Abstract
Healthcare information support (HIS) is essential in managing, gathering, and disseminating information for healthcare decision support through the Internet. To support HIS, text classification (TC) is a key kernel. Upon receiving a text of healthcare need (e.g. symptom description from patients) or healthcare information (e.g. information from medical literature and news), a text classifier may determine its corresponding categories (e.g. diseases), and hence subsequent HIS tasks (e.g. online healthcare consultancy and information recommendation) may be conducted. The key challenge lies on high-quality TC, which aims to classify most texts into suitable categories (i.e. recall is very high), while at the same time, avoid misclassifications of most texts (precision is very high). High-quality TC is particularly essential, since healthcare is a domain where an error may incur higher cost and/or serious problems. Unfortunately, high-quality TC was seldom achieved in previous studies. In the paper, we present a case study in which a high-quality classifier is built to support HIS in Chinese disease-related information, including the cause, symptom, curing, side-effect, and prevention of cancer. The results show that, without relying on domain knowledge and complicated processing, cancer information may be classified into suitable categories, with a controlled amount of confirmations.
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Liu, RL. (2007). Text Classification for Healthcare Information Support. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_5
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DOI: https://doi.org/10.1007/978-3-540-73325-6_5
Publisher Name: Springer, Berlin, Heidelberg
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