PURPOSE: To analyze the influence of individual parameters on the postoperative refractive outcomes of small incision lenticule extraction (SMILE) in myopic eyes using machine learning.
Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical state. For emerging digital phenotyping approaches, there is a need for individualized model estimati...
The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptoge...
Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from...
In the current study, we investigated the utility of generative AI for survey development and improvement. To do so, we generated a 24-item HEXACO personality inventory using ChatGPT 4.0, the ChatGPT HEXACO inventory (CHI), and investigated whether C...
: Venous thromboembolism (VTE) can be the first manifestation of an underlying cancer. This study aimed to develop a predictive model to assess the risk of occult cancer between 30 days and 24 months after a venous thrombotic event using machine lear...
We investigate the application of deep learning in comparing gait cycle time series from two groups of healthy children, each assessed in different gait laboratories. Both laboratories used similar gait analysis protocols with minimal differences in ...
International journal of molecular sciences
Dec 27, 2024
Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughp...
BACKGROUND: Private-part skin diseases (PPSDs) can cause a patient's stigma, which may hinder the early diagnosis of these diseases. Artificial intelligence (AI) is an effective tool to improve the early diagnosis of PPSDs, especially in preventing t...
. Accurate seizure prediction could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. While deep learning-based approaches have shown promising performance using scalp electroencephalogram (EEG) signals, the ...
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