临床推荐系统：用神经网络集成预测医学专业诊断选择
Clinical Recommender System: Predicting Medical Specialty Diagnostic Choices with Neural Network Ensembles
摘要：
对关键医疗资源（如临床专业知识和设施）的需求不断增长，促使基于人工智能（AI）的决策支持系统的出现。我们解决的问题，预测专业转诊的临床检查。作为手动创建的临床检查表的替代方案，我们提出了一个数据驱动的模型，该模型根据从电子健康记录（EHR）中提取的患者最近的临床记录推荐必要的诊断程序。这有可能使卫生系统扩大及时获得初步医疗专业诊断检查的病人。所提出的方法是基于一个合奏的前馈神经网络，并实现了显着更高的准确性相比，传统的临床检查表。
英文摘要：
The growing demand for key healthcare resources such as clinical expertise and facilities has motivated the emergence of artificial intelligence (AI) based decision support systems. We address the problem of predicting clinical workups for specialty referrals. As an alternative for manually-created clinical checklists, we propose a data-driven model that recommends the necessary set of diagnostic procedures based on the patients' most recent clinical record extracted from the Electronic Health Record (EHR). This has the potential to enable health systems expand timely access to initial medical specialty diagnostic workups for patients. The proposed approach is based on an ensemble of feed-forward neural networks and achieves significantly higher accuracy compared to the conventional clinical checklists.
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