Data Science Commons^™

An Association Test For Ordinal Outcomes In Clustered Data With Informative Cluster Size, Hasika K. Wickrama Senevirathne, Sandipan Dutta

Mathematics & Statistics Faculty Publications

In cluster-correlated data, the number of observations in a cluster can be associated with the outcome from that cluster. This phenomenon is known as informative cluster size which can occur in cluster-randomized clinical trial data. Several studies have found that ignoring the issue of informative cluster size can produce biased results in the analysis of clustered data. Most of the existing methods for addressing informative cluster size are suited to continuous outcomes. However, ordinal outcomes and covariates are often encountered in clustered data obtained from large clinical studies. The existing methods for ordinal association testing in clustered data can produce …

Differences In Biologic Clinical Trials For Chronic Rhinosinusitis With Nasal Polyps—Are We Comparing Apples With Oranges?, Marjolein Cornet, Peter W. Hellings, Martin Desrosiers, Martin Wagenmann, Richard Follows, Laura Walrave, Luz Adriana Jimenez, Lee Tombs, Dawn Edwards, Peter Howarth, Joseph K. Han

Department of Otolaryngology (ENT) Faculty Publications

In recent years, several biologics targeting Type 2 inflammation have been developed for treating chronic rhinosinusitis with nasal polyps (CRSwNP). These have been studied in registrational randomized controlled trials (RCTs), which vary in their patient populations, trial design, endpoints, geography, timing, or data-handling processes. While (in)direct treatment comparisons and meta-analyses have been carried out to compare efficacy results from RCTs, often these fail to properly account for these between-study differences. Here, we summarize the key between-study differences that can influence trial outcomes and highlight the resulting challenges faced when comparing outcomes from different Phase III RCTs of biologics in CRSwNP.

An Ensemble Classifier For Ordinal Outcomes In High-Dimensional Genomics Data, Heranga K. Rathnasekara, Sinjini Sikdar

Mathematics & Statistics Faculty Publications

Analysis of genomics data for predicting disease outcomes is a fast-growing field in medical research. There often exist categorical, specifically, ordinal outcomes that need to be predicted based on genomic profiles. This has led to recent development of some high-dimensional ordinal classification methods that can address the large dimensionality of the genomic covariate set. These high-dimensional ordinal models tend to vary widely in their performance depending on the data they are applied to and the evaluation criteria used. In this article, we outline an ensemble ordinal classifier that integrates different ordinal modeling approaches through bootstrap-based model evaluation, multi-metric performance assessment, …

Validation Of A Risk Score For Cancer-Associated Thrombosis Using Nationwide Ehr Data, Ang Li, Omid Jafari, Barbara D Lam, Jun Y Jiang, Rock Bum Kim, Shengling Ma, Emily Zhou, Joyce W Tiong, Elizabeth C Chiang, Justine Ryu, Christopher I Amos, Jennifer La, Nathanael R Fillmore

Faculty, Staff and Student Publications

Importance: Venous thromboembolism (VTE) is associated with increased mortality and morbidity in patients with cancer. Existing risk prediction models are typically validated within individual sites, a fragmented approach that limits clinical adoption.

Objective: To validate the electronic health record cancer-associated thrombosis (EHR-CAT) score compared with the benchmark Khorana score in a contemporary cohort of patients with cancer across the nation, before and after treatment, excluding those at high risk of bleeding.

Design, setting, and participants: This prognostic study included patients in a nationwide longitudinal EHR database from January 2018 to December 2023 with follow-up continuing to April 2025. Patients with …

Histone Methyltransferase Ash1l Primes Metastases And Metabolic Reprogramming Of Macrophages In The Bone Niche, Chenling Meng, Kevin Lin, Wei Shi, Hongqi Teng, Xinhai Wan, Anna Debruine, Yin Wang, Xin Liang, Javier Leo, Feiyu Chen, Qianlin Gu, Jie Zhang, Vivien Van, Kiersten L Maldonado, Boyi Gan, Li Ma, Yue Lu, Di Zhao

Faculty, Staff and Student Publications

Bone metastasis is a major cause of cancer death; however, the epigenetic determinants driving this process remain elusive. Here, we report that histone methyltransferase ASH1L is genetically amplified and is required for bone metastasis in men with prostate cancer. ASH1L rewires histone methylations and cooperates with HIF-1α to induce pro-metastatic transcriptome in invading cancer cells, resulting in monocyte differentiation into lipid-associated macrophage (LA-TAM) and enhancing their pro-tumoral phenotype in the metastatic bone niche. We identified IGF-2 as a direct target of ASH1L/HIF-1α and mediates LA-TAMs' differentiation and phenotypic changes by reprogramming oxidative phosphorylation. Pharmacologic inhibition of the ASH1L-HIF-1α-macrophages axis elicits …

Incorporating Latent Survival Trajectories And Covariate Heterogeneity In Time-To-Event Data Analysis: A Joint Mixture Model Approach, Fu-Wen Liang, Wenyaw Chan, Michael D Swartz, Bouthaina S Dabaja

Faculty, Staff and Student Publications

Background: Finite mixture models have been recently applied in time-to-event data to identify subgroups with distinct hazard functions, yet they often assume differing covariate effects on failure times across latent classes but homogeneous covariate distributions. This study aimed to develop a method for analyzing time-to-event data while accounting for unobserved heterogeneity within a mixture modeling framework.

Methods: A joint model was developed to incorporate latent survival trajectories and observed information for the joint analysis of time-to-event outcomes, correlated discrete and continuous covariates, and a latent class variable. It assumed covariate effects on survival times and covariate distributions vary across latent …

Precision Phenotyping For Curating Research Cohorts Of Patients With Unexplained Post-Acute Sequelae Of Covid-19, Alaleh Azhir, Jonas Hügel, Jiazi Tian, Jingya Cheng, Ingrid V Bassett, Douglas S Bell, Elmer V Bernstam, Maha R Farhat, Darren W Henderson, Emily S Lau, Michele Morris, Yevgeniy R Semenov, Virginia A Triant, Shyam Visweswaran, Zachary H Strasser, Jeffrey G Klann, Shawn N Murphy, Hossein Estiri

Faculty, Staff and Student Publications

BACKGROUND: Scalable identification of patients with post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms, which has led to suboptimal accuracy, demographic biases, and underestimation of the PASC.

METHODS: In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying cohorts of patients with PASC. We used longitudinal electronic health records data from over 295,000 patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to simultaneously exclude sequelae that prior conditions can explain and include infection-associated chronic conditions. We performed independent chart reviews …

Leveraging Large Language Models For Knowledge-Free Weak Supervision In Clinical Natural Language Processing, Enshuo Hsu, Kirk Roberts

Faculty, Staff and Student Publications

The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable. Weak supervision and in-context learning offer partial solutions to this issue, particularly using large language models (LLMs), but their performance still trails traditional supervised methods with moderate amounts of gold-standard data. In particular, inferencing with LLMs is computationally heavy. We propose an approach leveraging fine-tuning LLMs and weak supervision with virtually no domain knowledge that still achieves consistently dominant performance. Using a prompt-based approach, the LLM is used to generate weakly-labeled …

Proxy Panels Enable Privacy-Aware Outsourcing Of Genotype Imputation, Degui Zhi, Xiaoqian Jiang, Arif Harmanci

Faculty, Staff and Student Publications

One of the major challenges in genomic data sharing is protecting participants' privacy in collaborative studies and in cases when genomic data are outsourced to perform analysis tasks, for example, genotype imputation services and federated collaborations genomic analysis. Although numerous cryptographic methods have been developed, these methods may not yet be practical for population-scale tasks in terms of computational requirements, rely on high-level expertise in security, and require each algorithm to be implemented from scratch. In this study, we focus on outsourcing of genotype imputation, a fundamental task that utilizes population-level reference panels, and develop protocols that rely on using …

Scproatlas: An Atlas Of Multiplexed Single-Cell Spatial Proteomics Imaging In Human Tissues, Tiangang Wang, Xuanmin Chen, Yujuan Han, Jiahao Yi, Xi Liu, Pora Kim, Liyu Huang, Kexin Huang, Xiaobo Zhou

Faculty, Staff and Student Publications

Spatial proteomics can visualize and quantify protein expression profiles within tissues at single-cell resolution. Although spatial proteomics can only detect a limited number of proteins compared to spatial transcriptomics, it provides comprehensive spatial information with single-cell resolution. By studying the spatial distribution of cells, we can clearly obtain the spatial context within tissues at multiple scales. Spatial context includes the spatial composition of cell types, the distribution of functional structures, and the spatial communication between functional regions, all of which are crucial for the patterns of cellular distribution. Here, we constructed a comprehensive spatial proteomics functional annotation knowledgebase, scProAtlas (https://relab.xidian.edu.cn/scProAtlas/#/), …

Aspdb: An Integrative Knowledgebase Of Human Protein Isoforms From Experimental And Ai-Predicted Structures, Yuntao Yang, Himansu Kumar, Yuhan Xie, Zhao Li, Rongbin Li, Wenbo Chen, Chiamaka S Diala, Meer A Ali, Yi Xu, Albon Wu, Sayed-Rzgar Hosseini, Erfei Bi, Hongyu Zhao, Pora Kim, W Jim Zheng

Faculty, Staff and Student Publications

Alternative splicing is a crucial cellular process in eukaryotes, enabling the generation of multiple protein isoforms with diverse functions from a single gene. To better understand the impact of alternative splicing on protein structures, protein-protein interaction and human diseases, we developed ASpdb (https://biodataai.uth.edu/ASpdb/), a comprehensive database integrating experimentally determined structures and AlphaFold 2-predicted models for human protein isoforms. ASpdb includes over 3400 canonical isoforms, each represented by both experimentally resolved and predicted structures, and >7200 alternative isoforms with AlphaFold 2 predictions. In addition to detailed splicing events, 3D structures, sequence variations and functional annotations, ASpdb uniquely offers comparative analyses and …

Metsdb: A Knowledgebase Of Cancer Metastasis At Bulk, Single-Cell And Spatial Levels, Sijia Wu, Jiajin Zhang, Yanfei Wang, Xinyu Qin, Zhaocan Zhang, Zhennan Lu, Pora Kim, Xiaobo Zhou, Liyu Huang

Faculty, Staff and Student Publications

Cancer metastasis, the process by which tumour cells migrate and colonize distant organs from a primary site, is responsible for the majority of cancer-related deaths. Understanding the cellular and molecular mechanisms underlying this complex process is essential for developing effective metastasis prevention and therapy strategies. To this end, we systematically analysed 1786 bulk tissue samples from 13 cancer types, 988 463 single cells from 17 cancer types, and 40 252 spots from 45 spatial slides across 10 cancer types. The results of these analyses are compiled in the metsDB database, accessible at https://relab.xidian.edu.cn/metsDB/. This database provides insights into alterations in …

Crisprofft: Comprehensive Database Of Crispr/Cas Off-Targets, Grant Wang, Xiaona Liu, Aoqi Wang, Jianguo Wen, Pora Kim, Qianqian Song, Xiaona Liu, Xiaobo Zhou

Faculty, Staff and Student Publications

The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated protein) programmable nuclease system continues to evolve, with in vivo therapeutic gene editing increasingly applied in clinical settings. However, off-target effects remain a significant challenge, hindering its broader clinical application. To enhance the development of gene-editing therapies and the accuracy of prediction algorithms, we developed CRISPRoffT (https://ccsm.uth.edu/CRISPRoffT/). Users can access a comprehensive repository of off-target regions predicted and validated by a diverse range of technologies across various cell lines, Cas enzyme variants, engineered sgRNAs (single guide RNAs) and CRISPR editing systems. CRISPRoffT integrates results of off-target analysis from 74 studies, encompassing …

Elephant Presence Detection For Early-Warning In Kenya: A Cnn Transfer-Learning Approach, Pascaline Jerotich

All Graduate Theses, Dissertations, and Other Capstone Projects

In Kenya, conflicts between humans and elephants often lead to destruction of crops, lower family income, and put people and elephants at risk at the border of developing farms. Fences, patrols, and manual camera inspection are all expensive and take too long to be useful. This research creates an affordable early warning system that uses transfer learning with convolutional neural networks to find elephants in images. There are two label of elephant and non-elephant wildlife which are a public wildlife corpus of approximately 40,000 photographs of binary task. Cleaning of the dataset was done to ensure high quality and consistency. …

A Bayesian Deep Segmentation Framework For Glioblastoma Tumor Segmentation Using Follow-Up Mris, Tanjida Kabir, Kang-Lin Hsieh, Luis Nunez, Yu-Chun Hsu, Juan C Rodriguez Quintero, Octavio Arevalo, Kangyi Zhao, Jay-Jiguang Zhu, Roy F Riascos, Mahboubeh Madadi, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

Background: Glioblastoma (GBM) is the most common malignant brain tumor with an abysmal prognosis. Since complete tumor cell removal is impossible due to the infiltrative nature of GBM, accurate measurement is paramount for GBM assessment. Preoperative magnetic resonance images (MRIs) are crucial for initial diagnosis and surgical planning, while follow-up MRIs are vital for evaluating treatment response. The structural changes in the brain caused by surgical and therapeutic measures create significant differences between preoperative and follow-up MRIs. In clinical research, advanced deep learning models trained on preoperative MRIs are often applied to assess follow-up scans, but their effectiveness in this …

Depletion Of Adipose Stroma-Like Cancer-Associated Fibroblasts Potentiates Pancreatic Cancer Immunotherapy, Joseph Rupert, Alexes Daquinag, Yongmei Yu, Yulin Dai, Zhongming Zhao, Mikhail G Kolonin

Faculty, Staff and Student Publications

This study shows that populations of CAFs have distinct effects on pancreatic cancer progression and shows that depletion of CAFs expressing adipose markers potentiates tumor/metastasis suppression effects of immune checkpoint blockade.

Gene Expression Changes In Human Cerebral Arteries Following Hemoglobin Exposure: Implications For Vascular Responses In Sah, Chathathayil M Shafeeque, Arif O Harmanci, Sithara Thomas, Ari C Dienel, Devin W Mcbride, Kumar T Peeyush, Spiros L Blackburn

Faculty, Staff and Student Publications

Subarachnoid hemorrhage (SAH), characterized by the presence of hemoglobin (Hb) in the subarachnoid space, significantly impacts cerebral vessels, leading to various pathological outcomes. The toxicity of cell-free Hb released from erythrocytes and its metabolites after SAH causes vasoconstriction and neuronal damage, and correlates with delayed ischemic neurological deficits (DIND). While animal models have provided substantial and invaluable data in the research of aneurysmal SAH, the specific effects of subarachnoid blood on cerebral arteries remain greatly understudied. Here, we describe the changes in the genetic profile of human cerebral arteries exposed to free Hb for 48 h. We performed an ex …

Tumor Expression Of Cd83 Reduces Glioma Progression And Is Associated With Reduced Immunosuppression, Malcolm F Mcdonald, Rachel Naomi Curry, Isabella O'Reilly, Brittney Lozzi, Alexis Cervantes, Zhung-Fu Lee, Anna Rosenbaum, Peihao He, Carrie Mohila, Arif O Harmanci, Akdes Serin Harmanci, Benjamin Deneen, Ganesh Rao

Faculty, Staff and Student Publications

Immunosuppression in malignant glioma remains a barrier to therapeutic development. CD83 overexpression in human and mouse glioma increases survival. CD83+ tumor cells promote signatures related to cytotoxic T cells, enhanced activation of CD8+ T cells, and increased proinflammatory cytokines. These findings suggest that tumor-expressed CD83 could mediate tumor-immune communications.

De-Identification Is Not Enough: A Comparison Between De-Identified And Synthetic Clinical Notes, Atiquer Rahman Sarkar, Yao-Shun Chuang, Noman Mohammed, Xiaoqian Jiang

Faculty, Staff and Student Publications

For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative models and the breakthroughs in large generative language models raise the question of whether synthetically generated clinical notes could be a viable alternative to real notes for research purposes. In this work, we demonstrated that (i) de-identification of real clinical notes does not protect records against a membership inference attack, (ii) proposed a novel approach to generate synthetic clinical notes using the current state-of-the-art large language models, (iii) evaluated …

Enhancing Data Standards To Advance Translation In Spinal Cord Injury, Vanessa K. Noonan, Suzanne Humphreys, Fin Biering-Sørensen, Susan Charlifue, Yuying Chen, James D. Guest, Linda A. T. Jones, Jennifer French, Eva Widerström-Noga, Vance P. Lemmon, Allen W. Heinemann, Jan M. Schwab, Aaron A. Phillips, Marzieh M. Rizi, John L. K. Kramer, Catherine R. Jutzeler, Abel Torres-Espin

Department of Physical Therapy Faculty Papers

Data standards are available for spinal cord injury (SCI). The International SCI Data Sets were created in 2002 and there are currently 27 freely available. In 2014 the National Institute of Neurological Disorders and Stroke developed clinical common data elements to promote clinical data sharing in SCI. The objective of this paper is to provide an overview of SCI data standards, describe learnings from the traumatic brain injury (TBI) field using data to enhance research and care, and discuss future opportunities in SCI. Given the complexity of SCI, frameworks such as a systems medicine approach and Big Data perspective have …

Question Answering For Electronic Health Records: Scoping Review Of Datasets And Models, Jayetri Bardhan, Kirk Roberts, Daisy Zhe Wang

Faculty, Staff and Student Publications

Background: Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA.

Objective: This study aims to provide a methodological review of existing works on …

Characterizing The Progression From Mild Cognitive Impairment To Dementia: A Network Analysis Of Longitudinal Clinical Visits, Muskan Garg, Sara Hejazi, Sunyang Fu, Maria Vassilaki, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn

Faculty, Staff and Student Publications

Background: With the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare.

Objective: We aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, …

Design And Implementation Of An Opioid Scorecard For Hospital System-Wide Peer Comparison Of Opioid Prescribing Habits: Observational Study, Benjamin Slovis, Soonyip Huang, Melanie Mcarthur, Cara Martino, Tasia Beers, Meghan Labella, Jeffrey Riggio, Edmund Pribitkin

Jefferson Hospital Staff Papers and Presentations

BACKGROUND: Reductions in opioid prescribing by health care providers can lead to a decreased risk of opioid dependence in patients. Peer comparison has been demonstrated to impact providers' prescribing habits, though its effect on opioid prescribing has predominantly been studied in the emergency department setting.

OBJECTIVE: The purpose of this study is to describe the development of an enterprise-wide opioid scorecard, the architecture of its implementation, and plans for future research on its effects.

METHODS: Using data generated by the author's enterprise vendor-based electronic health record, the enterprise analytics software, and expertise from a dedicated group of informaticists, physicians, and …

Improving Large Language Models For Clinical Named Entity Recognition Via Prompt Engineering, Yan Hu, Qingyu Chen, Jingcheng Du, Xueqing Peng, Vipina Kuttichi Keloth, Xu Zuo, Yujia Zhou, Zehan Li, Xiaoqian Jiang, Zhiyong Lu, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.

OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.

MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, …

Ensemble Pretrained Language Models To Extract Biomedical Knowledge From Literature, Zhao Li, Qiang Wei, Liang-Chin Huang, Jianfu Li, Yan Hu, Yao-Shun Chuang, Jianping He, Avisha Das, Vipina Kuttichi Keloth, Yuntao Yang, Chiamaka S Diala, Kirk E Roberts, Cui Tao, Xiaoqian Jiang, W Jim Zheng, Hua Xu

Faculty, Staff and Student Publications

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking.

MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and …

Special Supplement Issue On Quality Assurance And Enrichment Of Biological And Biomedical Ontologies And Terminologies, Licong Cui, Ankur Agrawal

Faculty, Staff and Student Publications

Ontologies and terminologies serve as the backbone of knowledge representation in biomedical domains, facilitating data integration, interoperability, and semantic understanding across diverse applications. However, the quality assurance and enrichment of these resources remain an ongoing challenge due to the dynamic nature of biomedical knowledge. In this editorial, we provide an introductory summary of seven articles included in this special supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. These articles span a spectrum of topics, such as development of automated quality assessment frameworks for Resource Description Framework (RDF) resources, identification of missing concepts in SNOMED …

Perceptions Of Hiv-Related Comorbidities And Usability Of A Virtual Environment For Cardiovascular Disease Prevention Education In Sexual Minority Men With Hiv: Formative Phases Of A Pilot Randomized Controlled Trial, S Raquel Ramos, Harmony Reynolds, Constance Johnson, Gail Melkus, Trace Kershaw, Julian F Thayer, Allison Vorderstrasse

Faculty, Staff and Student Publications

Background: Sexual minority men with HIV are at an increased risk of cardiovascular disease (CVD) and have been underrepresented in behavioral research and clinical trials.

Objective: This study aims to explore perceptions of HIV-related comorbidities and assess the interest in and usability of a virtual environment for CVD prevention education in Black and Latinx sexual minority men with HIV.

Methods: This is a 3-phase pilot behavioral randomized controlled trial. We report on formative phases 1 and 2 that informed virtual environment content and features using qualitative interviews, usability testing, and beta testing with a total of 25 individuals. In phase …

Sexannodb, A Knowledgebase Of Sex-Specific Regulations From Multi-Omics Data Of Human Cancers, Mengyuan Yang, Yuzhou Feng, Jiajia Liu, Hong Wang, Sijia Wu, Weiling Zhao, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

Background

Sexual differences across molecular levels profoundly impact cancer biology and outcomes. Patient gender significantly influences drug responses, with divergent reactions between men and women to the same drugs. Despite databases on sex differences in human tissues, understanding regulations of sex disparities in cancer is limited. These resources lack detailed mechanistic studies on sex-biased molecules.

Methods

In this study, we conducted a comprehensive examination of molecular distinctions and regulatory networks across 27 cancer types, delving into sex-biased effects. Our analyses encompassed sex-biased competitive endogenous RNA networks, regulatory networks involving sex-biased RNA binding protein-exon skipping events, sex-biased transcription factor-gene regulatory networks, …

Patient-Centered Clinical Decision Support Challenges And Opportunities Identified From Workflow Execution Models, Dean F Sittig, Aziz Boxwala, Adam Wright, Courtney Zott, Nicole A Gauthreaux, James Swiger, Edwin A Lomotan, Prashila Dullabh

Faculty, Staff and Student Publications

OBJECTIVE: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.

METHODS: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee.

RESULTS AND DISCUSSION: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new …

Automatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Modelsautomatic Uncovering Of Patient Primary Concerns In Portal Messages Using A Fusion Framework Of Pretrained Language Models, Yang Ren, Yuqi Wu, Jungwei W Fan, Aditya Khurana, Sunyang Fu, Dezhi Wu, Hongfang Liu, Ming Huang

Faculty, Staff and Student Publications

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care.

MATERIALS AND METHODS: We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural …