On the explainability of hospitalization prediction on a large COVID-19 patient dataset.

We develop various AI models to predict hospitalization on a large (over 110k) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021. Models range from Random Forest to Neural Network (NN) and Time Convolutional NN, where combination of the data modalities (tabular and time dependent) are performed at different stages (early vs. model fusion).

Artificial Intelligence Decision Support for Medical Triage.

Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation.

Cognitive DDx Assistant in Rare Diseases.

There are between 6,000 - 7,000 known rare diseases today. Identifying and diagnosing a patient with rare disease is time consuming, cumbersome, cost intensive and requires resources generally available only at large hospital centers. Furthermore, most medical doctors, especially general practitioners, will likely only see one patient with a rare disease if at all.