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.

研究使用极端多标签分类方法解决医生推荐系统中的医疗转诊问题，并提出了一个统一的模型利用患者病历。相比使用相同特征的最先进推荐系统，该方法在有咨询历史的患者中稳定改善了标准推荐度量指标达约 10%。对于新患者，极端多标签分类方法在利用可用特征方面表现更好，在有利情况下胜过基准系统，特别是强调召回度量指标。因此，该研究方法使医生推荐系统更加有效和个性化，并将极端多标签分类方法作为一种有前途的替代选择，并指出使用该方法时需要考虑的关键方面。

利用隐式反馈和有限患者元数据的专科医生推荐的极度多标签分类

Extreme Multilabel Classification for Specialist Doctor Recommendation  with Implicit Feedback and Limited Patient Metadata

The project aimed to define the rules and develop a technological solution to
automatically identify a set of attributes within free-text clinical records
written in Portuguese. The first application developed and implemented on this
basis was a structured summary of a patient's clinical history, including
previous diagnoses and procedures, usual medication, and relevant
characteristics or conditions for clinical decisions, such as allergies, being
under anticoagulant therapy, etc. The project's goal was achieved by a
multidisciplinary team that included clinicians, epidemiologists, computational
linguists, machine learning researchers and software engineers, bringing
together the expertise and perspectives of a public hospital, the university
and the private sector. Relevant benefits to users and patients are related
with facilitated access to the patient's history, which translates into
exhaustiveness in apprehending the patient's clinical past and efficiency due
to time saving.

研究开发了一种技术解决方案，通过机器学习自动识别临床记录中的属性，使患者历史记录更全面，提高了临床决策的效率。