Doctor of Philosophy

dissertationManual annotation of clinical texts is often used as a method of generating reference standards that provide data for training and evaluation of Natural Language Processing (NLP) systems. Manually annotating clinical texts is time consuming, expensive, and requires considerable cognitive effort on the part of human reviewers. Furthermore, reference standards must be generated in ways that produce consistent and reliable data but must also be valid in order to adequately evaluate the performance of those systems. The amount of labeled data necessary varies depending on the level of analysis, the complexity of the clinical use case, and the methods that will be used to develop automated machine systems for information extraction and classification. Evaluating methods that potentially reduce cost, manual human workload, introduce task efficiencies, and reduce the amount of labeled data necessary to train NLP tools for specific clinical use cases are active areas of research inquiry in the clinical NLP domain. This dissertation integrates a mixed methods approach using methodologies from cognitive science and artificial intelligence with manual annotation of clinical texts. Aim 1 of this dissertation identifies factors that affect manual annotation of clinical texts. These factors are further explored by evaluating approaches that may introduce efficiencies into manual review tasks applied to two different NLP development areas - semantic annotation of clinical concepts and identification of information representing Protected Health Information (PHI) as defined by HIPAA. Both experiments integrate iv different priming mechanisms using noninteractive and machine-assisted methods. The main hypothesis for this research is that integrating pre-annotation or other machineassisted methods within manual annotation workflows will improve efficiency of manual annotation tasks without diminishing the quality of generated reference standards

Immediate chromatin immunoprecipitation and on-bead quantitative PCR analysis: A versatile and rapid ChIP procedure

© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research. Genome-wide chromatin immunoprecipitation (ChIP) studies have brought significant insight into the genomic localization of chromatin-associated proteins and histone modifications. The large amount of data generated by these analyses, however, require approaches that enable rapid validation and analysis of biological relevance. Furthermore, there are still protein and modification targets that are difficult to detect using standard ChIP methods. To address these issues, we developed an immediate chromatin immunoprecipitation procedure which we call ZipChip. ZipChip significantly reduces the time and increases sensitivity allowing for rapid screening of multiple loci. Here we describe how ZipChIP enables detection of histone modifications (H3K4 mono- and trimethylation) and two yeast histone demethylases, Jhd2 and Rph1, which were previously difficult to detect using standard methods. Furthermore, we demonstrate the versatility of ZipChIP by analyzing the enrichment of the histone deacetylase Sir2 at heterochromatin in yeast and enrichment of the chromatin remodeler, PICKLE, at euchromatin in Arabidopsis thaliana

2018-2019 Philharmonia No. 5

Concert Date & Time: February 23, 2019 at 7:30 PM and February 24, 2019 at 4:00 PM Program Concerto for Clarinet, Strings and Harp / Aaron Copland Te Deum, op. 22 / Hector Berliozhttps://spiral.lynn.edu/conservatory_philharmonia/1140/thumbnail.jp

2022-2023 Philharmonia with the Master Chorale of South Florida

Concert Dates & Time March 30, 2023, 8:00 P.M. at the Parker, Fort Lauderdale April 1, 2023. 8:00 P.M. at the Wold Performing Arts Center April 2, 2023. 8:00 P.M. at the Wold Performing Arts Center Program Messa da Requiem / Giuseppe Verdihttps://spiral.lynn.edu/conservatory_philharmonia/1167/thumbnail.jp

2019-2020 Philharmonia No. 4

Concert Date & Time: February 22, 2020 at 7:30 pm and February 23, 2020 at 4:00 pm Program Short Ride in a Fast Machine / John Adams Lincoln Portrait / Aaron Copland Jon Robertson, narrator Symphony No. 9 in D minor, Op. 125 / Ludwig van Beethoven Sherezade Panthaki, soprano Rebecca Robinson, mezzo-soprano Robert Stahley, tenor Adrian Smith, baritone The Master Chorale of South Florida Brett Karlin, artistic directorhttps://spiral.lynn.edu/conservatory_philharmonia/1150/thumbnail.jp

Task 2: ShARe/CLEF eHealth evaluation lab 2014

This paper reports on Task 2 of the 2014 ShARe/CLEF eHealth evaluation lab which extended Task 1 of the 2013 ShARe/CLEF eHealth evaluation lab by focusing on template lling of disorder attributes. The task was comprised of two subtasks: attribute normalization (task 2a) and cue identication (task 2b).We instructed participants to develop a system which either kept or updated a default attribute value for each task. Participant systems were evaluated against a blind reference standard of 133 discharge summaries using Accuracy (task 2a) and F-score (task 2b). In total, ten teams participated in task 2a, and three teams in task 2b. For task 2a and 2b, the HITACHI team systems (run 2) had the highest performances, with an overall average average accuracy of 0.868 and F1-score (strict) of 0.676, respectively

Overview of the ShARe/CLEF eHealth evaluation lab 2013

Discharge summaries and other free-text reports in healthcare transfer information between working shifts and geographic locations. Patients are likely to have difficulties in understanding their content, because of their medical jargon, non-standard abbreviations, and ward-specific idioms. This paper reports on an evaluation lab with an aim to support the continuum of care by developing methods and resources that make clinical reports in English easier to understand for patients, and which helps them in finding information related to their condition. This ShARe/CLEFeHealth2013 lab offered student mentoring and shared tasks: identification and normalisation of disorders (1a and 1b) and normalisation of abbreviations and acronyms (2) in clinical reports with respect to terminology standards in healthcare as well as information retrieval (3) to address questions patients may have when reading clinical reports. The focus on patients' information needs as opposed to the specialised information needs of physicians and other healthcare workers was the main feature of the lab distinguishing it from previous shared tasks. De-identied clinical reports for the three tasks were from US intensive care and originated from the MIMIC II database. Other text documents for Task 3 were from the Internet and originated from the Khresmoi project. Task 1 annotations originated from the ShARe annotations. For Tasks 2 and 3, new annotations, queries, and relevance assessments were created. 64, 56, and 55 people registered their interest in Tasks 1, 2, and 3, respectively. 34 unique teams (3 members per team on average) participated with 22, 17, 5, and 9 teams in Tasks 1a, 1b, 2 and 3, respectively. The teams were from Australia, China, France, India, Ireland, Republic of Korea, Spain, UK, and USA. Some teams developed and used additional annotations, but this strategy contributed to the system performance only in Task 2. The best systems had the F1 score of 0.75 in Task 1a; Accuracies of 0.59 and 0.72 in Tasks 1b and 2; and Precision at 10 of 0.52 in Task 3. The results demonstrate the substantial community interest and capabilities of these systems in making clinical reports easier to understand for patients. The organisers have made data and tools available for future research and development