Natural Language Processing Methods to Extract Lifestyle Exposures for Alzheimer’s Disease from Clinical Notes

Due to the absence of medications on Alzheimer’s disease (AD), lifestyle exposures that could improve cognitive functionality have become extremely important. Thus, the objective of the study was to show the feasibility of using natural language processing (NLP) methods to extract lifestyle exposures from clinical texts. The proposed named-entity recognition (NER) task’s results indicate that NLP models can detect lifestyle information (i.e., excessive diet, physical activity, sleep deprivation and substance abuse) from clinical notes, which has the potential for improving efficiency in information acquisition and accrual for AD clinical trials.