Temporal disambiguation of relative temporal expressions in clinical texts using temporally fine-tuned contextual word embeddings.

Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Much of the current work in this field is focused on direct and explicit temporal expressions and identifying temporal relations. However, there is little work focused on relative temporal expressions, which can be difficult to normalize, but are vital to ordering events on a timeline. This work introduces a new temporal expression recognition and normalization tool, Chrono, that normalizes temporal expressions into both SCATE and TimeML schemes. Chrono advances clinical timeline extraction as it is capable of identifying more vague and relative temporal expressions than the current state-of-the-art and utilizes contextualized word embeddings from fine-tuned BERT models to disambiguate temporal types, which achieves state-of-the-art performance on relative temporal expressions. In addition, this work shows that fine-tuning BERT models on temporal tasks modifies the contextualized embeddings so that they achieve improved performance in classical SVM and CNN classifiers. Finally, this works provides a new tool for linking temporal expressions to events or other entities by introducing a novel method to identify which tokens an entire temporal expression is paying the most attention to by summarizing the attention weight matrices output by BERT models

Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi

Spectators’ aesthetic experiences of sound and movement in dance performance

In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences

The social brain: neural basis of social knowledge

Social cognition in humans is distinguished by psychological processes that allow us to make inferences about what is going on inside other people—their intentions, feelings, and thoughts. Some of these processes likely account for aspects of human social behavior that are unique, such as our culture and civilization. Most schemes divide social information processing into those processes that are relatively automatic and driven by the stimuli, versus those that are more deliberative and controlled, and sensitive to context and strategy. These distinctions are reflected in the neural structures that underlie social cognition, where there is a recent wealth of data primarily from functional neuroimaging. Here I provide a broad survey of the key abilities, processes, and ways in which to relate these to data from cognitive neuroscience

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