New research article: “Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes”

Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges, in particular, to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, and different date formats cause ambiguity that has to be solved to reconstruct the patient’s natural history. In this paper, the authors concentrate on extracting from clinical narratives the cancer diagnosis and relating it to the diagnosis date. To address this challenge, a hybrid approach that combines deep learning-based and rule-based methods is proposed. The approach integrates three steps: lung cancer named entity recognition, negation and speculation detection, and relating the cancer diagnosis to a valid date.

This study, conducted by researchers from Universidad Politécnica de Madrid and Hospital Universitario Puerta de Hierro, has been published in Applied Sciences, an international peer-reviewed open access journal.