Gundlapalli Adi V, South Brett R, Phansalkar Shobha, Kinney Anita Y, Shen Shuying, Delisle Sylvain, Perl Trish, Samore Matthew H
Departments of Internal Medicine and.
Summit Transl Bioinform. 2008 Mar 1;2008:36-40.
Informatics tools to extract and analyze clinical information on patients have lagged behind data-mining developments in bioinformatics. While the analyses of an individual's partial or complete genotype is nearly a reality, the phenotypic characteristics that accompany the genotype are not well known and largely inaccessible in free-text patient health records. As the adoption of electronic medical records increases, there exists an urgent need to extract pertinent phenotypic information and make that available to clinicians and researchers. This usually requires the data to be in a structured format that is both searchable and amenable to computation. Using inflammatory bowel disease as an example, this study demonstrates the utility of a natural language processing system (MedLEE) in mining clinical notes in the paperless VA Health Care System. This adaptation of MedLEE is useful for identifying patients with specific clinical conditions, those at risk for or those with symptoms suggestive of those conditions.
用于提取和分析患者临床信息的信息学工具落后于生物信息学中数据挖掘的发展。虽然对个体部分或完整基因型的分析几乎成为现实，但与基因型相关的表型特征却鲜为人知，并且在自由文本的患者健康记录中大多无法获取。随着电子病历的采用率不断提高，迫切需要提取相关的表型信息并将其提供给临床医生和研究人员。这通常要求数据采用既便于搜索又适合计算的结构化格式。以炎症性肠病为例，本研究展示了自然语言处理系统（MedLEE）在无纸化退伍军人医疗保健系统中挖掘临床记录的效用。MedLEE的这种改编版本有助于识别患有特定临床病症的患者、有患病风险的患者或有提示这些病症症状的患者。
