Review of Temporal Reasoning in the Clinical Domain for Timeline Extraction: Where we are and where we need to be

Understanding a patient's medical history, such as how long symptoms last or when a procedure was performed, is vital to diagnosing problems and providing good care. Frequently, important information regarding a patient's medical timeline is buried in their Electronic Health Record (EHR) in the form of unstructured clinical notes. This results in care providers spending time reading notes in a patient's record in order to become familiar with their condition prior to developing a diagnosis or treatment plan. Valuable time could be saved if this information was readily accessible for searching and visualization for fast comprehension by the medical team. Clinical Natural Language Processing (NLP) is an area of research that aims to build computational methods to automatically extract medically relevant information from unstructured clinical texts. A key component of Clinical NLP is Temporal Reasoning, as understanding a patient's medical history relies heavily on the ability to identify, assimilate, and reason over temporal information. In this work, we review the current state of Temporal Reasoning in the clinical domain with respect to Clinical Timeline Extraction. While much progress has been made, the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Areas such as handling relative and implicit temporal expressions, both in normalization and in identifying temporal relationships, improving co-reference resolution, and building inter-operable timeline extraction tools that can integrate multiple types of data are in need of new and innovative solutions to improve performance on clinical data.