Fever detection from free-text clinical records for biosurveillance

RODS Laboratory, Center for Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Journal of Biomedical Informatics (Impact Factor: 2.48). 05/2004; 37(2):120-7. DOI: 10.1016/j.jbi.2004.03.002
Source: PubMed

ABSTRACT Automatic detection of cases of febrile illness may have potential for early detection of outbreaks of infectious disease either by identification of anomalous numbers of febrile illness or in concert with other information in diagnosing specific syndromes, such as febrile respiratory syndrome. At most institutions, febrile information is contained only in free-text clinical records. We compared the sensitivity and specificity of three fever detection algorithms for detecting fever from free-text. Keyword CC and CoCo classified patients based on triage chief complaints; Keyword HP classified patients based on dictated emergency department reports. Keyword HP was the most sensitive (sensitivity 0.98, specificity 0.89), and Keyword CC was the most specific (sensitivity 0.61, specificity 1.0). Because chief complaints are available sooner than emergency department reports, we suggest a combined application that classifies patients based on their chief complaint followed by classification based on their emergency department report, once the report becomes available.

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    ABSTRACT: The literature of medicine and medicinal data is growing specialized and increasing by every day. Most of the data is contained by the journal of medicines and biology which makes this type of textual mining a central and core problem. Finding disease-medicine relationships requires laborious examination of hundreds of possible candidate heterogeneous factors. Most of the peoples face serious problems in extracting and finding useful information to access the clinical support from the currently available search engines and other tools, thus there should have some ability to identify the relationship between disease and other relevant factors which could support clinical diagnosis. In the paper, we are presenting a methodology for extracting useful information from Medline papers. The system tries to identify the relationship of an active disease and extract relevant medicine for the patient automatically. The core objective of proposed system is to find out or extracts only those documents that user is looking from a huge repository of documents or some other collection of facts and figures. The proposed method extracts some useful and interesting keywords that reveal sentences containing our desired task patterns. After finding the regularities of these patterns based on specific keywords, we apply the techniques of our system and mined semantic relationship. Our findings got more than 20 such keyword frequently produced relevant information. Proposed techniques give multiple medicines proposed by domain experts. The choice for proposed medicines are ranked on the basis of frequency and also based on the experience of different surveys performed by the authors in their papers.
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    ABSTRACT: OBJECTIVE To assess the performance of an unmodified, general purpose natural language processing system to detect fever, and to assess the feasibility of parsing visit notes for syndromic surveillance. BACKGROUND With increased penetration of clinical information system products and increased interest in clinical data exchange, a variety of clinician's notes are becoming available for surveillance. Chief complaints have been studied extensively, and emergency department notes have received attention [1], but narrative clinic visit notes have gotten little attention.

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