Characteristics and Long-Term Outcome of Cerebellar Strokes in a Single Health Care Facility in Mexico.

Objective The purpose of this study was to analyze and discuss the clinical characteristics, long-term outcome, and prognostic factors of cerebellar strokes treated in a single health care facility in Mexico. Methods We retrospectively reviewed the medical records of adult patients admitted to our hospital with diagnosis of cerebellar ischemic and hemorrhagic stroke between 2018 and 2020. Baseline data included sociodemographic and radiological variables, treatment (surgical versus conservative), and Glasgow Coma Scale on arrival (GCSOA). The final neurological outcome was evaluated with the Glasgow Outcome Scale (GOS) six months after hospital discharge. Results Ten patients (seven male and three female) with a mean age of 57.9 ± 9.3 years were included, six with cerebellar ischemic infarction and four with cerebellar hemorrhage. Out of the 10 patients, four underwent surgery (suboccipital decompressive craniectomy {SDC} ± ventriculostomy). The outcome was favorable in four cases (40%) and unfavorable in six (60%). Patients who underwent surgical treatment fared worse with all four cases associating poor outcome. The comparison between good and poor outcome groups showed significant differences in the presence of obstructive hydrocephalus (one versus six, p = 0.05) and poorer GCSOA (6.16 ± 1.72 versus 12.5 ± 3.6, p = 0.05), associating poorer outcome. Conclusion There is still controversy regarding the appropriate management of cerebellar strokes. The presence of obstructive hydrocephalus and poorer GCSOA are associated to worse outcomes.

Lateral elbow tendinopathy and artificial intelligence: Binary and multilabel findings detection using machine learning algorithms.

Background: Ultrasound (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. Aim: To assess multilabel classification models using machine learning models to detect degenerative findings and intrasubstance tears in US images with LET diagnosis. Materials and methods: A 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. Results: A 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. Conclusion: Machine 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.

Machine learning based forecast for the prediction of inpatient bed demand.

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.