  There has been a rapidly growing interest in Automatic Symptom Detection
(ASD) and Automatic Diagnosis (AD) systems in the machine learning research
literature, aiming to assist doctors in telemedicine services. These systems
are designed to interact with patients, collect evidence about their symptoms
and relevant antecedents, and possibly make predictions about the underlying
diseases. Doctors would review the interactions, including the evidence and the
predictions, collect if necessary additional information from patients, before
deciding on next steps. Despite recent progress in this area, an important
piece of doctors' interactions with patients is missing in the design of these
systems, namely the differential diagnosis. Its absence is largely due to the
lack of datasets that include such information for models to train on. In this
work, we present a large-scale synthetic dataset of roughly 1.3 million
patients that includes a differential diagnosis, along with the ground truth
pathology, symptoms and antecedents for each patient. Unlike existing datasets
which only contain binary symptoms and antecedents, this dataset also contains
categorical and multi-choice symptoms and antecedents useful for efficient data
collection. Moreover, some symptoms are organized in a hierarchy, making it
possible to design systems able to interact with patients in a logical way. As
a proof-of-concept, we extend two existing AD and ASD systems to incorporate
the differential diagnosis, and provide empirical evidence that using
differentials as training signals is essential for the efficiency of such
systems or for helping doctors better understand the reasoning of those
systems.
