Abstract
An extensive set of applications and approaches for automatic disease inference with risk assessment system in medical fields objected to improve the efficacy and efficiency of patient care. Recent research studies are getting done in the area to predict patients’ future health conditions based on the medical records, particularly full clinical texts, medical transcriptions, clinical measurements, or medical codes. Healthcare data are highly complex, multi-dimensional, and heterogeneous in nature which are the key challenges. In this paper, a system has been proposed for disease inference by extracting symptoms and mapping the metadata using Unified Medical Language System (UMLS) to have the disease code. The symptoms and metadata are extracted from medical transcripts using natural language processing (NLP) and the extracted information has been categorized into chief complaints, present illness, and past history. These extracted symptoms are mapped using UMLS for disease code inference. Based on the disease code, the risk classifier classifies the level of risk. The proposed system based on deep learning and UMLS for disease inference resulted with significant improvement in accuracy.
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Acknowledgements
The infrastructure facility provided by Department of Science and Technology, Government of India (DST-FIST) was used while implementing the solution model. The facility and support were provided by IBM for the execution of this study.
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Arikrishnan, T., Swamynathan, S. (2021). Medical Transcriptions and UMLS-Based Disease Inference and Risk Assessment Using Machine Learning. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_49
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DOI: https://doi.org/10.1007/978-981-15-5788-0_49
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