Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.

Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and ef fi cient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12 – 18 clinical variables were used for model training. Logistic regression, random forest classi fi er, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce over fi tting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision- recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust arti fi cial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.

Kang N, et al. Allergy Asthma Immunol Res. 2024 Jan;16(1):42-54. https://doi.org/10.4168/aair.2024.16.1.42

Objective: The heterogeneous nature of sepsis renders determining its underlying causes difficult, which may delay diagnosis and intervention. VitalCare-SEPsis Score (VC-SEPS) is a deep learning-based algorithm that predicts sepsis and monitors patient conditions based on electronic medical record data. However, few studies have prospectively compared medical artificial intelligence software algorithms and traditional scoring systems to predict sepsis. This prospective observational study attempted to validate the predictive performance and risk stratification of VC-SEPS for early prediction of sepsis. Methods: In this prospective observational study, we collected electronic medical record data from 6,797 patients hospitalised at Keimyung University Dongsan Hospital, Daegu, South Korea. The final version of the analysed set included 6,455 patients, 325 of whom were diagnosed with sepsis. Results: The area under the receiver operating characteristic curve of VC-SEPS was 0.880, indicating its superiority over traditional scoring systems. The algorithm performance showed a consistent trend within 24 hours. On patients’ initial admission, the VC-SEPS was associated with the risk of developing sepsis, and the score accurately predicted sepsis by an average of 68.05 min compared with diagnosis time by an operational definition of sepsis. Discussion: VC-SEPS could assist medical staff with early diagnosis and intervention in clinical practice by providing a sepsis risk score. Prompt recognition assisting recognition can significantly help shorten the time between recognition and intervention in clinical decision-making processes. Conclusion: This study suggests that using a clinical decision support system can help improve hospital workflows as well as the quality of medical care.

Autonomic cardiac regulation is affected by advancing age and can be observed by variations in R-peak to R-peak intervals (RRIs). Heart rate variability (HRV) has been investigated as a physiological marker for predicting age using machine learning. However, deep learning-based age prediction has rarely been performed using RRI data. In this study, age prediction was demonstrated in a healthy population based on RRIs using deep learning. The RRI data were extracted from 1093 healthy subjects and applied to a modified ResNet model to classify four age groups. The HRV features were evaluated using this RRI dataset to establish an HRV-based prediction model as a benchmark. In addition, an age prediction model was developed that combines RRI and HRV data. The adaptive synthetic algorithm was used because of class imbalance and a hybrid loss function that combined classification loss and mean squared error functions was implemented. Comparisons suggest that the RRI model can perform similarly to the HRV and combined models, demonstrating the potential of the RRI-based deep learning model for automated age prediction. However, these models showed limited efficacy in predicting all age groups, indicating the need for significant improvement before they can be considered reliable age prediction methods.