Machine learning-based prediction of coronary care unit readmission: A multihospital validation study.

Objective: Readmission to the coronary care unit (CCU) has significant implications for patient outcomes and healthcare expenditure, emphasizing the urgency to accurately identify patients at high readmission risk. This study aims to construct and externally validate a predictive model for CCU readmission using machine learning (ML) algorithms across multiple hospitals. Methods: Patient information, including demographics, medical history, and laboratory test results were collected from electronic health record system and contributed to a total of 40 features. Five ML models: logistic regression, random forest, support vector machine, gradient boosting, and multilayer perceptron were employed to estimate the readmission risk. Results: The gradient boosting model was selected demonstrated superior performance with an area under the receiver operating characteristic curve (AUC) of 0.887 in the internal validation set. Further external validation in hold-out test set and three other medical centers upheld the model's robustness with consistent high AUCs, ranging from 0.852 to 0.879. Conclusion: The results endorse the integration of ML algorithms in healthcare to enhance patient risk stratification, potentially optimizing clinical interventions, and diminishing the burden of CCU readmissions.

Use of a Deep-Learning Algorithm to Guide Novices in Performing Focused Assessment With Sonography in Trauma.

This quality improvement study compares the diagnostic quality and completion time between ultrasonography operators guided by artificial intelligence vs those without such assistance.

Using Deep Transfer Learning to Detect Hyperkalemia From Ambulatory Electrocardiogram Monitors in Intensive Care Units: Personalized Medicine Approach.

BACKGROUND: Hyperkalemia is a critical condition, especially in intensive care units. So far, there have been no accurate and noninvasive methods for recognizing hyperkalemia events on ambulatory electrocardiogram monitors. OBJECTIVE: This study aimed to improve the accuracy of hyperkalemia predictions from ambulatory electrocardiogram (ECG) monitors using a personalized transfer learning method; this would be done by training a generic model and refining it with personal data. METHODS: This retrospective cohort study used open source data from the Waveform Database Matched Subset of the Medical Information Mart From Intensive Care III (MIMIC-III). We included patients with multiple serum potassium test results and matched ECG data from the MIMIC-III database. A 1D convolutional neural network-based deep learning model was first developed to predict hyperkalemia in a generic population. Once the model achieved a state-of-the-art performance, it was used in an active transfer learning process to perform patient-adaptive heartbeat classification tasks. RESULTS: The results show that by acquiring data from each new patient, the personalized model can improve the accuracy of hyperkalemia detection significantly, from an average of 0.604 (SD 0.211) to 0.980 (SD 0.078), when compared with the generic model. Moreover, the area under the receiver operating characteristic curve level improved from 0.729 (SD 0.240) to 0.945 (SD 0.094). CONCLUSIONS: By using the deep transfer learning method, we were able to build a clinical standard model for hyperkalemia detection using ambulatory ECG monitors. These findings could potentially be extended to applications that continuously monitor one's ECGs for early alerts of hyperkalemia and help avoid unnecessary blood tests.

Correction: Yau et al. Risk Factors for Early Return Visits to the Emergency Department in Patients Presenting with Nonspecific Abdominal Pain and the Use of Computed Tomography Scan. Healthcare 2021, 9, 1470.

The authors would like to make corrections to their published paper [...].

Risk Factors for Early Return Visits to the Emergency Department in Patients Presenting with Nonspecific Abdominal Pain and the Use of Computed Tomography Scan.

Over a quarter of patients presenting with abdominal pain at emergency departments (EDs) are diagnosed with nonspecific abdominal pain (NSAP) at discharge. This study investigated the risk factors associated with return ED visits in Taiwanese patients with NSAP after discharge. We divided patients into two groups: the study group comprising patients with ED revisits after the index ED visit, and the control group comprising patients without revisits. During the study period, 10,341 patients discharged with the impression of NSAP after ED management. A regression analysis found that older age (OR [95%CI]: 1.007 [1.003-1.011], p = 0.004), male sex (OR [95%CI]: 1.307 [1.036-1.650], p = 0.024), and use of NSAIDs (OR [95%CI]: 1.563 [1.219-2.003], p < 0.001) and opioids (OR [95%CI]: 2.213 [1.643-2.930], p < 0.001) during the index visit were associated with increased return ED visits. Computed tomography (CT) scans (OR [95%CI]: 0.605 [0.390-0.937], p = 0.021) were associated with decreased ED returns, especially for those who were older than 60, who had an underlying disease, or who required pain control during the index ED visit.