For clinical decision support systems, the disease knowledge base accounts for the majority of the total development cost, because it requires considerable effort by highly-paid domain experts. However, existing research on automated acquisition of medical knowledge has focused on accuracy, while mostly ignoring the cost issue. This case study investigates the cost breakdown of our simplified disease knowledge base, and discusses a way of reducing development cost of the core component for clinical decision support systems. To achieve broad disease coverage with limited cost, we adopted a hybridization approach, that is, combining a handmade knowledge base for essential diseases and an automatically generated knowledge base for rare diseases. Toward further cost reduction, the case study suggested to minimize the human intervention by medical experts through i) keeping the structure of the knowledge base simple, ii) establishing a public resource for laboratory examination results, and iii) lazy evaluation of data quality in the utilization phase. Although the resulting knowledge base may not be adequate for a definitive diagnosis, the approach could be suitable to build clinical decision support systems for differential diagnosis and for disease search engines.

Text mining of clinical findings has been employed to extract clinical information contained in “electronic medical records” without the need for labor intensive work by medical experts. However, the automated building of disease ontology necessitates knowledge acquisition of clinical findings documented in “medical literature” that requires an independent strategy. This study performs a preliminary analysis of clinical finding expressions in medical literature to enable the automated acquisition of disease knowledge. To this end, we selected descriptions of 20 diseases in a free-text format and annotated the texts to extract expressions of clinical findings. This resulted in 1368 expressions with varying lengths and syntactic features, and 161 annotator comments. The comments suggested that certain types of expressions, which were further classified into 10 categories. Also, in-depth analyses of their syntactic and semantic characteristics were performed, resulting in the following observations. First, expressions of clinical findings have certain patterns, syntactic and semantic, which can be exploited for appropriate knowledge acquisition. Second, clinical knowledge may guide the knowledge acquisition process in a top-down manner. Third, natural language processing of medical literature requires specific considerations compared with the processing of health records, namely, i) distinction of subjects, ii) handling of generalized knowledge, and iii) processing of expressions for examination results. This preliminary survey on the expressions in medical literature provides helpful insights for future corpus design.

Knowledge about the causal relationship between diseases and their laboratory findings is a key component for clinical decision support systems. For efficient acquisition of such knowledge, this paper attempted to represent the interpretation of laboratory results in a guidebook for laboratory examinations. A preliminary survey revealed the structure of the knowledge compiled in the guidebook, and found essential patterns in the cause-effect relationship. We then attempted to code the knowledge, utilizing a simple cause-effect relationship between exam results and their possible causes, expressed in a disease master table. For coding of the knowledge, a two-step approach was used: first the causing disease was looked up automatically in the disease master table, and then, manually. In the study, 84.5% of the knowledge in the guidebook was identified as a candidate for the coding in the simple cause-effect relationship, and 69.1% of the knowledge was successfully coded. Failure analysis suggested that further expressive power in the representation is gained only at the cost of considerable human intervention in the knowledge acquisition, and the cost for the utilization of the resulting data. Accordingly, for a certain type of application, which might prefer simplicity over accuracy or completeness of the information, the minimalist representation could be a reasonable choice.

BACKGROUND: Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge. RESULTS: To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, 'bio-inference,' as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of 'bio-inference' schemes observed in the pathway corpus. CONCLUSIONS: We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text.

We present a practical HPSG parser for English, an intelligent search engine to retrieve MEDLINE abstracts that represent biomedical events and an efficient MED-LINE search tool helping users to find information about biomedical entities such as genes, proteins, and the interactions between them.

A method of stochastic syntactic analysis is applied to extracting the logical structure of a printed document from its physical layout and keywords indicating logical components. The document is parsed as a sentence consisting of text lines and graphic objects according to a stochastic regular grammar with attributes. By using stochastic analysis, the parser can retain possible results in order of their probability, and thus, if ambiguity occurs, it selects an optimal result more appropriately than deterministic systems. A mark up system applying the method was constructed, and 87% of the logical components of manuals and 82% of those of technical papers are correctly marked up. The rate improved to 89% when the second candidates were considered, showing the advantage of the authors' approach over the deterministic approach.