Background Overcrowding is a serious problem that impacts the ability to provide optimal level of care in a timely manner. High patient volume is known to increase the boarding time at the emergency department (ED), as well as at post-anesthesia care unit (PACU). Furthermore, the same high volume increases inpatient bed transfer times, which causes delays in elective surgeries, increases the probability of near misses, patient safety incidents, and adverse events. Objective The purpose of this study is to develop a Machine Learning (ML) based strategy to predict weekly forecasts of the inpatient bed demand in order to assist the resource planning for the ED and PACU, resulting in a more efficient utilization. Methods The data utilized included all adult inpatient encounters at Geisinger Medical Center (GMC) for the last 5 years. The variables considered were class of inpatient encounter, observation, or surgical overnight recovery (SORU) at the time of their discharge. The ML based strategy is built using the K-means clustering method and the Support Vector Machine Regression technique (K-SVR). Results The performance obtained by the K-SVR strategy in the retrospective cohort amounts to a mean absolute percentage error (MAPE) that ranges between 0.49 and 4.10% based on the test period. Additionally, results present a reduced variability, which translates into more stable forecasting results. Conclusions The results from this study demonstrate the capacity of ML techniques to forecast inpatient bed demand, particularly using K-SVR. It is expected that the implementation of this model in the workflow of bed capacity management will create efficiencies, which will translate in a more reliable, inexpensive and timely care for patients.

Background: Lateral elbow tendinopathy (LET) is prevalent in the upper extremity, with various therapeutic options. Understanding the types and the relations between the radiological tendon features would help to develop more specific treatments. This study reviewed ultrasound exams of LET to investigate the types of degenerative findings and the relationships between them in one of the most prominent sports medicine clinics in Latin America. Methods: A retrospective study was performed. We evaluated 4335 ultrasonographic exams with LET from 2017 and 2018. Five principal degenerative ultrasound criteria with subtypes were selected: hypoechogenicity, neovascularity, calcification, enthesopathy, and intrasubstance tear. A multiple linear regression model was conducted to explore the association between the findings, sex, and age. Results: Overall, 4324 ultrasound exams were analyzed; 2607 (60.29%) were males. Multiple degenerative tendon findings were found in adults (≥18 years) with LET. Hypoechogenicity (67.77%) and neovascularity (37.8%) were the most frequent. The mean length of a tendon tear in both sexes was 4.44 (± 2.81) millimeters. Mild hypoechogenicity (P < .001), and depth intrasubstance tear (P < .01) were statistically significant between them. Severe hypoechogenicity was associated with an increase in all tendon tear dimensions for length 1.37 ([95% Confidence interval (CI), 0.57, 2.17]; P < .001), for width 1.10 ([95% CI, 0.33, 1.87]; P < .01) and for depth 1.64 ([95% CI, 0.40, 2.88]; P < .01). Additional findings associated with an increase in the length dimension were 0.42 associated with focal neovascularity ([95% CI, 0.19, 0.65]; P < .001), and 0.71 associated with multiple neovascularity ([95% CI, 0.27, 1.15]; P < .01). Conclusions: Hypoechogenicity and neovascularity findings presented a positive association with the size of tendon tear in patients with LET. This study reaffirms the increased predominance of tendon tear during the 4th to 6th decades of life.

Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed to establish this power with X-ray chest images of healthy, pneumonia, and COVID-19 infected patients. This design considers data-balancing methods, feature extraction approaches, different algorithms, and hyper-parameter optimization. The ML techniques were evaluated based on classification metrics, including accuracy, the area under the receiver operating characteristic curve (AUROC), F1-score, sensitivity, and specificity. Results: The design of experiment provided the mean and its confidence intervals for the predictive capability of different ML techniques, which reached AUROC values as high as 90% with suitable sensitivity and specificity. Among the learning algorithms, support vector machines and random forest performed best. The down-sampling method for unbalanced data improved the predictive power significantly for the images used in this study. Conclusions: Our investigation demonstrated that ML techniques are able to identify COVID-19 infected patients. The results provided suitable values of sensitivity and specificity, minimizing the false-positive or false-negative rates. The models were trained with significantly low computational resources, which helps to provide access and deployment in rural and impoverished areas.

BackgroundUltrasound (US) is a valuable technique to detect degenerative findings and intrasubstance tears in lateral elbow tendinopathy (LET). Machine learning methods allow supporting this radiological diagnosis.AimTo assess multilabel classification models using machine learning models to detect degenerative findings and intrasubstance tears in US images with LET diagnosis.Materials and methodsA retrospective study was performed. US images and medical records from patients with LET diagnosis from January 1st, 2017, to December 30th, 2018, were selected. Datasets were built for training and testing models. For image analysis, features extraction, texture characteristics, intensity distribution, pixel-pixel co-occurrence patterns, and scales granularity were implemented. Six different supervised learning models were implemented for binary and multilabel classification. All models were trained to classify four tendon findings (hypoechogenicity, neovascularity, enthesopathy, and intrasubstance tear). Accuracy indicators and their confidence intervals (CI) were obtained for all models following a K-fold-repeated-cross-validation method. To measure multilabel prediction, multilabel accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) with 95% CI were used.ResultsA total of 30,007 US images (4,324 exams, 2,917 patients) were included in the analysis. The RF model presented the highest mean values in the area under the curve (AUC), sensitivity, and also specificity by each degenerative finding in the binary classification. The AUC and sensitivity showed the best performance in intrasubstance tear with 0.991 [95% CI, 099, 0.99], and 0.775 [95% CI, 0.77, 0.77], respectively. Instead, specificity showed upper values in hypoechogenicity with 0.821 [95% CI, 0.82, −0.82]. In the multilabel classifier, RF also presented the highest performance. The accuracy was 0.772 [95% CI, 0.771, 0.773], a great macro of 0.948 [95% CI, 0.94, 0.94], and a micro of 0.962 [95% CI, 0.96, 0.96] AUC scores were detected. Diagnostic accuracy, sensitivity, and specificity with 95% CI were calculated.ConclusionMachine learning algorithms based on US images with LET presented high diagnosis accuracy. Mainly the random forest model shows the best performance in binary and multilabel classifiers, particularly for intrasubstance tears.