… We compare this mentioned study with one that is developed on existing 2D and 3D deeplearning
models, combining them with the latest clinical understanding, and achieved an AUC …

… between clinical and radiographic features as well as to assess the feasibility of deep
learning … Clinical factors (age, symptoms, comorbidities) were investigated for association with …

… framework based on deep learning to process heterogeneous … EHRs do not quantitatively
capture PD symptom severity; … data-driven clinical patterns with machine learning. Specifically, …

… ability, DL can mine features that are related to clinical … but are strongly associated with
clinical outcomes. In contrast to … system is fast and robust in clinical use. Moreover, we collected …

… In this study, we perform clinical predictive models that estimate, using deep learning and …
in general, these symptoms begin as mild in all patients. However, severe symptoms such as …

… Their final report has also considered all pertinent clinical information, such as detailed
patient's history, symptoms, CAD predisposing factors, previous tests results, baseline ECG and …

… In this study, we developed a machine learning methodology to identify the most important
and significant clinical symptoms that predict true COVID-19 positive cases. We validated …

… deep learning architecture. To balance the time efficiency and performance in a practical
clinical NLP tool 28 , the deep learning… used to recognize mentions of clinical concepts, with 200…

… While expert rules are widely used in clinical medicine today, including most clinical … focus
primarily on machine learning and, more recently, deep learning. Machine learning (ML) is the …

… on clinical questionnaires, environmental data, and physiological data. Data on patient
symptoms … tests for COPD, and some studies [4,10] have also used these clinical tools to evaluate …