  Respiratory diseases, including asthma, bronchitis, pneumonia, and upper
respiratory tract infection (RTI), are among the most common diseases in
clinics. The similarities among the symptoms of these diseases precludes prompt
diagnosis upon the patients' arrival. In pediatrics, the patients' limited
ability in expressing their situation makes precise diagnosis even harder. This
becomes worse in primary hospitals, where the lack of medical imaging devices
and the doctors' limited experience further increase the difficulty of
distinguishing among similar diseases. In this paper, a pediatric fine-grained
diagnosis-assistant system is proposed to provide prompt and precise diagnosis
using solely clinical notes upon admission, which would assist clinicians
without changing the diagnostic process. The proposed system consists of two
stages: a test result structuralization stage and a disease identification
stage. The first stage structuralizes test results by extracting relevant
numerical values from clinical notes, and the disease identification stage
provides a diagnosis based on text-form clinical notes and the structured data
obtained from the first stage. A novel deep learning algorithm was developed
for the disease identification stage, where techniques including adaptive
feature infusion and multi-modal attentive fusion were introduced to fuse
structured and text data together. Clinical notes from over 12000 patients with
respiratory diseases were used to train a deep learning model, and clinical
notes from a non-overlapping set of about 1800 patients were used to evaluate
the performance of the trained model. The average precisions (AP) for
pneumonia, RTI, bronchitis and asthma are 0.878, 0.857, 0.714, and 0.825,
respectively, achieving a mean AP (mAP) of 0.819.

Artificial Intelligence