EliXR-TIME: A Temporal Knowledge Representation for Clinical Research Eligibility Criteria.

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria

Temporal relation discovery between events and temporal expressions identified in clinical narrative

AbstractThe automatic detection of temporal relations between events in electronic medical records has the potential to greatly augment the value of such records for understanding disease progression and patients’ responses to treatments. We present a three-step methodology for labeling temporal relations using machine learning and deterministic rules over an annotated corpus provided by the 2012 i2b2 Shared Challenge. We first create an expanded training network of relations by computing the transitive closure over the annotated data; we then apply hand-written rules and machine learning with a feature set that casts a wide net across potentially relevant lexical and syntactic information; finally, we employ a voting mechanism to resolve global contradictions between the local predictions made by the learned classifier. Results over the testing data illustrate the contributions of initial prediction and conflict resolution

Recognition and normalization of temporal expressions in Serbian medical narratives

The temporal dimension emerges as one of the essential concepts in the field of medicine, providing a basis for the proper interpretation and understanding of medically relevant information, often recorded only in unstructured texts. Automatic processing of temporal expressions involves their identification and formalization in a language understandable to computers. This paper aims to apply the existing system for automatic processing of temporal expressions in Serbian natural language texts to medical narrative texts, to evaluate the system’s efficiency in recognition and normalization of temporal expressions and to determine the degree of necessary adaptation according to the characteristics and requirements of the medical domain

From temporal expressions to symptom onset date identification in emergency department notes: a temporal information extraction process

A patient's visit to the Emergency Department (ED) starts with the triage nurse making a note of the patient's account of the reason for the visit. This triage note (TN) contains symptoms the patient is suffering from, prior treatments if any, related events and sometimes the nurse's evaluation of the situation. Public health officials may use these TNs to identify features of disease outbreaks. Here we present a system that processes triage notes, producing a timeline of events leading to the ED visit and identifying patterns in occurrence of symptoms across patients. This system is designed as an initial step in the process of automatically extracting signals/symptoms defining a disease outbreak based on the details (symptoms and temporal information) associated with ED visits

Improving Syntactic Parsing of Clinical Text Using Domain Knowledge

Syntactic parsing is one of the fundamental tasks of Natural Language Processing (NLP). However, few studies have explored syntactic parsing in the medical domain. This dissertation systematically investigated different methods to improve the performance of syntactic parsing of clinical text, including (1) Constructing two clinical treebanks of discharge summaries and progress notes by developing annotation guidelines that handle missing elements in clinical sentences; (2) Retraining four state-of-the-art parsers, including the Stanford parser, Berkeley parser, Charniak parser, and Bikel parser, using clinical treebanks, and comparing their performance to identify better parsing approaches; and (3) Developing new methods to reduce syntactic ambiguity caused by Prepositional Phrase (PP) attachment and coordination using semantic information. Our evaluation showed that clinical treebanks greatly improved the performance of existing parsers. The Berkeley parser achieved the best F-1 score of 86.39% on the MiPACQ treebank. For PP attachment, our proposed methods improved the accuracies of PP attachment by 2.35% on the MiPACQ corpus and 1.77% on the I2b2 corpus. For coordination, our method achieved a precision of 94.9% and a precision of 90.3% for the MiPACQ and i2b2 corpus, respectively. To further demonstrate the effectiveness of the improved parsing approaches, we applied outputs of our parsers to two external NLP tasks: semantic role labeling and temporal relation extraction. The experimental results showed that performance of both tasks’ was improved by using the parse tree information from our optimized parsers, with an improvement of 3.26% in F-measure for semantic role labelling and an improvement of 1.5% in F-measure for temporal relation extraction

Doctor of Philosophy

dissertationThe primary objective of cancer registries is to capture clinical care data of cancer populations and aid in prevention, allow early detection, determine prognosis, and assess quality of various treatments and interventions. Furthermore, the role of cancer registries is paramount in supporting cancer epidemiological studies and medical research. Existing cancer registries depend mostly on humans, known as Cancer Tumor Registrars (CTRs), to conduct manual abstraction of the electronic health records to find reportable cancer cases and extract other data elements required for regulatory reporting. This is often a time-consuming and laborious task prone to human error affecting quality, completeness and timeliness of cancer registries. Central state cancer registries take responsibility for consolidating data received from multiple sources for each cancer case and to assign the most accurate information. The Utah Cancer Registry (UCR) at the University of Utah, for instance, leads and oversees more than 70 cancer treatment facilities in the state of Utah to collect data for each diagnosed cancer case and consolidate multiple sources of information.Although software tools helping with the manual abstraction process exist, they mainly focus on cancer case findings based on pathology reports and do not support automatic extraction of other data elements such as TNM cancer stage information, an important prognostic factor required before initiating clinical treatment. In this study, I present novel applications of natural language processing (NLP) and machine learning (ML) to automatically extract clinical and pathological TNM stage information from unconsolidated clinical records of cancer patients available at the central Utah Cancer Registry. To further support CTRs in their manual efforts, I demonstrate a new approach based on machine learning to consolidate TNM stages from multiple records at the patient level