Explore the automated acquisition of knowledge in biomedical and clinical documents using text mining and statistical techniques to identify disease-drug associations. Design: Biomedical literature and clinical narratives from the patient record were mined to gather knowledge about disease-drug associations. Two NLP systems, BioMedLEE and MedLEE, were applied to Medline articles and discharge summaries, respectively. Disease and drug entities were identified using the NLP systems in addition to MeSH annotations for the Medline articles. Focusing on eight diseases, co-occurrence statistics were applied to compute and evaluate the strength of association between each disease and relevant drugs. Results: Ranked lists of disease-drug pairs were generated and cutoffs calculated for identifying stronger associations among these pairs for further analysis. Differences and similarities between the text sources (i.e., biomedical literature and patient record) and annotations (i.e., MeSH and NLP-extracted UMLS concepts) with regards to disease-drug knowledge were observed. This paper presents a method for acquiring disease-specific knowledge and a feasibility study of the method. The method is based on applying a combination of NLP and statistical techniques to both biomedical and clinical documents. The approach enabled extraction of knowledge about the drugs clinicians are using for patients with specific diseases based on the patient record, while it is also acquired knowledge of drugs frequently involved in controlled trials for those same diseases. In comparing the disease-drug associations, we found the results to be appropriate: the two text sources contained consistent as well as complementary knowledge, and manual review of the top five disease-drug associations by a medical expert supported their correctness across the diseases.  Clinical knowledge is constantly evolving as new discoveries are made and practices change. This knowledge is valuable but often buried in text within a range of sources such as journal articles and clinical narratives in the patient record. Through automated approaches, information associated with diseases can be extracted and integrated from these disparate text sources for understanding the various characteristics of diseases (e.g., treatment or symptoms 1-3 ) and how they may change over time. The availability of up-to-date disease profiles may be valuable for a variety of applications including decision support (e.g., recommending treatments), quality assurance (e.g., inter-and intra-institutional review), clinical information needs (e.g., answering clinical questions), information retrieval (e.g., classifying documents), and data mining (e.g., hypothesis discovery). Literature on randomized controlled trials (RCTs) reports on the results of testing one or more treatments such as drugs, devices, or procedures that are studied for diagnostic, therapeutic, or prophylactic effectiveness. 4 Patient records reflect practices within an institution and provide patient-specific information such as past and present diseases or medications. Given these contrasting roles, these text sources may offer valuable complementary disease-specific knowledge. Natural language processing (NLP) has been shown to facilitate the tasks of extracting information and relations between information captured within text.
E. S. Chen, et.al, proposed an automated disease acquisition of disease-drug knowledge from biomedical documents REF .
Automated Acquisition of Disease–Drug Knowledge from Biomedical and Clinical Documents: An Initial Study
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Computer Science Medicine