This module is dedicated to processing electronic medical records for meaningful events, temporal expressions, and their relations on a timeline.

Temporal relations are of prime importance in biomedicine as they are intrinsically linked to diseases, signs and symptoms, and treatments. The identification of temporal relations in medical text has drawn growing attention because of its potential to dramatically increase the understanding of many medical phenomena such as disease progression, longitudinal effects of medications, and a patient's clinical course, and its many clinical applications such as question answering[1, 2], clinical outcomes prediction[3], and the recognition of temporal patterns and timelines[4, 5].

Event annotator
A Begin-Inside-Outside (BIO) style sequence annotator for clinically meaningful events, i.e. anything that would show up on a detailed timeline of the patient’s care or life.

Temporal expression annotators
A series of BIO style sequence annotators that employed forward and backward search algorithms and multiple learning methods (Support Vector Machine (SVM), Conditional Random Field (CRV) ) for annotating temporal expressions which would provide concrete temporal references throughout the document or section, e.g. “today”, “24 hours ago”, “postoperative”. More details can be found in [6].

DocTimeRel annotator
For every event, there is an SVM-based annotator that can automatically reason the temporal relation between the target event and the document creation time (DCT). This module provided a basic and stable temporal solution that can position all events into coarse temporal bins, e.g. “before the DCT”, “after the DCT”, or “overlap the DCT”. This annotator has proved helpful in solving real clinical temporal-sensitive tasks for multiple institutions [5].

Temporal relation (TLINK) annotators
SVM-based annotators for detecting within-sentence Event-Time relations and Event-Event relations. For i2b2 datasets there are also cross sentence Event-Time and Event-Event relation annotators. Multiple techniques have been implemented, including narrative container-based annotation concept [7], tree kernels [8] for syntactic similarity measurement, multi-layered temporal modeling [9], event expansion [10], and deep neural network methods [11, 12].

All the above annotators were trained and tested on colon cancer notes from the THYME data set [14].