Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.
Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.
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关于阿片类药物使用障碍（OUD）状态和严重程度的信息对于患者护理非常重要。临床记录为检测和表征有问题的阿片类药物使用提供了有价值的信息，需要开发自然语言处理（NLP）工具，这反过来又需要可靠标记的OUD相关文本和对文档模式的理解。为了告知自动化NLP方法，我们的目标是开发和评估用于表征OUD及其严重程度的注释模式，并在异质患者队列的临床笔记中记录OUD相关信息的模式。我们开发了一种注释模式，以根据精神疾病诊断和统计手册第5版中的标准来表征OUD的严重程度。总共有2名注释者审查了来自100名具有不同OUD证据的成年患者的关键就诊的临床记录，包括患有和不患有慢性疼痛的患者、接受和不接受OUD药物治疗的患者以及对照组。我们在句子层面完成了注释。我们根据注释文本的注释计算了严重程度评分，其中18个类别与OUD严重程度标准一致，并确定了OUD严重程度的阳性预测值。注释模式包含27个类。我们对82名患者的1436个句子进行了注释; 18名患者（其中11名为对照组）的注释未包含相关信息。15批经审查的注释中有11批注释间一致性超过70%。对照组患者的严重程度评分均为0。在非对照组患者中，平均严重程度评分为5.1（SD 3.2），表明中度OUD，检测中度或重度OUD的阳性预测值为0.71。来自急诊科和门诊的病程记录和记录包含了最多和最多样化的信息。物质滥用和精神病类是最普遍的，高度相关的笔记类型与患者之间的高共现。注释模式的实施证明了基于一小组临床笔记中的关键信息推断OUD严重程度的强大潜力，并突出显示记录此类信息的位置。这些进步将促进NLP工具的开发，以改善OUD的预防，诊断和治疗。
Information regarding opioid use disorder (OUD) status and severity is important for patient care. Clinical notes provide valuable information for detecting and characterizing problematic opioid use, necessitating development of natural language processing (NLP) tools, which in turn requires reliably labeled OUD-relevant text and understanding of documentation patterns. To inform automated NLP methods, we aimed to develop and evaluate an annotation schema for characterizing OUD and its severity, and to document patterns of OUD-relevant information within clinical notes of heterogeneous patient cohorts. We developed an annotation schema to characterize OUD severity based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. In total, 2 annotators reviewed clinical notes from key encounters of 100 adult patients with varied evidence of OUD, including patients with and those without chronic pain, with and without medication treatment for OUD, and a control group. We completed annotations at the sentence level. We calculated severity scores based on annotation of note text with 18 classes aligned with criteria for OUD severity and determined positive predictive values for OUD severity. The annotation schema contained 27 classes. We annotated 1436 sentences from 82 patients; notes of 18 patients (11 of whom were controls) contained no relevant information. Interannotator agreement was above 70% for 11 of 15 batches of reviewed notes. Severity scores for control group patients were all 0. Among noncontrol patients, the mean severity score was 5.1 (SD 3.2), indicating moderate OUD, and the positive predictive value for detecting moderate or severe OUD was 0.71. Progress notes and notes from emergency department and outpatient settings contained the most and greatest diversity of information. Substance misuse and psychiatric classes were most prevalent and highly correlated across note types with high co-occurrence across patients. Implementation of the annotation schema demonstrated strong potential for inferring OUD severity based on key information in a small set of clinical notes and highlighting where such information is documented. These advancements will facilitate NLP tool development to improve OUD prevention, diagnosis, and treatment.
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DOI：
10.1136/amiajnl-2012-000896
发表时间：
2013-06-01
影响因子：
6.4
作者：
Newton, Katherine M.;Peissig, Peggy L.;Denny, Joshua C.
通讯作者：
Denny, Joshua C.
影响因子：
4.9
作者：
Schirle L;Jeffery A;Yaqoob A;Sanchez-Roige S;Samuels DC
通讯作者：
Samuels DC
影响因子：
4.2
作者：
Freda PJ;Moore JH;Kranzler HR
通讯作者：
Kranzler HR
DOI：
10.1093/database/bay121
发表时间：
2018-01-01
期刊：
Database : the journal of biological databases and curation
影响因子：
--
作者：
Hu R;Dai Y;Jia P;Zhao Z
通讯作者：
Zhao Z
影响因子：
5.5
作者：
Arifkhanova，Aziza;Prieto，Jose Tomas;Shlay，Judith C.
通讯作者：
Shlay，Judith C.