Journal des technologies de l'information et du génie logiciel
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Using Fuzzy Logic and Ranking Algorithms Techniques for Automatic Summarization and Extraction of Clinical Text

Moges Tsegaw Melesse*, Gizatie Desalegn Taye, Gezahegn Mulusew

Information and knowledge management has become a serious issue in the endeavor to serve the medical society due to the growing volume of data, the absence of structured information, and the diversity of information. Clinical doctors may need to know the information included in any piece of clinical free text, but do not have the time to read the entire item. This problem can be mitigated by using an automatic text summarizing technique that reduces the amount of time required while maintaining the integrity of the information. Recognizing the redundancy is a problem that has yet to be solved, and fragmentation makes creating an effective clinical summary even more difficult. We propose an automatic clinical free text summarizer in this work. The researcher utilizes five extraction rates for both rank and fuzzy logic algorithms to summarize the clinical free texts. As a result, the summarizing rates are ten percent, twenty percent, thirty percent, forty percent, and fifty percent. The ranking algorithm had the highest accuracy of 43.52 percent among the five extractive summaries, while the fuzzy logic method had the best accuracy of 43.88 percent. The outcome shown that fuzzy logic extractive summarization outperforms rank algorithm extractive summarization. Fuzzy logic is founded on the idea of computing with words rather than numbers, because words are less accurate than numbers. Using linguistic variables, fuzzy logic seeks to imitate human reasoning. The result is too little; thus we advocate using supervised algorithms to produce a satisfying performance that medical practitioners will approve. The system's performance can be improved further by looking into a variety of domain-specific aspects and enhancing the methods for detecting medical entities.