With increasing and extensive use of electronic health records (EHR), clinicians are often challenged in retrieving relevant patient information efficiently and effectively to arrive at a diagnosis. While using the search function built into an EHR can be more useful than browsing in a voluminous patient record, it is cumbersome and repetitive to search for the same or similar information on similar patients. To address this challenge, there is a critical need to build effective recommender systems that can recommend search terms to clinicians accurately. In this study, we developed a hybrid collaborative filtering model to recommend search terms for a specific patient to a clinician. The model draws on information from patients’ clinical encounters and the searches that were performed during them. To generate recommendations, the model uses search terms which are (1) frequently co-occurring with the ICD codes recorded for the patient and (2) highly relevant to the most recent search terms. In one variation of the model (Hybrid Collaborative Filtering Method for Healthcare, or HCFMH), we use only the most recent ICD codes assigned to the patient, and in the other (Co-occurrence Pattern based HCFMH, or cpHCFMH), all ICD codes. We have conducted comprehensive experiments to evaluate the proposed model. These experiments demonstrate that our model outperforms state-of-the-art baseline methods for top-N search term recommendation on different data sets.

随着电子健康记录 (EHR) 的使用日益广泛，临床医生常常面临如何高效检索相关患者信息以做出诊断的挑战。虽然使用 EHR 内置的搜索功能比浏览大量患者记录更有用，但搜索类似患者的相同或相似信息既麻烦又重复。为了应对这一挑战，迫切需要建立有效的推荐系统，可以准确地向临床医生推荐搜索术语。在这项研究中，我们开发了一种混合协作过滤模型，向临床医生推荐特定患者的搜索词。该模型借鉴了患者的临床经历和期间进行的搜索的信息。为了生成推荐，该模型使用以下搜索词：(1) 经常与为患者记录的 ICD 代码同时出现；(2) 与最近的搜索词高度相关。在该模型的一种变体（医疗保健混合协同过滤方法，或 HCFMH）中，我们仅使用分配给患者的最新 ICD 代码，而在另一种模型（基于 HCFMH 的共现模式，或 cpHCFMH）中，我们使用所有 ICD 代码。我们进行了全面的实验来评估所提出的模型。这些实验表明，我们的模型在不同数据集上的前 N ​​个搜索词推荐方面优于最先进的基线方法。