… models for monitoring and diagnostic classification based on clinical data and symptoms.
Machine Learning (ML) and Deep Learning (DL) models have been widely proposed in the …

… ) subjects and other Parkinsonian syndrome categories using clinical data (eg, speech and
… In fact, Deep Learning DL has provided an ultimate solution for the features' extraction and …

… key strengths of machine and deep learning algorithms. These … significant heterogeneity of
symptoms amongst populations … applicable in a clinical setting. Rosa et al. [53] developed a …

… These results highlight once more how deep learning could have a profound impact on
clinical practice and on personalized medicine. However, it has to be noted that this is a …

… battle these problems, machine learning approaches have been … With the growing interest
in machine and deep learning … their use of machine learning and deep learning technologies, …

… We included all research articles in all languages that applied deep learning models for
clinical … Modeling and prediction of clinical symptom trajectories in Alzheimer’s disease using …

… Using the above data, they designed a model that used features like age, gender, and clinical
symptoms like fever, cough, and headache to forecast test results. The training and test set …

… , facilitate clinical practice, and improve the clinical outcomes of … clinical data, including
clinical symptoms, medical history, family history, and neuroimaging features [66]. Deep learning …

… Our analysis shows growing acceptance of deep learning as a baseline for NLP research, …
surprisingly nuanced (eg, the scarcity of French language clinical NLP with deep learning). …

… and several modalities such as demographic, clinical, imaging, genetics and environmental
… Deep learning, a subpart of machine learning, provides complex algorithms that can learn …