Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.
Predicting early psychiatric readmission with natural language processing of narrative discharge summaries.
复制标题
DOI：
10.1038/tp.2015.182
复制
发表时间：
2016-10-18
影响因子：
6.8
通讯作者：
Perlis RH
中科院分区：
文献类型：
--
作者：
Rumshisky A;Ghassemi M;Naumann T;Szolovits P;Castro VM;McCoy TH;Perlis RH
预测精神病患者再入院的能力将有助于制定干预措施，以降低这一风险，这是精神病保健费用的主要驱动因素。开发可靠预测指标所必需的病程症状或特征在编码账单数据中没有，但可能存在于叙述性电子健康记录（EHR）出院摘要中。我们确定了1994年至2012年间入住精神科住院病房的主要诊断为重度抑郁症的个体队列，并提取了住院精神科出院记录。使用这些数据，我们训练了一个75个主题的潜在狄利克雷分配（LDA）模型，这是一种自然语言处理形式，可以识别与文档集合中讨论的主题相关的单词组。该队列被随机分割，以获得训练（70%）和测试（30%）数据集，我们为单独的基线临床特征、基线特征加常见个体词以及以上加上从75个主题LDA模型中识别的主题训练了单独的支持向量机模型。在4687例有出院总结的患者中，470例在30天内再次入院。75个主题的LDA模型包括与精神症状（自杀、严重抑郁、焦虑、创伤、饮食/体重和恐慌）和严重抑郁症合并症（感染、产后、脑肿瘤、腹泻和肺部疾病）相关的主题。通过纳入LDA主题，以测试数据集中接收者-工作特征曲线下面积测量的再入预测从基线（曲线下面积0.618）到基线+1000字（0.682）再到基线+75个主题（0.784）得到改善。纳入来自叙述笔记的主题可以更准确地区分本队列中精神疾病再入院高风险个体。如果可以在其他临床队列中建立通用性，主题建模和相关方法提供了使用电子病历改进预测的潜力。
The ability to predict psychiatric readmission would facilitate the development of interventions to reduce this risk, a major driver of psychiatric health-care costs. The symptoms or characteristics of illness course necessary to develop reliable predictors are not available in coded billing data, but may be present in narrative electronic health record (EHR) discharge summaries. We identified a cohort of individuals admitted to a psychiatric inpatient unit between 1994 and 2012 with a principal diagnosis of major depressive disorder, and extracted inpatient psychiatric discharge narrative notes. Using these data, we trained a 75-topic Latent Dirichlet Allocation (LDA) model, a form of natural language processing, which identifies groups of words associated with topics discussed in a document collection. The cohort was randomly split to derive a training (70%) and testing (30%) data set, and we trained separate support vector machine models for baseline clinical features alone, baseline features plus common individual words and the above plus topics identified from the 75-topic LDA model. Of 4687 patients with inpatient discharge summaries, 470 were readmitted within 30 days. The 75-topic LDA model included topics linked to psychiatric symptoms (suicide, severe depression, anxiety, trauma, eating/weight and panic) and major depressive disorder comorbidities (infection, postpartum, brain tumor, diarrhea and pulmonary disease). By including LDA topics, prediction of readmission, as measured by area under receiver-operating characteristic curves in the testing data set, was improved from baseline (area under the curve 0.618) to baseline+1000 words (0.682) to baseline+75 topics (0.784). Inclusion of topics derived from narrative notes allows more accurate discrimination of individuals at high risk for psychiatric readmission in this cohort. Topic modeling and related approaches offer the potential to improve prediction using EHRs, if generalizability can be established in other clinical cohorts.