Extreme Multilabel Classification for Specialist Doctor Recommendation
  with Implicit Feedback and Limited Patient Metadata
Filipa Valdeira Stevo Racković Valeria Danalachi Qiwei Han Cláudia Soares
Recommendation Systems (RS) are often used to address the issue of medical
doctor referrals. However, these systems require access to patient feedback and
medical records, which may not always be available in real-world scenarios. Our
research focuses on medical referrals and aims to predict recommendations in
different specialties of physicians for both new patients and those with a
consultation history. We use Extreme Multilabel Classification (XML), commonly
employed in text-based classification tasks, to encode available features and
explore different scenarios. While its potential for recommendation tasks has
often been suggested, this has not been thoroughly explored in the literature.
Motivated by the doctor referral case, we show how to recast a traditional
recommender setting into a multilabel classification problem that current XML
methods can solve. Further, we propose a unified model leveraging patient
history across different specialties. Compared to state-of-the-art RS using the
same features, our approach consistently improves standard recommendation
metrics up to approximately $10\%$ for patients with a previous consultation
history. For new patients, XML proves better at exploiting available features,
outperforming the benchmark in favorable scenarios, with particular emphasis on
recall metrics. Thus, our approach brings us one step closer to creating more
effective and personalized doctor referral systems. Additionally, it highlights
XML as a promising alternative to current hybrid or content-based RS, while
identifying key aspects to take into account when using XML for recommendation
tasks.