Mayo Clinic, Rochester, Minnesota, USA.
J Am Med Inform Assoc. 2012 Jun;19(e1):e83-9. doi: 10.1136/amiajnl-2011-000295. Epub 2011 Dec 1.
To develop an algorithm for the discovery of drug treatment patterns for endocrine breast cancer therapy within an electronic medical record and to test the hypothesis that information extracted using it is comparable to the information found by traditional methods.
The electronic medical charts of 1507 patients diagnosed with histologically confirmed primary invasive breast cancer.
The automatic drug treatment classification tool consisted of components for: (1) extraction of drug treatment-relevant information from clinical narratives using natural language processing (clinical Text Analysis and Knowledge Extraction System); (2) extraction of drug treatment data from an electronic prescribing system; (3) merging information to create a patient treatment timeline; and (4) final classification logic.
Agreement between results from the algorithm and from a nurse abstractor is measured for categories: (0) no tamoxifen or aromatase inhibitor (AI) treatment; (1) tamoxifen only; (2) AI only; (3) tamoxifen before AI; (4) AI before tamoxifen; (5) multiple AIs and tamoxifen cycles in no specific order; and (6) no specific treatment dates. Specificity (all categories): 96.14%-100%; sensitivity (categories (0)-(4)): 90.27%-99.83%; sensitivity (categories (5)-(6)): 0-23.53%; positive predictive values: 80%-97.38%; negative predictive values: 96.91%-99.93%.
Our approach illustrates a secondary use of the electronic medical record. The main challenge is event temporality.
We present an algorithm for automated treatment classification within an electronic medical record to combine information extracted through natural language processing with that extracted from structured databases. The algorithm has high specificity for all categories, high sensitivity for five categories, and low sensitivity for two categories.
开发一种算法，用于在电子病历中发现内分泌乳腺癌治疗的药物治疗模式，并检验以下假设，即使用该算法提取的信息与传统方法发现的信息具有可比性。
1507 例组织学确诊为原发性浸润性乳腺癌患者的电子病历。
自动药物治疗分类工具包括以下组件：（1）使用自然语言处理（临床文本分析和知识提取系统）从临床叙述中提取药物治疗相关信息；（2）从电子处方系统中提取药物治疗数据；（3）合并信息以创建患者治疗时间轴；（4）最终分类逻辑。
算法与护士摘要器结果之间的一致性，针对以下类别进行测量：（0）无他莫昔芬或芳香化酶抑制剂（AI）治疗；（1）仅他莫昔芬；（2）仅 AI；（3）他莫昔芬前 AI；（4）AI 前他莫昔芬；（5）无特定顺序的多个 AI 和他莫昔芬周期；（6）无特定治疗日期。特异性（所有类别）：96.14%-100%；敏感性（类别（0）-（4））：90.27%-99.83%；敏感性（类别（5）-（6））：0-23.53%；阳性预测值：80%-97.38%；阴性预测值：96.91%-99.93%。
我们的方法说明了电子病历的二次使用。主要挑战是事件的时间性。
我们提出了一种在电子病历中进行自动治疗分类的算法，以将通过自然语言处理提取的信息与从结构化数据库提取的信息相结合。该算法对所有类别均具有高特异性，对五个类别具有高敏感性，对两个类别具有低敏感性。
