Medicine and Health Sciences Commons^™

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 …

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, …

Paper-Recorded Ecg Digitization Method With Automatic Reference Voltage Selection For Telemonitoring And Diagnosis, Liang Hung Wang, Chao Xin Xie, Tao Yang, Hong Xin Tan, Ming Hui Fan, I. Chun Kuo, Zne Jung Lee, Tsung Yi Chen, Pao Cheng Huang, Shih Lun Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed …

Incorporating Solvation Thermodynamic Mapping In Computer-Aided Drug Design, Yeonji Ji

Dissertations, Theses, and Capstone Projects

Advancements in computational techniques have revolutionized structure-based drug design, substantially improving the efficiency and effectiveness of the drug discovery process by reducing time, costs, and labor requirements. These advancements include various methods, such as investigating small molecule ligands binding to proteins, exploring alternative protein conformations, and solvation mapping on the protein surfaces. Among these methods, understanding the correlation between protein-ligand binding and the role of solvation is important.

A fundamental concept in protein-ligand binding is shape and electrostatic complementarity, which is complicated by the inherent flexibility of proteins. In the absence of small molecule ligands, proteins are complementary to surface …

Foliar Diseases And Their Management In Lupins, Department Of Primary Industries And Regional Development, Western Australia

Grains and other field crops factsheets

This factsheet describes symptoms and management of the major fungal and viral diseases affecting lupin foliage, stems, and pods. Several of these diseases have the capacity to cause significant losses if not managed effectively.

Inoculum can be carried in soil, seed, stubble, on green regrowth, or by insect vectors, depending on the disease. Therefore, an integrated approach to disease management is required using tactics such as crop rotation, stubble management, fungicide seed dressing, variety selection, and seed testing

Artificial Intelligence In Orthopaedic Education: A Comparative Analysis Of Chatgpt And Bing Ai’S Orthopaedic In-Training Examination Performance, Clark Chen, Vivek Biololikar, Duncan Vannest, James Raphael, Gene Shaffer

Einstein Health Papers

Background: This study evaluated the performance of generative artificial intelligence (AI) models on the Orthopaedic In-Training Examination (OITE), an annual exam administered to U.S. orthopaedic residency programs. Methods: ChatGPT 3.5 and Bing AI GPT 4.0 were evaluated on standardised sets of multiple-choice questions drawn from the American Academy of Orthopaedic Surgeons OITE online question bank spanning 5 years (2018–2022). A total of 1165 questions were posed to each AI system. The performance of both systems was standardised using the latest versions of ChatGPT 3.5 and Bing AI GPT 4.0. Historical data of resident scores taken from the annual OITE technical …

Reclaiming Healing Spaces: A Phenomenological Study On The Transformative Power Of Outdoor Therapy From The Lived Experiences Of Black Clinicians Working With Black Clients, Lynn Murphy

Dissertations

This phenomenological study involved assessing the experiences of Black therapists who engaged Black clients in outdoor therapeutic contexts. The study was founded on the existing literature that shows the quality of the therapeutic relationship is pivotal for client retention and the Western standards that have historically favored treatment within indoor environments. To contextualize this research, a comprehensive literature review was commenced, covering topics such as the decolonization of therapy, the historical and present-day relationship between Blacks and the outdoors in the United States, sedentary lifestyles, the psychological benefits of time spent in nature, various types of outdoor therapy, and the …

Highly Toxic Aβ Begets More Aβ, Merc M. Kemeh, Noel Lazo

Chemistry

No abstract provided.

Image-Guided Patient-Specific Optimization Of Catheter Placement For Convection-Enhanced Nanoparticle Delivery In Recurrent Glioblastoma, Chengyue Wu, David A Hormuth, Chase D Christenson, Ryan T Woodall, Michael R A Abdelmalik, William T Phillips, Thomas J R Hughes, Andrew J Brenner, Thomas E Yankeelov

Faculty, Staff and Student Publications

Background: Proper catheter placement for convection-enhanced delivery (CED) is required to maximize tumor coverage and minimize exposure to healthy tissue. We developed an image-based model to patient-specifically optimize the catheter placement for rhenium-186 (186Re)-nanoliposomes (RNL) delivery to treat recurrent glioblastoma (rGBM).

Methods: The model consists of the 1) fluid fields generated via catheter infusion, 2) dynamic transport of RNL, and 3) transforming RNL concentration to the SPECT signal. Patient-specific tissue geometries were assigned from pre-delivery MRIs. Model parameters were personalized with either 1) individual-based calibration with longitudinal SPECT images, or 2) population-based assignment via leave-one-out cross-validation. The concordance correlation coefficient …

Performance Of 5 Prominent Large Language Models In Surgical Knowledge Evaluation: A Comparative Analysis, Adam M. Ostrovsky, Joshua R. Chen, Vishal N. Shah, Babak Abai

Department of Surgery Faculty Papers

No abstract provided.

Multi-Scale Variational Autoencoder For Imputation Of Missing Values In Untargeted Metabolomics Using Whole-Genome Sequencing Data, Chen Zhao, Kuan-Jui Su, Chong Wu, Xuewei Cao, Qiuying Sha, Wu Li, Zhe Luo, Tian Qing, Chuan Qiu, Lan Juan Zhao, Anqi Liu, Lindong Jiang, Xiao Zhang, Hui Shen, Weihua Zhou, Hong-Wen Deng

Faculty, Staff and Student Publications

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics …

Diabetes Technology Meeting 2023, Tiffany Tian, Rachel E Aaron, Ashley Y Dunova, Johan H Jendle, David Kerr, Eda Cengiz, Andjela Drincic, John C Pickup, Kong Y Chen, Naomi Schwartz, Douglas B Muchmore, Halis K Akturk, Carol J Levy, Signe Schmidt, Riccardo Bellazzi, Alan H B Wu, Elias K Spanakis, Bijan Najafi, James Geoffrey Chase, Jane Jeffrie Seley, David C Klonoff

Center on Aging Staff Publications

Diabetes Technology Society hosted its annual Diabetes Technology Meeting from November 1 to November 4, 2023. Meeting topics included digital health; metrics of glycemia; the integration of glucose and insulin data into the electronic health record; technologies for insulin pumps, blood glucose monitors, and continuous glucose monitors; diabetes drugs and analytes; skin physiology; regulation of diabetes devices and drugs; and data science, artificial intelligence, and machine learning. A live demonstration of a personalized carbohydrate dispenser for people with diabetes was presented.

Large Deviation Theory In Stochastic Processes: Applications To Biological Modeling, Moshe C. Silverstein

Dissertations

This dissertation delves into developing and applying stochastic models to analyze complex biological systems. It leverages Large Deviation Theory (LDT) to gain insights into these systems, focusing on two key examples: neural networks and calcium signaling dynamics. Traditional deterministic methods frequently fail to capture biological processes' randomness and inherent variability. Meanwhile, many stochastic approaches struggle to be mathematically tractable or provide accessible insights. The approach introduced in this study provides rigorous mathematical frameworks to enhance understanding of these stochastic behaviors while remaining tractable and insightful.

A stochastic model for a random biological neural network is constructed that addresses the dependencies …

Investigations On V2o5 And Its Derivative Nanocomposites For Photocatalytic Textile Organic Dye Degradation, Jenifer A

Theses and Dissertations

In recent times, organic dyes from industrial sludge become one of the significant causes of water contamination due to their utility in textile and paper manufacturing industries. These organic dyes are highly toxic and carcinogenic. Fortunately, the photocatalytic degradation process is a practical tool for decomposing toxic organic matter in wastewater by utilizing light. The photodegradation process can be performed using semiconductor materials to purify wastewater. Thus, semiconductor photocatalysis is a desirable alternative technique for decolorizing various organic pollutants.

In this context, the present work reports the preparation and characterization of V2O5 nanocomposites by facile methods and tests them against …

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 …

Unveiling Environmental Justice In Two Us Cities Through Greenspace Accessibility And Visible Greenness Exposure, Md Shahinoor Rahman, Mahbubur Meenar, S. M. Labib, Ted Howell, Deepti Adlakha, Ben Woodward

School of Public Health Faculty Publications

Uneven access to greenspaces or visible greenness is an environmental justice (EJ) issue. In this paper, we use a social equity lens to develop geospatial models that measure convenient walking access to urban greenspaces such as parks and street-level green exposure en route to greenspaces. We utilized earth science, geospatial, and demographic datasets to develop two models—Greenspace Accessibility and Visible Greenness Exposure—and applied them in Camden and Jersey City, USA, two communities experiencing environmental injustices. Modeling results show that greenspace accessibility is a concern in both cities, with Jersey City experiencing more prominent disparities. We observed significant positive relationships in …

Advancing Endovascular Neurosurgery Training With Extended Reality: Opportunities And Obstacles For The Next Decade, Shray Patel, Michael Covell, Saarang Patel, Sandeep Kandregula, Sai Krishna Palepu, Avi Gajjar, Oleg Shekhtman, Georgios Sioutas, Ali Dhanaliwala, Terence Gade, Jan-Karl Burkhardt, Visish Srinivasan

SKMC Student Presentations and Publications

Background: Extended reality (XR) includes augmented reality (AR), virtual reality (VR), and mixed reality (MR). Endovascular neurosurgery is uniquely positioned to benefit from XR due to the complexity of cerebrovascular imaging. Given the different XR modalities available, as well as unclear clinical utility and technical capabilities, we clarify opportunities and obstacles for XR in training vascular neurosurgeons. Methods: A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted. Studies were critically appraised using ROBINS-I. Results: 19 studies were identified. 13 studies used VR, while 3 studies used MR, and 3 studies used AR. …

08.26.2024 Ored Connect, Liz Williamson

ORED Newsletter

Research reception

Small Business Innovation Research and STTR lunch and learn with Paul Cox, consultant.

Use olemiss.edu email addresses when submitting SPAN and ARC forms

Army STTR: Resources for RIs

Developing A Comprehensive Cognitive Model Of Math Achievement, Nina Anderson

Electronic Theses and Dissertations

Recently, downward trends have been reported in U.S. children’s math performance following school disruptions during COVID-19 amidst longstanding concerns for instruction and curricula within the subject. In support of efforts to remedy these declines, the current work presents two studies dedicated to identifying cognitive factors that are most strongly related to math performance, and which therefore offer promising potential targets for assessment and intervention. Both studies use data from the Colorado Learning Disabilities Research Center, which includes participants ages 8 - 16 and multiple well-validated measures of all constructs of interest. Study 1 tests three alternative latent cognitive models of …

Supervised Classification Modeling On Louisiana Medicaid Data: A Comparative Study, Yead Rahman

Master's Theses

This thesis systematically optimizes and compares state-of-the-art supervised classification models for Louisiana Medicaid data targeting clinical services, COVID-19 infection, and tobacco use. These target variables are critically important as they represent key health outcomes and behaviors among Medicaid enrollees in Louisiana, a population often characterized by poverty and limited access to education. This study applies advanced machine learning techniques to identify the best model for multinomial and binary classification tasks. These include models such as Logistic Regression, XGBoost, AdaBoost, Random Forest, Decision Tree, Artificial Neural Networks, and Naïve Bayes. Extensive tuning of the hyperparameters and optimization of each classifier were …

Risk Factors For Cognitive Impairment In Adult Population Of Coastal Area: A Cross-Sectional Study In Maringkik Island, Indonesia, Herpan Syafii Harahap, Arina Windri Rivarti, Nurhidayati Nurhidayati, Fitriannisa Faradina Zubaidi, Dini Suryani, Legis Ocktaviana Saputri, Yanna Indrayana, Athalita Andhera, Muhammad Hilam, Abiyyu Didar Haq

Kesmas

Cognitive impairment is a medical condition commonly found in elderly populations, which can be due to vascular risk factors in patients. There remains limited data on risk factors for cognitive impairment among coastal region populations. This study aimed to investigate risk factors for cognitive impairment in the adult population of Maringkik Island, West Nusa Tenggara Province, Indonesia. Data collected were age, sex, education level, hypertension, antihypertensive treatment, diabetes mellitus, cigarette smoking, and body mass index status. A total of 114 participants were recruited using a consecutive sampling method. The participants’ cognitive function assessment used the Mini-Cog instrument. The cognitive impairment …

Variation And Predictors Of Covid-19 Mortality In Hospitalized Cases In West Sumatra Province, Indonesia: A Retrospective Observational Study, Defriman Djafri, Ade Suzana Eka Putri, Yudi Pradipta

Kesmas

During 2020, the year of the COVID-19 pandemic, different Indonesian provinces had different numbers of COVID-19 infections and fatalities, particularly in West Sumatra Province. This study aimed to investigate the variation of confirmed COVID-19 cases and determine predictors of mortality in hospitalized patients across districts in West Sumatra Province. A retrospective observational study was conducted during the COVID-19 pandemic. From March 2020 to June 2021, 46,005 confirmed cases were collected in the province, of which 42,308 were hospitalized and analyzed. Confirmed cases and deaths were compared by geographic location using spatial analysis. The risk predictors of death were estimated using …

Exploration Of Positive Deviance In Prevention Of Underweight In The Under-Five: A Qualitative Study On Low-Income Urban Families, Irwan Budiono, Lukman Fauzi, Dewi Sari Rochmayani

Kesmas

Children under the age of five (the under-five) from low-income families are more vulnerable to experience underweight. This nutritional vulnerability is evident in the preliminary study, where 35.1% of the under-five experience underweight, and 28.48% are low-income families. This study aimed to explore Positive Deviance (PD) behaviors in preventing underweight among the under-five. The study applied a qualitative approach with a case study design. Data collection took place in July-August 2022, focusing on low-income families in the Gunung Brintik area. Data were collected through two focus group discussions, seven in-depth interviews, and five key informant interviews. Coding, subtheme, and theme …

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, …

Analysis Of Serum Exosome Metabolites Identifies Potential Biomarkers For Human Hepatocellular Carcinoma, Tingting Zhao, Yan Liang, Xiaolan Zhen, Hong Wang, Li Song, Didi Xing, Hui Li

Faculty, Staff and Student Publications

Currently, the clinical cure rate for primary liver cancer remains low. Effective screening and early diagnosis of hepatocellular carcinoma (HCC) remain clinical challenges. Exosomes are intimately associated with tumor development and their contents have the potential to serve as highly sensitive tumor-specific markers. A comprehensive untargeted metabolomics study was conducted using exosome samples extracted from the serum of 48 subjects (36 HCC patients and 12 healthy controls) via a commercial kit. An ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) strategy was used to identify the metabolic compounds. A total of 18 differential metabolites were identified using the non-targeted metabolomics approach of UPLC-QTOF-MS/MS. …

08.19.2024 Orsp Connect, Liz Williamson

ORED Newsletter

Lei Cao NSF award

FDP Meeting Summaries Available

August LINC Lunch

Collaboration Opportunities

Interventional Radiology's Exploration Into Artificial Intelligence, Raymond Nguyen

Master's Projects and Capstones

Background: Artificial intelligence (AI) has become more prominent in our daily lives in recent years. This includes various aspects of healthcare. Interventional radiology (IR) is one of these specialties that has taken strides in understanding how AI can be leveraged for patient care. This literature review aims to understand what areas will be most impacted by AI in IR and how it will influence both the patient and interventional radiologist.

Methods: Twenty-six publications from 2019-2024 were selected from PubMed and Scopus. Publications were sourced through a combination of keywords, subject headings (MeSH terms), and citation searching.

Results: This literature review …

Human-Human Vs Human-Ai Therapy: An Empirical Study, Mohammad Amin Kuhail, Nazik Alturki, Justin Thomas, Amal K. Alkhalifa, Amal Alshardan

All Works

In many nations, demand for mental health services currently outstrips supply, especially in the area of talk-based psychological interventions. Within this context, chatbots (software applications designed to simulate conversations with human users) are increasingly explored as potential adjuncts to traditional mental healthcare service delivery with a view to improving accessibility and reducing waiting times. However, the effectiveness and acceptability of such chatbots remains under-researched. This study evaluates mental health professionals’ perceptions of Pi, a relational Artificial Intelligence (AI) chatbot, in the early stages of the psychotherapeutic process (problem exploration). We asked 63 therapists to assess therapy transcripts between a human …

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, And Artificial Intelligence To Interrogate The Metabolic Evolution Of Glioblastoma, Kang Lin Hsieh, Qing Chen, Travis C Salzillo, Jian Zhang, Xiaoqian Jiang, Pratip K Bhattacharya, Shyan Shams

Faculty, Staff and Student Publications

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs …