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.

Background: Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. Objective: This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. Methods: This study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and December 31, 2018. A total of 6914 extubation cases were included. We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used the area under the receiver operating characteristic curve (AUROC). The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were also determined for each model. For performance evaluation, we also used a calibration curve, the Brier score, and the integrated calibration index (ICI) to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve. Results: Among the 6914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was higher during the night shift as compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality were higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.763, and for SVM was 0.740. Conclusions: We successfully developed and validated machine learning–based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the model holds potential for clinical usefulness.

Background: A clinical decision support system (CDSS) is recognized as a technology that enhances clinical efficacy and safety. However, its full potential has not been realized, mainly due to clinical data standards and noninteroperable platforms. Objective: In this paper, we introduce the common data model–based intelligent algorithm network environment (CANE) platform that supports the implementation and deployment of a CDSS. Methods: CDSS reasoning engines, usually represented as R or Python objects, are deployed into the CANE platform and converted into C# objects. When a clinician requests CANE-based decision support in the electronic health record (EHR) system, patients’ information is transformed into Health Level 7 Fast Healthcare Interoperability Resources (FHIR) format and transmitted to the CANE server inside the hospital firewall. Upon receiving the necessary data, the CANE system’s modules perform the following tasks: (1) the preprocessing module converts the FHIRs into the input data required by the specific reasoning engine, (2) the reasoning engine module operates the target algorithms, (3) the integration module communicates with the other institutions’ CANE systems to request and transmit a summary report to aid in decision support, and (4) creates a user interface by integrating the summary report and the results calculated by the reasoning engine. Results: We developed a CANE system such that any algorithm implemented in the system can be directly called through the RESTful application programming interface when it is integrated with an EHR system. Eight algorithms were developed and deployed in the CANE system. Using a knowledge-based algorithm, physicians can screen patients who are prone to sepsis and obtain treatment guides for patients with sepsis with the CANE system. Further, using a nonknowledge-based algorithm, the CANE system supports emergency physicians’ clinical decisions about optimum resource allocation by predicting a patient’s acuity and prognosis during triage. Conclusions: We successfully developed a common data model–based platform that adheres to medical informatics standards and could aid artificial intelligence model deployment using R or Python.

Understanding age-specific injury patterns allows the continued improvement of prevention strategies. This is a retrospective study analyzing the Korea Emergency Department-Based Injury In-depth Surveillance data, including those aged ≤19 years old between January 2011 and December 2017. In this study, we focused on changes in the modes of injury and severity, and prevention potential by dividing the patients into four age groups: group 1 (0–4 years), group 2 (5–9 years), group 3 (10–14 years), and group 4 (15–19 years). The most common mode of injury in younger age groups 1 and 2 was a fall or slip. Most injuries in older age groups 3 and 4 were unintentional and intentional collisions combined. Traumatic brain injuries (2.1%), intensive care unit admissions (1.8%), and overall death (0.4%) were the highest in group 4. The proportions of severe and critical injury (EMR-ISS ≥ 25) were 7.5% in group 4, 3.2% in group 3, 2.5% in group 1, and 1% in group 2. This study presents a comprehensive trend of injuries in the pediatric population in South Korea. Our results suggest the importance of designing specific injury-prevention strategies for targeted groups, circumstances, and situations.

Surveillance of injury patterns and comparisons among different age groups help develop a better understanding of recent injury trends and early prevention. This study conducted a national surveillance of injury by age group. Data were collected retrospectively from Emergency Department-Based Injury In-Depth Surveillance (EDIIS) in South Korea, between January 2011 and December 2017. Patients were divided into the following four groups by age: Group 1–18 to 34 years, Group 2–35 to 49 years, Group 3–50 to 64 years, and Group 4—≥65 years. A total of 1,221,746 patients were included in the study. Findings revealed that, each year, the injury rate increased in the population aged ≥65 years. The place and mechanism of injury in Group 3 were similar to those in younger age groups, while injury outcomes and injured body parts were similar to those in Group 4. Further, hospital admission rate, ICU admission rate, hospital death, traumatic brain injury, and injury severity increased with an increase in age. In our study, each age group showed diverse characteristics pertaining to the mechanism, place, time, and outcomes of injuries. Interestingly, Group 3, which represented the late middle age, exhibited increased vulnerability to injury, and emerged as a gray zone between the young and old age groups. Therefore, different injury prevention methods are needed for each age group. Specifically, early prevention methods need to be implemented from the late middle age to improve the old age group’s injury outcomes.

Background: Liraglutide, a drug used for the management of obesity, has many known side effects. In this study, we developed a predictive model for the occurrence of liraglutide-related side effects using data from electronic medical records (EMRs).Methods: This study included 237 patients from Seoul St. Mary's Hospital and Eunpyeong St. Mary's Hospital who were prescribed liraglutide. An endocrinologist obtained medical data through an EMR chart review. Model performance was evaluated using the mean of the area under the receiver operating characteristic curve (AUROC) with a 95% confidence interval (CI).Results: A predictive model was developed for patients who were prescribed liraglutide. However, 37.1% to 75.5% of many variables were missing, and the AUROC of the developed predictive model was 0.630 (95% CI, 0.551–0.708). Patients who had previously taken antiobesity medication had significantly fewer side effects than those without previous antiobesity medication use (20.7% vs. 41.4%, P<0.003). The risk of side effect occurrence was significantly higher in patients with diabetes than in patients without diabetes by 2.389 times (odds ratio, 2.389; 95% CI, 1.115–5.174).Conclusions: This study did not successfully develop a predictive model for liraglutide-related side effects, primarily due to issues related to missing data. When prescribing antiobesity drugs, detailed records and basic blood tests are expected to be essential. Further large-scale studies on liraglutide-related side effects are needed after obtaining high-quality data.