Automated Medical Diagnosis from Clinical Data
Applied Data Science
Databases and Information Systems | Data Science
2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)
A significant portion of the world population does not have access to proper healthcare. The key factor for healthcare's success is the physician's expertise. In this paper, we examine if that expertise can be modeled as an information corpus, a flavor of Big Data and extracted using text mining techniques, particularly using the Vector Space Model, to perform diagnosis. Using cloud and mobile technologies, medical diagnosis can then be made available everywhere there is Internet connectivity, reducing costs, increasing coverage and improving quality of life. The key to the possibility of performing medical diagnosis using an information retrieval approach is the data. This paper therefore focuses on the suitability of the dataset for automating diagnosis using text mining. We use various text mining tools relevant to the Vector Space Model to perform operations on the data to see if meaningful conclusions can be drawn from it. We present some of our observations from the experiments conducted and conclude with future directions.
Medical Diagnosis, Information Retrieval, Machine Learning, Text Mining, Vector Space Model, TF-IDF, Cluster Analysis, K-means
Vishnu S. Pendyala and Silvia Figueira. "Automated Medical Diagnosis from Clinical Data" 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService) (2017): 185-190. https://doi.org/10.1109/BigDataService.2017.14