Abstract
Every year thousands of people get their diagnoses wrongly, and several patients have their health conditions aggravated due to misdiagnosis. This problem is even more challenging when the list of possible diseases is long, as in a general medicine speciality. The development of Artificial Intelligence (AI) medical diagnosis systems could prevent misdiagnosis when clinicians are in doubt. We developed an AI system to help clinicians in their daily practice. They could consult the system to get an immediate opinion and diminish waiting times in triage services since this task could be carried out with minimal human interaction. Our method relies on Machine Learning techniques, more precisely on Active Learning and Neural Networks classifiers. To train this model, we used a data set that relates symptoms to several diseases. We compared our models with other models from the literature, and our results show that it is possible to achieve even better performance with much less data, mainly because of the contribution of the Active Learning component.
This work is funded by the FCT - Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R &D Unit - UIDB/00326/2020 or project code UIDP/00326/2020.
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Pinto, C., Faria, J., Macedo, L. (2022). An Active Learning-Based Medical Diagnosis System. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_18
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