Abstract
In a medical appointment, patient information, including past exams, is analyzed in order to define a diagnosis. This process is prone to errors, since there may be many possible diagnoses. This analysis is very dependent on the experience of the doctor. Even with the correct diagnosis, prescribing medicines can be a problem, because there are multiple drugs for each disease and some may not be used due to allergies or high cost. Therefore, it would be helpful, if the doctors were able to use a system that, for each diagnosis, provided a list of the most suitable medicines. Our approach is to support the physician in this process. Rather than trying to predict the medicine, we aim to, given the available information, predict the set of the most likely drugs. The prescription problem may be solved as a Multi-Label classification problem since, for each diagnosis, multiple drugs may be prescribed at the same time. Due to its complexity, some simplifications were performed for the problem to be treatable. So, multiple approaches were done with different assumptions. The data supplied was also complex, with important problems in its quality, that led to a strong investment in data preparation, in particular, feature engineering. Overall, the results in each scenario are good with performances almost twice the baseline, especially using Binary Relevance as transformation approach.
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I would like to thank Glintt - Healthcare Solutions S.a., who provided data and expertise that greatly assisted the development of this work.
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Silva, P., Rivolli, A., Rocha, P., Correia, F., Soares, C. (2018). Machine Learning for Drugs Prescription. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_57
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