Medical tests are crucial for treatment decision making. However, over-testing can often occur in any medical speciality or level of expertise. Since over-testing usually results in a financial burden for patients and is also a waste of medical resources, this naturally leads to the question: which medical test items (MTIs) are necessary and should be prioritized for the target patients? It is a nontrivial task to identify the right MTIs due to the diversified health status of patients and the complicated prerequisites of therapies. To this end, in this paper, we propose a data-driven approach to evaluate the priority which should be given to MTIs by modeling the relationships between MTIs and therapies. Specifically, we first develop a dual hierarchical topic model (DHTM), which views the adopted hierarchical therapies as labeled topics and the MTI reports, i.e., the set of hierarchical attribute-value pairs (AVPs), as documents. Then, with the therapy-AVP distribution and the partial MTI reports of the target patient, we can scope the candidate therapies, which are further utilized to evaluate the accumulated gain of MTIs to be tested. Moreover, the next MTI recommendation is conducted based on the gains. Finally, extensive experiments on real-world medical data validate the effectiveness of our approach, and some interesting observations are also provided.

医学检查对于治疗决策至关重要。然而，过度测试通常发生在任何医学专业或专业水平中。由于过度检测通常会给患者带来经济负担，同时也是对医疗资源的浪费，这自然会引出一个问题：哪些医学检测项目 （MTI） 是必要的，应该优先提供给目标患者？由于患者的健康状况多样化和治疗的先决条件复杂，确定正确的 MTI 是一项艰巨的任务。为此，在本文中，我们提出了一种数据驱动的方法，通过对 MTI 和疗法之间的关系进行建模来评估应优先考虑 MTI。具体来说，我们首先开发了一个双分层主题模型 （DHTM），该模型将采用的分层疗法视为标记主题，将 MTI 报告（即分层属性-值对 （AVP） 集）视为文档。然后，根据目标患者的治疗 AVP 分布和部分 MTI 报告，我们可以确定候选疗法的范围，这些疗法进一步用于评估待测 MTI 的累积增益。此外，下一个 MTI 建议是根据收益进行的。最后，对真实世界医学数据的广泛实验验证了我们方法的有效性，并且还提供了一些有趣的观察结果。