Endocarditis.

Infectious endocarditis (IE) is a universally fatal condition if left unmanaged, requiring urgent evaluation and treatment. Fever, new heart murmur, vegetations found by echocardiogram, and bacteremia are the most common symptoms and findings. Blood cultures and echocardiography are obligatory diagnostic modalities and should be used with the modified Duke criteria, the accepted diagnostic aid, when establishing a diagnosis of IE. When IE is suspected, consultations with cardiology, infectious disease, and cardiothoracic surgery teams should be made early. Staphylococci, Streptococci, and Enterococci are common pathogens, necessitating bactericidal antimicrobial therapy. Importantly, up to 50% of patients with IE will require cardiothoracic surgical intervention.

Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics.

Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians' attention to intervene. Clinical tools, e.g., the Timed Up and Go (TUG) test, have been created for aiding clinicians in fall-risk assessment. Often simple to evaluate, these assessments are subject to a clinician's judgment. Wearable sensor data with machine learning algorithms were introduced as an alternative to precisely quantify ambulatory kinematics and predict prospective falls. However, they require a long-term evaluation of large samples of subjects' locomotion and complex feature engineering of sensor kinematics. Therefore, it is critical to build an objective fall-risk detection model that can efficiently measure biometric risk factors with minimal costs. We built and studied a sensor data-driven convolutional neural network model to predict older adults' fall-risk status with relatively high sensitivity to geriatrician's expert assessment. The sample in this study is representative of older patients with multiple co-morbidity seen in daily medical practice. Three non-intrusive wearable sensors were used to measure participants' gait kinematics during the TUG test. This data collection ensured convenient capture of various gait impairment aspects at different body locations.