OBJECTIVE Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers. MATERIALS AND METHODS All clinical notes available on the 38,575 patients enrolled in the Mount Sinai BioMe cohort were loaded into the NLP system. We compared analysis of structured and unstructured EHR data using NLP, free-text search, and diagnostic codes with validation against expert adjudication. We then used the NLP findings to measure physician impression of progression from early-stage NAFLD to NASH or cirrhosis. Similarly, we used the same NLP findings to identify mentions of NAFLD in radiology reports that did not persist into clinical notes. RESULTS Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP outperformed ICD and text search in both sensitivity (NLP: 0.93, ICD: 0.28, text search: 0.81) and F2 score (NLP: 0.92, ICD: 0.34, text search: 0.81). Of 2281 NAFLD patients, 673 (29.5%) were believed to have progressed to NASH or cirrhosis. Among 176 where NAFLD was noted prior to NASH, the average progression time was 410 days. 619 (27.1%) NAFLD patients had it documented only in radiology notes and not acknowledged in other forms of clinical documentation. Of these, 170 (28.4%) were later identified as having likely developed NASH or cirrhosis after a median 1057.3 days. DISCUSSION NLP-based approaches were more accurate at identifying NAFLD within the EHR than ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. Analysis of information flows demonstrated loss of key information that could have been used to help prevent the progression of early NAFLD (NAFL) to NASH or cirrhosis. CONCLUSION For identification of NAFLD, NLP performed better than alternative selection modalities. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.

中文翻译：

---

将自然语言处理的增强智能应用于电子健康记录，以识别患有疾病进展风险的非酒精性脂肪肝患者。

目的电子健康记录（EHR）系统包含结构化数据（例如诊断代码）和非结构化数据（临床文档）。可以通过对两者的分析得出临床见解。已经充分证明了使用自然语言处理（NLP）算法来有效分析非结构化数据。在这里，我们检查了NLP在识别非酒精性脂肪肝患者中的作用，评估疾病进展的模式以及在与提供者之间的沟通中断相关的护理方面的差距。材料和方法将参加西奈山生物医学研究所的38,575名患者的所有可用临床笔记载入NLP系统。我们比较了使用NLP，自由文本搜索，以及经过专家裁定验证的诊断代码。然后，我们使用NLP研究结果来衡量医师对从早期NAFLD到NASH或肝硬化进展的印象。同样，我们使用相同的NLP发现来确定放射学报告中提到NAFLD的内容，这些内容并未保留在临床记录中。结果在38,575例患者中，我们确定了2,281例NAFLD患者。从其余的患者中，选择10,653名具有相似数据密度的患者作为对照组。NLP的灵敏度（NLP：0.93，ICD：0.28，文本搜索：0.81）和F2得分（NLP：0.92，ICD：0.34，文本搜索：0.81）均优于ICD和文本搜索。在2281名NAFLD患者中，有673名（29.5％）被认为已发展为NASH或肝硬化。在NASH之前注意到NAFLD的176个患者中，平均进展时间为410天。619（27。1％）NAFLD患者仅在放射学笔记中记录了该信息，而在其他形式的临床文档中未对此进行确认。其中有170（28.4％）人在中位数1057.3天后被确定为可能发展为NASH或肝硬化。讨论基于NLP的方法比基于ICD /文本搜索的方法在EHR中识别NAFLD更准确。在随后的临床文献中常常不承认可疑的NAFLD影像学检查。后来发现许多此类患者患有更晚期的肝脏疾病。对信息流的分析表明，本可以用来帮助防止早期NAFLD（NAFL）演变为NASH或肝硬化的关键信息丢失了。结论对于鉴定NAFLD，NLP的表现优于其他选择方式。