Recommending treatments for comorbid patients using word-based and phrase-based alignment methods
Elie Merhej, Steven Schockaert, T. Greg McKelvey, Martine De Cock · ORCA Online Research @Cardiff (Cardiff University) · 2017
The problem of finding treatments for patients diagnosed with multiple diseases (i.e.~a comorbidity) is an important research topic in the medical literature. In this paper, we propose a new data driven approach to recommend treatments for these comorbidities using word-based and phrase-based alignment methods. The most popular methods currently rely on combining specific information from individual diseases (e.g.~procedures, tests, etc.), then aim to detect and repair the conflicts that arise in the combined treatments. This proves to be a challenge especially in the cases where the studied comorbidities contain large numbers of diseases. In contrast, our methods rely on training a translation model using previous medical records to find treatments for newly diagnosed comorbidities. We also explore the use of additional criteria in the form of a drug interactions penalty and a treatment popularity score to select the best treatment in the case where multiple valid translations for a single comorbidity are available.