Semantic representation of reported measurements in radiology

Background In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-text data consists of reports covering the assessment of changes in tumor burden, which are needed for the evaluation of cancer treatment success. Any change of lesion size is a critical factor in follow-up examinations. It is difficult to retrieve specific information from unstructured reports and to compare them over time. Therefore, a prototype was implemented that demonstrates the structured representation of findings, allowing selective review in consecutive examinations and thus more efficient comparison over time. Methods We developed a semantic Model for Clinical Information (MCI) based on existing ontologies from the Open Biological and Biomedical Ontologies (OBO) library. MCI is used for the integrated representation of measured image findings and medical knowledge about the normal size of anatomical entities. An integrated view of the radiology findings is realized by a prototype implementation of a ReportViewer. Further, RECIST (Response Evaluation Criteria In Solid Tumors) guidelines are implemented by SPARQL queries on MCI. The evaluation is based on two data sets of German radiology reports: An oncologic data set consisting of 2584 reports on 377 lymphoma patients and a mixed data set consisting of 6007 reports on diverse medical and surgical patients. All measurement findings were automatically classified as abnormal/normal using formalized medical background knowledge, i.e., knowledge that has been encoded into an ontology. A radiologist evaluated 813 classifications as correct or incorrect. All unclassified findings were evaluated as incorrect. Results The proposed approach allows the automatic classification of findings with an accuracy of 96.4 % for oncologic reports and 92.9 % for mixed reports. The ReportViewer permits efficient comparison of measured findings from consecutive examinations. The implementation of RECIST guidelines with SPARQL enhances the quality of the selection and comparison of target lesions as well as the corresponding treatment response evaluation. Conclusions The developed MCI enables an accurate integrated representation of reported measurements and medical knowledge. Thus, measurements can be automatically classified and integrated in different decision processes. The structured representation is suitable for improved integration of clinical findings during decision-making. The proposed ReportViewer provides a longitudinal overview of the measurements

Integrated representation of clinical data and medical knowledge: an ontology-based approach for the radiology domain

The increase in available clinical data and availability of medical knowledge provides the basis for various applications which aim to increase the quality and safety of healthcare. Personalized treatment, e.g. the optimal medication with fewest side effects, less unnecessary examinations and mistreatment can be realized based on the available data. Today however enhanced quality of treatment on the one hand and lower cost on the other hand are in conflict: in the current situation more data means more efforts for the clinicians and thus higher costs. This is mostly because of three problems: Firstly, clinical data is stored case-centric and distributed across different systems. It is not longitudinal integrated. Secondly, only small amount of the data is structured while high percentage of clinically relevant data is unstructured. Thirdly, existing data is not sufficiently aligned to medical knowledge and thus not on the appropriate level of detail for decision support systems. As a result of these problems most of the available data is simply not used in their full strength and no personalized treatment is applied. Today healthcare providers do not achieve improvements in quality and efficiency. The thesis has three objectives targeting parts of the aforementioned problems. The first objective is the creation of a semantic model for clinical information which is based on established upper ontologies. In particular, it is focused on the representation of clinical findings from radiology examinations. The second objective is to extract structured representations from radiology reports using formalized medical knowledge. The extracted information is stored in the semantic model. The third objective is to enrich the data using inference techniques and formalized medical knowledge to allow realization of different views on the data, needed by clinicians for more efficient decision making. In particular, radiology findings extracted from unstructured reports are classified as normal/abnormal and finding descriptions are linked to disease information. A longitudinal view of radiology finding data is realized through a prototype implementation of a report viewer which relies on medical knowledge about the human anatomy, the semantic representation of finding descriptions and the meta-data of the original radiology reports

Integrated representation of clinical data and medical knowledge: an ontology-based approach for the radiology domain

The increase in available clinical data and availability of medical knowledge provides the basis for various applications which aim to increase the quality and safety of healthcare. Personalized treatment, e.g. the optimal medication with fewest side effects, less unnecessary examinations and mistreatment can be realized based on the available data. Today however enhanced quality of treatment on the one hand and lower cost on the other hand are in conflict: in the current situation more data means more efforts for the clinicians and thus higher costs. This is mostly because of three problems: Firstly, clinical data is stored case-centric and distributed across different systems. It is not longitudinal integrated. Secondly, only small amount of the data is structured while high percentage of clinically relevant data is unstructured. Thirdly, existing data is not sufficiently aligned to medical knowledge and thus not on the appropriate level of detail for decision support systems. As a result of these problems most of the available data is simply not used in their full strength and no personalized treatment is applied. Today healthcare providers do not achieve improvements in quality and efficiency. The thesis has three objectives targeting parts of the aforementioned problems. The first objective is the creation of a semantic model for clinical information which is based on established upper ontologies. In particular, it is focused on the representation of clinical findings from radiology examinations. The second objective is to extract structured representations from radiology reports using formalized medical knowledge. The extracted information is stored in the semantic model. The third objective is to enrich the data using inference techniques and formalized medical knowledge to allow realization of different views on the data, needed by clinicians for more efficient decision making. In particular, radiology findings extracted from unstructured reports are classified as normal/abnormal and finding descriptions are linked to disease information. A longitudinal view of radiology finding data is realized through a prototype implementation of a report viewer which relies on medical knowledge about the human anatomy, the semantic representation of finding descriptions and the meta-data of the original radiology reports

Corpus-based Translation of Ontologies for Improved Multilingual Semantic Annotation

Ontologies have proven to be useful to enhance NLP-based applications such as information ex-traction. In the biomedical domain rich ontologies are available and used for semantic annotation of texts. However, most of them have either no or only few non-English concept labels and can-not be used to annotate non-English texts. Since translations need expert review, a full translation of large ontologies is often not feasible. For semantic annotation purpose, we propose to use the corpus to be annotated to identify high occurrence terms and their translations to extend respec-tive ontology concepts. Using our approach, the translation of a subset of ontology concepts is sufficient to significantly enhance annotation coverage. For evaluation, we automatically trans-lated RadLex ontology concepts from English into German. We show that by translating a rather small set of concepts (in our case 433), which were identified by corpus analysis, we are able to enhance the amount of annotated words from 27.36 % to 42.65 %.