, so they are willing to engage in re...

Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach.

Scientific reports
Osteochondral interface consists of two tissues: the calcified cartilage (CC) containing chondrocytes, and subchondral bone (SCB) containing osteocytes that interact with each other. In this study, we propose a new method for the three-dimensional (3...

Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques.

Scientific reports
Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge due to the lack of widespread screening among...

Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.

Journal of medical Internet research
BACKGROUND: Accurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are...

Public Perception on Artificial Intelligence-Driven Mental Health Interventions: Survey Research.

JMIR formative research
BACKGROUND: Artificial intelligence (AI) has become increasingly important in health care, generating both curiosity and concern. With a doctor-patient ratio of 1:834 in India, AI has the potential to alleviate a significant health care burden. Publi...

A deep learning approach for automated scoring of the Rey-Osterrieth complex figure.

eLife
Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visu...

Multi-omics characterization and machine learning of lung adenocarcinoma molecular subtypes to guide precise chemotherapy and immunotherapy.

Frontiers in immunology
BACKGROUND: Lung adenocarcinoma (LUAD) is a heterogeneous tumor characterized by diverse genetic and molecular alterations. Developing a multi-omics-based classification system for LUAD is urgently needed to advance biological understanding.

Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework.

Frontiers in immunology
BACKGROUND: Sepsis is a life-threatening condition that causes millions of deaths globally each year. The need for biomarkers to predict the progression of sepsis to septic shock remains critical, with rapid, reliable methods still lacking. Transcrip...

Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.

Frontiers in immunology
BACKGROUND: Breast cancer prognosis remains a significant challenge due to the disease's molecular heterogeneity and complexity. Accurate predictive models are critical for improving patient outcomes and tailoring personalized therapies.