Medicine and Health Sciences Commons^™

Deep Learning In Clinical Natural Language Processing: A Methodical Review, Stephen Wu, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, Qiong Wang, Qiang Wei, Yang Xiang, Bo Zhao, Hua Xu

Faculty, Staff and Student Publications

OBJECTIVE: This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and context of current research.

MATERIALS AND METHODS: We searched MEDLINE, EMBASE, Scopus, the Association for Computing Machinery Digital Library, and the Association for Computational Linguistics Anthology for articles using DL-based approaches to NLP problems in electronic health records. After screening 1,737 articles, we collected data on 25 variables across 212 papers.

RESULTS: DL in clinical NLP publications more than doubled each year, through 2018. Recurrent neural networks (60.8%) …

A Broad-Spectrum Antiviral Molecule, Ql47, Selectively Inhibits Eukaryotic Translation, Mélissanne De Wispelaere, Margot Carocci, Dominique J Burri, William J Neidermyer, Calla M Olson, Imme Roggenbach, Yanke Liang, Jinhua Wang, Sean P J Whelan, Nathanael S Gray, Priscilla L Yang

Faculty, Staff and Students Publications

Small-molecule inhibitors of translation are critical tools to study the molecular mechanisms of protein synthesis. In this study, we sought to characterize how QL47, a host-targeted, small-molecule antiviral agent, inhibits steady-state viral protein expression. We demonstrate that this small molecule broadly inhibits both viral and host protein synthesis and targets a translation step specific to eukaryotic cells. We show that QL47 inhibits protein neosynthesis initiated by both canonical cap-driven and noncanonical initiation strategies, most likely by targeting an early step in translation elongation. Our findings thus establish QL47 as a new small-molecule inhibitor that can be utilized to probe the …

Prospects And Challenges Of Population Health With Online And Other Big Data In Africa; Understanding The Link To Improving Healthcare Service Delivery, Rowland Edet, Bolarinwa Afolabi

Department of Sociology: Faculty Publications

Big data analytics offers promises to many health care service challenges and can provide answers to many population health issues. Big data is having a positive impact in almost every sphere of life in more advanced world while developing countries are striving to meet up. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa and identify its peculiar needs. The purpose of this review was to summarize the challenges faced by …

Digilego For Peripartum Depression: A Novel Patient-Facing Digital Health Instantiation, J Rodin, C Timko, S Harris

Faculty, Staff and Student Publications

Digital health technologies offer unique opportunities to improve health outcomes for mental health conditions such as peripartum depression (PPD), a disorder that affects approximately 10-15% of women in the U.S. every year. In this paper, we present the adaption of a digital technology development framework, Digilego, in the context of PPD. Methods include mapping of the Behavior Intervention Technology (BIT) model and the Patient Engagement Framework (PEF) to translate patient needs captured through focus groups. This informs formative development and implementation of digital health features for optimal patient engagement in PPD screening and management. Results show an array ofPPD-specific Digilego …

Non-Uniformity Of Projection Distributions Attenuates Resolution In Cryo-Em, Philip R Baldwin, Dmitry Lyumkis

Faculty, Staff and Students Publications

Virtually all single-particle cryo-EM experiments currently suffer from specimen adherence to the air-water interface, leading to a non-uniform distribution in the set of projection views. Whereas it is well accepted that uniform projection distributions can lead to high-resolution reconstructions, non-uniform (anisotropic) distributions can negatively affect map quality, elongate structural features, and in some cases, prohibit interpretation altogether. Although some consequences of non-uniform sampling have been described qualitatively, we know little about how sampling quantitatively affects resolution in cryo-EM. Here, we show how inhomogeneity in any projection distribution scheme attenuates the global Fourier Shell Correlation (FSC) in relation to the number …

Metaproteomic Analysis Of Human Gut Microbiome In Digestive And Metabolic Diseases, Sheng Pan, Ru Chen

Faculty, Staff and Students Publications

Metaproteomics, as a subfield of proteomics, has quickly emerged as a pivotal tool for global characterization of a microbiome system at a functional level. It has been increasingly applied in studying human digestive and metabolic diseases, and provides information-rich data to identify the dysbiosis of human gut microbiome related to healthy or disease states to elucidate the molecular events underlying host-microbiota interplays. While significant technical challenges still exist, this emerging technology has been demonstrated to provide essential information in interrogating functional changes in the human gut microbiome, complementary to metagenomics and metatranscriptomics. This chapter overviews the overall metaproteomic work flow …

Causal Discovery In Radiographic Markers Of Knee Osteoarthritis And Prediction For Knee Osteoarthritis Severity With Attention-Long Short-Term Memory, Yanfei Wang, Lei You, Jacqueline Chyr, Lan Lan, Weiling Zhao, Yujia Zhou, Hua Xu, Philip Noble, Xiaobo Zhou

Faculty, Staff and Student Publications

The goal of this study is to build a prognostic model to predict the severity of radiographic knee osteoarthritis (KOA) and to identify long-term disease progression risk factors for early intervention and treatment. We designed a long short-term memory (LSTM) model with an attention mechanism to predict Kellgren/Lawrence (KL) grade for knee osteoarthritis patients. The attention scores reveal a time-associated impact of different variables on KL grades. We also employed a fast causal inference (FCI) algorithm to estimate the causal relation of key variables, which will aid in clinical interpretability. Based on the clinical information of current visits, we accurately …

Single-Nuclei Rna-Seq On Human Retinal Tissue Provides Improved Transcriptome Profiling, Qingnan Liang, Rachayata Dharmat, Leah Owen, Akbar Shakoor, Yumei Li, Sangbae Kim, Albert Vitale, Ivana Kim, Denise Morgan, Shaoheng Liang, Nathaniel Wu, Ken Chen, Margaret M Deangelis, Rui Chen

Faculty, Staff and Students Publications

Single-cell RNA-seq is a powerful tool in decoding the heterogeneity in complex tissues by generating transcriptomic profiles of the individual cell. Here, we report a single-nuclei RNA-seq (snRNA-seq) transcriptomic study on human retinal tissue, which is composed of multiple cell types with distinct functions. Six samples from three healthy donors are profiled and high-quality RNA-seq data is obtained for 5873 single nuclei. All major retinal cell types are observed and marker genes for each cell type are identified. The gene expression of the macular and peripheral retina is compared to each other at cell-type level. Furthermore, our dataset shows an …

Electrochemical Hydrogen Evolution Over Hydrothermally Synthesized Re-Doped Mos2 Flower-Like Microspheres, Juan Aliaga, Pablo Vera, Juan Araya, Luis Ballesteros, Julio Urzúa, Mario Farías, Francisco Paraguay-Delgado, Gabriel Alonso-Núñez, Guillermo González, Eglantina Benavente

Faculty, Staff and Students Publications

In this research, we report a simple hydrothermal synthesis to prepare rhenium (Re)- doped MoS2 flower-like microspheres and the tuning of their structural, electronic, and electrocatalytic properties by modulating the insertion of Re. The obtained compounds were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). Structural, morphological, and chemical analyses confirmed the synthesis of poorly crystalline Re-doped MoS2 flower-like microspheres composed of few stacked layers. They exhibit enhanced hydrogen evolution reaction (HER) performance with low overpotential of 210 mV at current density of 10 mA/cm2, with a …

Enhancing Clinical Concept Extraction With Contextual Embeddings, Yuqi Si, Jingqi Wang, Hua Xu, Kirk Roberts

Faculty, Staff and Student Publications

OBJECTIVE: Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText).

MATERIALS AND METHODS: Both off-the-shelf, open-domain embeddings and …

Microrna-374a, -4680, And -133b Suppress Cell Proliferation Through The Regulation Of Genes Associated With Human Cleft Palate In Cultured Human Palate Cells, Akiko Suzuki, Aimin Li, Mona Gajera, Nada Abdallah, Musi Zhang, Zhongming Zhao, Junichi Iwata

Faculty, Staff and Student Publications

BACKGROUND: Cleft palate (CP) is the second most common congenital birth defect; however, the relationship between CP-associated genes and epigenetic regulation remains largely unknown. In this study, we investigated the contribution of microRNAs (miRNAs) to cell proliferation and regulation of genes involved in CP development.

METHODS: In order to identify all genes for which mutations or association/linkage have been found in individuals with CP, we conducted a systematic literature search, followed by bioinformatics analyses for these genes. We validated the bioinformatics results experimentally by conducting cell proliferation assays and miRNA-gene regulatory analyses in cultured human palatal mesenchymal cells treated with …

Turf For Teams: Considering Both The Team And I In The Work-Centered Design Of Systems, Vickie Nguyen

Dissertations and Theses (Open Access)

Teams are an inherent part of many work domains, especially in the healthcare environment. Yet, most systems are often built with only the individual user in mind. How can we better incorporate the team, as a user, into the design of a system? By better understanding the team, through their user, task, representational, and functional needs, we can create more useful and helpful systems that match their work domain. For this research project, we utilize the TURF framework and expanded it further by also considering teams as a user, thus, creating the TURF for Teams framework. In addition, we chose …

Deep Learning Enables Robust Assessment And Selection Of Human Blastocysts After In Vitro Fertilization, Pegah Khosravi, Ehsan Kazemi, Qiansheng Zhan, Jonas E. Malmsten, Marco Toschi, Pantelis Zisimopoulos, Alexandros Sigaras, Stuart Lavery, Lee A. D. Cooper, Cristina Hickman, Marcos Meseguer, Zev Rosenwaks, Olivier Elemento, Nikica Zaninovic, Iman Hajirasouliha

Publications and Research

Visual morphology assessment is routinely used for evaluating of embryo quality and selecting human blastocysts for transfer after in vitro fertilization (IVF). However, the assessment produces different results between embryologists and as a result, the success rate of IVF remains low. To overcome uncertainties in embryo quality, multiple embryos are often implanted resulting in undesired multiple pregnancies and complications. Unlike in other imaging fields, human embryology and IVF have not yet leveraged artificial intelligence (AI) for unbiased, automated embryo assessment. We postulated that an AI approach trained on thousands of embryos can reliably predict embryo quality without human intervention. We …

Behavioral Analysis Of Zebrafish (Danio Rerio) As A Model For Bjornstad Syndrome, Luke Schellenberg, Amy Wilstermann, Rachael Baker

Summer Research

Bjornstad Syndrome

• Autosomal recessive disease caused by single mutations in the BCS1L gene
• Characterized by sensorineural hearing loss and pili torti (brittle hair susceptible to falling out)

Zebrafish as a Model

• 70% of the same genes as humans, 84% of the same genes associated with human disease
• High fecundity and quick development

Deep Patient Representation Of Clinical Notes Via Multi-Task Learning For Mortality Prediction, Yuqi Si, Kirk Roberts

Faculty, Staff and Student Publications

We propose a deep learning-based multi-task learning (MTL) architecture focusing on patient mortality predictions from clinical notes. The MTL framework enables the model to learn a patient representation that generalizes to a variety of clinical prediction tasks. Moreover, we demonstrate how MTL enables small but consistent gains on a single classification task (e.g., in-hospital mortality prediction) simply by incorporating related tasks (e.g., 30-day and 1-year mortality prediction) into the MTL framework. To accomplish this, we utilize a multi-level Convolutional Neural Network (CNN) associated with a MTL loss component. The model is evaluated with 3, 5, and 20 tasks and is …

Melatonin Enhances Sorafenib-Induced Cytotoxicity In Flt3-Itd Acute Myeloid Leukemia Cells By Redox Modification, Tian Tian, Jiajun Li, Yizhuo Li, Yun-Xin Lu, Yan-Lai Tang, Hua Wang, Fufu Zheng, Dingbo Shi, Qian Long, Miao Chen, Guillermo Garcia-Manero, Yumin Hu, Lijun Qin, Wuguo Deng

Faculty, Staff and Student Publications

Acute myeloid leukemia (AML) with an internal tandem duplication in Fms-related tyrosine kinase 3 (FLT3-ITD) is identified as a subgroup with poor outcome and intrinsic resistance to chemotherapy and therefore urgent need for development of novel therapeutic strategies.

Methods: The antitumor effects of melatonin alone or combined with sorafenib were evaluated via flow cytometry and immunoblotting assays in FLT-ITD AML cells. Also, the ex vivo and in vivo models were used to test the synergistic effects of melatonin and sorafenib against leukemia with FLT3/ITD mutation.

Results: Our study shows for the first time that melatonin inhibits proliferation and induces apoptosis …

Effects Of A Community Population Health Initiative On Blood Pressure Control In Latinos, James R Langabeer, Timothy D Henry, Carlos Perez Aldana, Larissa Deluna, Nora Silva, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

Background Hypertension remains one of the most important, modifiable cardiovascular risk factors. Yet, the largest minority ethnic group (Hispanics/Latinos) often have different health outcomes and behavior, making hypertension management more difficult. We explored the effects of an American Heart Association-sponsored population health intervention aimed at modifying behavior of Latinos living in Texas. Methods and Results We enrolled 8071 patients, and 5714 (65.7%) completed the 90-day program (58.5 years ±11.7; 59% female) from July 2016 to June 2018. Navigators identified patients with risk factors; initial and final blood pressure ( BP ) readings were performed in the physician's office; and interim …

A Frame-Based Nlp System For Cancer-Related Information Extraction, Yuqi Si, Kirk Roberts

Faculty, Staff and Student Publications

We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep learning-based approach, bidirectional Long Short-term Memory (LSTM) Conditional Random Field (CRF), which uses both character and word embeddings. The system consists of two constituent sequence classifiers: a frame identification (lexical unit) classifier and a frame element classifier. The classifier achieves an F

Metatranscriptome Of Human Faecal Microbial Communities In A Cohort Of Adult Men, Galeb S. Abu-Ali, Raaj S. Mehta, Jason Lloyd-Price, Himel Mallick, Tobyn Branck, Kerry L. Ivey, David A. Drew, Casey Dulong, Eric Rimm, Jacques Izard, Andrew T. Chan, Curtis Huttenhower

Department of Food Science and Technology: Faculty Publications

The gut microbiome is intimately related to human health, but it is not yet known which functional activities are driven by specific microorganisms' ecological configurations or transcription. We report a large-scale investigation of 372 human fecal metatranscriptomes and 929 metagenomes from a subset of 308 men in the Health Professionals Follow-Up Study. We identified a metatranscriptomic 'core' universally transcribed over time and across participants, often by different microorganisms. In contrast to the housekeeping functions enriched in this core, a 'variable' metatranscriptome included specialized pathways that were differentially expressed both across participants and among microorganisms. Finally, longitudinal metagenomic profiles allowed ecological …

Deep Convolutional Neural Networks Enable Discrimination Of Heterogeneous Digital Pathology Images, Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha

Publications and Research

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error.

In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer.

In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry …

The Ability Of Different Imputation Methods To Preserve The Significant Genes And Pathways In Cancer, Rosa Aghdam, Taban Baghfalaki, Pegah Khosravi, Elnaz Saberi Ansari

Publications and Research

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase …

Regulation Of Hnrnpa1 By Micrornas Controls The Mir-18a–K-Ras Axis In Chemotherapy-Resistant Ovarian Cancer, Cristian Rodriguez-Aguayo, Paloma Del C Monroig, Roxana S Redis, Emine Bayraktar, Maria I Almeida, Cristina Ivan, Enrique Fuentes-Mattei, Mohammed H Rashed, Arturo Chavez-Reyes, Bulent Ozpolat, Rahul Mitra, Anil K Sood, George A Calin, Gabriel Lopez-Berestein

Faculty, Staff and Student Publications

The regulation of microRNA (miRNA) biogenesis, function and degradation involves a range of mechanisms, including interactions with RNA-binding proteins. The potential contribution of regulatory miRNAs to the expression of these RNA interactor proteins that could control other miRNAs expression is still unclear. Here we demonstrate a regulatory circuit involving oncogenic and tumor-suppressor miRNAs and an RNA-binding protein in a chemotherapy-resistant ovarian cancer model. We identified and characterized miR-15a-5p and miR-25-3p as negative regulators of hnRNPA1 expression, which is required for the processing of miR-18a-3p, an inhibitor of the

A Dynamical Systems Model Of Progesterone Receptor Interactions With Inflammation In Human Parturition, Douglas Brubaker, Mark R. Chance, Sam Mesiano

Faculty Scholarship

Background: Progesterone promotes uterine relaxation and is essential for the maintenance of pregnancy. Withdrawal of progesterone activity and increased inflammation within the uterine tissues are key triggers for parturition. Progesterone actions in myometrial cells are mediated by two progesterone receptor (PR) isoforms, PR-A and PR-B, that function as ligand-activated transcription factors. PR-B mediates relaxatory actions of progesterone, in part, by decreasing myometrial cell responsiveness to pro-inflammatory stimuli. These same pro-inflammatory stimuli promote the expression of PR-A which inhibits the anti-inflammatory activity of PR-B. Competitive interaction between the progesterone receptors then augments myometrial responsiveness to pro-inflammatory stimuli. The interaction between PR-B …

Inferring Interaction Type In Gene Regulatory Networks Using Co-Expression Data, Pegah Khosravi, Vahid H. Gazestani, Leila Pirhaji, Brian Law, Mehdi Sadeghi, Bahram Goliaei, Gary D. Bader

Publications and Research

Background

Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction.

Results

This paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark …

Relating The Metatranscriptome And Metagenome Of The Human Gut, Eric A. Franzosa, Xochitl C. Morgan, Nicola Segata, Levi Waldron, Joshua Reyes, Ashlee M. Earl, Georgia Giannoukos, Matthew R. Boylan, Dawn Ciulla, Dirk Gevers, Jacques Izard, Wendy S. Garrett, Andrew T. Chan, Curtis Huttenhower

Department of Food Science and Technology: Faculty Publications

Although the composition of the human microbiome is now well-studied, the microbiota’s > 8 million genes and their regulation remain largely uncharacterized. This knowledge gap is in part because of the difficulty of acquiring large numbers of samples amenable to functional studies of the microbiota. We conducted what is, to our knowledge, one of the first human microbiome studies in a well-phenotyped prospective cohort incorporating taxonomic, metagenomic, and metatranscriptomic profiling at multiple body sites using self-collected samples. Stool and saliva were provided by eight healthy subjects, with the former preserved by three different methods (freezing, ethanol, and RNAlater) to validate self-collection. …

Metabolic Reconstruction For Metagenomic Data And Its Application To The Human Microbiome, Sahar Abubucker, Nicola Segata, Johannes Goll, Alyxandria M. Schubert, Jacques Izard, Brandi L. Cantarel, Beltran Rodriguez-Mueller, Jeremy Zucker, Mathangi Thiagarajan, Bernard Henrissat, Owen White, Scott T. Scott, Barbara Methé, Patrick D. Schloss, Dirk Gever, Makedonka Mitreva, Curtis Huttenhower

Department of Food Science and Technology: Faculty Publications

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities’ metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as …

Comparison Of Clinical Knowledge Management Capabilities Of Commercially-Available And Leading Internally-Developed Electronic Health Records, Dean F Sittig, Adam Wright, Seth Meltzer, Linas Simonaitis, R Scott Evans, W Paul Nichol, Joan S Ash, Blackford Middleton

Faculty, Staff and Student Publications

BACKGROUND: We have carried out an extensive qualitative research program focused on the barriers and facilitators to successful adoption and use of various features of advanced, state-of-the-art electronic health records (EHRs) within large, academic, teaching facilities with long-standing EHR research and development programs. We have recently begun investigating smaller, community hospitals and out-patient clinics that rely on commercially-available EHRs. We sought to assess whether the current generation of commercially-available EHRs are capable of providing the clinical knowledge management features, functions, tools, and techniques required to deliver and maintain the clinical decision support (CDS) interventions required to support the recently defined …

What Is Biomedical Informatics?, Elmer V Bernstam, Jack W Smith, Todd R Johnson

Faculty, Staff and Student Publications

Biomedical informatics lacks a clear and theoretically-grounded definition. Many proposed definitions focus on data, information, and knowledge, but do not provide an adequate definition of these terms. Leveraging insights from the philosophy of information, we define informatics as the science of information, where information is data plus meaning. Biomedical informatics is the science of information as applied to or studied in the context of biomedicine. Defining the object of study of informatics as data plus meaning clearly distinguishes the field from related fields, such as computer science, statistics and biomedicine, which have different objects of study. The emphasis on data …

Sequential Incoherence In A Multi-Party Synchronous Computer Mediated Communication For An Introductory Health Informatics Course, Jorge R Herskovic, J Caleb Goodwin, Pamela A Bozzo Silva, Irmgard Willcockson, Amy Franklin

Faculty, Staff and Student Publications

Online courses will play a key role in the high-volume Informatics education required to train the personnel that will be necessary to fulfill the health IT needs of the country. Online courses can cause feelings of isolation in students. A common way to address these feelings is to hold synchronous online "chats" for students. Conventional chats, however, can be confusing and impose a high extrinsic cognitive load on their participants that hinders the learning process. In this paper we present a qualitative analysis that shows the causes of this high cognitive load and our solution through the use of a …

Can Prospective Usability Evaluation Predict Data Errors?, Constance M Johnson, Meredith Nahm, Ryan J Shaw, Ashley Dunham, Kristin Newby, Rowena Dolor, Michelle Smerek, Guilherme Del Fiol, Jiajie Zhang

Faculty, Staff and Student Publications

Increasing amounts of clinical research data are collected by manual data entry into electronic source systems and directly from research subjects. For this manual entered source data, common methods of data cleaning such as post-entry identification and resolution of discrepancies and double data entry are not feasible. However data accuracy rates achieved without these mechanisms may be higher than desired for a particular research use. We evaluated a heuristic usability method for utility as a tool to independently and prospectively identify data collection form questions associated with data errors. The method evaluated had a promising sensitivity of 64% and a …