Research Area: Machine Learning
During the last decades, a huge amount of data have been collected in clinical databases in the form of medical reports, laboratory results, treatment plans, etc., representing patients health status. Hence, digital information available for patient-oriented decision making has increased drastically but it is often not mined and analyzed in depth since: (i) medical documents are often unstructured and therefore difficult to analyze automatically, (ii) doctors traditionally rely on their experience to recognize an illness, give a diagnosis, and prescribe medications. However doctors experience can be limited by the cases they are treated so far and medication errors can occur frequently. In addition, it is generally hard and time-consuming inferring information for comparing unstructured data and evaluating similarities between heterogeneous resources. Technologies as Data Mining, Natural Language Processing, and Machine Learning can provide possibilities to explore and exploit potential knowledge from diagnosis history records and help doctors to prescribe medication correctly to decrease medication error effectively. In this paper, we design and implement a medical recommender system that is able to cluster a collection of medical reports on features detected by IBM Watson and Framester, two emerging tools from, respectively, Cognitive Computing and Frame Semantics, and then, giving a medical report from a specific patient as input, to recommend similar other medical reports from patients who had analogues symptoms. Experiments and results have proved the quality of the resulting clustering and recommendations, and the key role that these innovative services can play on the biomedical sector. The proposed system is able to classify new medical cases thus supporting physicians to take more correct and reliable actions about specific diagnosis and cares.
Keywords:
medical reports
laboratory results
treatment plans
data mining
natural language processing
machine learning
cognitive computing
frame semantics
Author(s) Name: Danilo Dessì, Diego Reforgiato Recupero, Gianni Fenu, Sergio Consoli
Journal name: Machine Learning Paradigms
Conferrence name:
Publisher name: Springer
DOI: https://doi.org/10.1007/978-3-319-94030-4_2
Volume Information: Volume 149
