Semantic Text Analytics for Quality Controlled Extraction of Clinical Phenotype Information in Healthcare Integrated Bioban­king STACI2B2

高质量生物材料的不断增加是转化生物医学取得可持续和可重复结果的先决条件。这一观察结果不仅适用于探索性研究，而且越来越多地适用于验证研究结果。然而，一些人已经表达了怀疑，认为过多的临床前研究结果在转化为临床实践时是否能兑现承诺。例如，正在进行的生物标志物研究。在对新生物标志物的大量研究和临床验证的应用数量之间存在明显的差异。一个主要问题是缺乏对辅助样本质量差异的关注，以及基于具有明确定义的疾病和与目标疾病不同的合并症的比较集体对潜在标记的验证不足。过去，许多临床站点通过建立和维护专业生物库建立了高质量收集和储存此类生物样本的基础设施，但仍然缺乏对有效表型数据进行采样、确定有效比较集体和适当选择样本进行高质量生物库建立的常规工作流程。在我们的项目中，我们建议使用自动自然语言处理的方法从临床文档中提取此类信息。我们计划使用半监督机器学习技术建立文本分析管道，以获取与医学相关的命名实体(如疾病、药物、诊断)以及这些实体之间的关系(如相对于疾病和患者的药物的有效性或剂量、实验室和用于诊断的测试数据等)。非结构化的临床文件(如出院摘要、放射学或病理报告等)。因此，自动文本分析将成为从耶拿大学医院临床信息系统中包含的文件中计算与医学相关的背景数据的基础，并将即时提供结构化的评估程序，以便在进入常规实验室时实时选择定义明确的患者集体的样本。同时，可以利用进一步诊断不需要的残留材料来建立比较样本库。目前，德国任何一家医院都没有这样的德语临床文档信息提取系统，并将其集成到常规临床工作流程中。此外，我们规定，这样的联合努力将对转化医学的未来进步产生深远的影响，转化医学的发展超越了确定有效的表型数据、选择定义明确的患者集体和生产高质量生物材料的示范应用。

The growing availability of high-quality biomaterials is a prerequisite for sustainable and reproducible results in translational biomedicine. This observation not only holds for exploratory but also, increasingly, for validating research outcomes. However, some skepticism has already been expressed whether the plethora of results from preclinical studies hold their promises when transferred into clinical practice.Consider, as an example, ongoing research on biomarkers. There is an apparent discrepancy between the multitude of studies on novel biomarkers and the number of clinically validated applications. One major problem is the lacking concern for quality differences in ancillary samples and the insufficient validation of potential markers based on comparison collectives with well-defined diseases and comorbidities differing from the target disease.Whereas in the past the infrastructure for high-quality collection and warehousing of such bio samples has been established at many clinical sites by building and maintaining professional biobanks, what is still lacking are routine workflows to sample valid phenotype data, to determine valid comparison collectives and to properly select samples for high-quality biobanking. In our project, we propose to extract such information from clinical documents using methods from automatic natural language processing. We plan to build a text analytics pipeline using semi-supervised machine learning techniques to harvest medically relevant named entities (such as diseases, drugs, diagnoses) and relations among these entities (such as the effectiveness or dosage of medications relative to a disease and a patient, lab and test data for diagnosis, etc.) from unstructured clinical documents (such as discharge summaries, radiology or pathology reports, etc.). Automatic text analysis will thus form the basis for computing medically relevant context data from the documents contained in the clinical information system of the university hospital in Jena and will instantaneously feed structured evaluation procedures for the real-time selection of samples of well-defined collectives of patients when they enter routine laboratories. At the same time, residual material not needed for further diagnosis can be utilized for building up a repository of comparison samples.Such an information extraction system for German-language clinical documents and its integration into routine clinical workflows is currently not available in any German hospital. Moreover, we stipulate that such a combined effort will have far-reaching implications for future progress in translational medicine which goes beyond the exemplary application to determine validated phenotype data, to select well-defined collectives of patients and to produce high-quality bio materials.