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Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks.

BMC cancer
OBJECTIVE: The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment ...

Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study.

BMC anesthesiology
BACKGROUND: Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain manag...

Using fuzzy decision support to create a positive mental health environment for preschoolers.

Scientific reports
The preschool period is a crucial time for behavioural and social-emotional development and the cultivation of mental well-being. Preschoolers may be affected by various traumatic problems. During this process, preschoolers may develop hazardous beha...

Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning.

Scientific reports
Sepsis is a highly variable condition, and tracking leukocyte patterns may offer insights for tailored treatment and prognosis. We used the MIMIC-IV database to analyze patients diagnosed with Sepsis-3 within 24 h of ICU admission. Latent class mixed...

Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach.

Scientific reports
This study investigated and validated all-cause in-hospital death prediction models for hospitalized pneumonia patients based on large-scale clinical data, including diagnoses, medication prescriptions, and laboratory test codes. Feature selection wa...

Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.

Scientific reports
This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST) paradigms through EEG microstate and functional connectivity (FC) analysis, providing a theoretica...

Influence of AI behavior on human moral decisions, agency, and responsibility.

Scientific reports
There is a growing interest in understanding the effects of human-machine interaction on moral decision-making (Moral-DM) and sense of agency (SoA). Here, we investigated whether the "moral behavior" of an AI may affect both moral-DM and SoA in a mil...

Evaluating the Effectiveness of Large Language Models in Providing Patient Education for Chinese Patients With Ocular Myasthenia Gravis: Mixed Methods Study.

Journal of medical Internet research
BACKGROUND: Ocular myasthenia gravis (OMG) is a neuromuscular disorder primarily affecting the extraocular muscles, leading to ptosis and diplopia. Effective patient education is crucial for disease management; however, in China, limited health care ...

Web-Based Explainable Machine Learning-Based Drug Surveillance for Predicting Sunitinib- and Sorafenib-Associated Thyroid Dysfunction: Model Development and Validation Study.

JMIR formative research
BACKGROUND: Unlike one-snap data collection methods that only identify high-risk patients, machine learning models using time-series data can predict adverse events and aid in the timely management of cancer.

Development of a Mobile Intervention for Procrastination Augmented With a Semigenerative Chatbot for University Students: Pilot Randomized Controlled Trial.

JMIR mHealth and uHealth
BACKGROUND: Procrastination negatively affects university students' academics and mental health. Traditional time management apps lack therapeutic strategies like cognitive behavioral therapy to address procrastination's psychological aspects. Theref...