Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy

“…To identify an optimal detection method for our protocol we evaluated the performance of four different algorithms in detecting the motor preparation phase: (1) Thresholding, where a trial-specific threshold value was set in real-time using the mean of the Cz channel’s Event-Related Potentials (ERP) during the rest period of each trial; (2) Average PN, in which the mean timing of PN was determined during the calibration trials and then implemented during training ( Mrachacz-Kersting et al, 2019 ) (not real-time); and two deep learning models both enabling real-time detection of brain activity; (3) a Multi-layer perceptron neural network (MLP-NN) ( Behboodi et al, 2022 ), and (4) a Long-short term memory (LSTM) neural network. Machine Learning (ML) is a type of Artificial Intelligence (AI) that enables the analysis and synthesis of very large datasets, while Deep Learning (DL), a ML methodology, is a type of recurrent neural network especially proficient at extracting meaningful patterns from datasets and which is increasingly being utilized in healthcare settings ( Aminizadeh et al, 2024 ). The proposed benefit of LSTM is to appreciably reduce calibration time when used repeatedly across sessions in an individual participant by utilizing and learning from their previous data over time, a feature which could be particularly advantageous when working with a pediatric population.…”

“…(3) a Multi-layer perceptron neural network (MLP-NN) (Behboodi et al, 2022), and (4) a Long-short term memory (LSTM) neural network. Machine Learning (ML) is a type of Artificial Intelligence (AI) that enables the analysis and synthesis of very large datasets, while Deep Learning (DL), a ML methodology, is a type of recurrent neural network especially proficient at extracting meaningful patterns from datasets and which is increasingly being utilized in healthcare settings (Aminizadeh et al, 2024). The proposed benefit of LSTM is to appreciably reduce calibration time when used repeatedly across sessions in an individual participant by utilizing and learning from their previous data over time, a feature which could be particularly advantageous when working with a pediatric population.…”

Which factors are associated with adverse prognosis in multiple myeloma patients after surgery? - preliminary establishment and validation of the nomogram

“…Utzschneider et al suggest that, considering the unique nature of MM, surgical evaluations for MM patients should comprehensively consider surgical risks, patient longevity, and quality of life to accurately assess the actual benefits of surgery for each individual patient [ 11 ]. The nomogram is a data-integrated visualization tool that quantifies predictive models into probability values, making it convenient for physicians to accurately and simply estimate individual risk [ 12 ], which has led to its growing popularity among physicians [ 13 ]. The primary aim of this study is to establish a nomogram based on patients’ preoperative data to identify which types of patients are prone to adverse prognoses (e.g., short survival time, symptom exacerbation, recurrence, or complications), thereby assisting physicians in formulating individualized treatment plans and reducing the likelihood of postoperative adverse events [ 14 ].…”