Improving Eligibility Classification on Clinical Trials Document using Bidirectional Long Short Term Memory Recurrent Neural Network

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Jasmir Jasmir, Siti Nurmaini , Reza Firsandaya Malik, Bambang Tutuko


Cancer clinical trials intervention are generally too restrictive, and some patients are often excluded on the basis of
comorbidity, past or concomitant treatments, or the fact that they are over a certain age. In this research we built a
classification model for clinical information using public clinical trial protocols labeled as eligible or not eligible. Text
classifications are trained using deep learning to determine the predictive outcome of short free text statements
reflectingeligible and not eligible clinical information.then we also performed semantic analysis for the obtained wordembedding
representations and were able to identify similar treatments. We have proven that learning outcomes using deep
learning methods to extract medical information from clinical trial documents have been successful in assisting health
practitioners in prescribing treatments. The evaluation results showed a value with an accuracy value is 77.74%, precision is
76.8%, recall is 80.80%, and F1-score is 78.80%

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