Abstract
The Medical diagnosis and screening field has benefited tremendously from the advancement of computer technology and access to data over the past decades. However, due to the complexity of medical diagnosis and medical research, there are still many unknown problems in this field. With the recent emphasis of the national government and academic agencies in open sharing data and advancement of computational tools, e.g. machine learning and artificial intelligence, there is a renewed hope to tackle such complex problems. One of the fields that can benefit greatly from such advancement is the cancer clinical trial matching. This field has gained a lot of attention due to its complexity and the large number of people it affects. The algorithms proposed in this paper aim to assist clinicians and patients in cancer clinical trial matching by reducing the number of eligible trials using a combination of logical conditions and text mining of trial descriptions. Data for this study is provided by the Clinical Trial website which consists of 100 patients’ demographic and clinical information. Our preliminary results demonstrate the capabilities of unsupervised learning and conditional logic in reducing the matched trials and suggest further exploration for improvement and optimization of clinical trial matching for clinicians.
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Mansouri, M., Roland, J., Nukala, S., Cho, J., Sartipi, M. (2020). The Heavy Lifting Treatment Helper (HeaLTH) Algorithm: Streamlining the Clinical Trial Selection Process. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_37
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DOI: https://doi.org/10.1007/978-3-030-63393-6_37
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