Medicine and Health Sciences Commons^™

Predictive Digital Twin For Optimizing Patient-Specific Radiotherapy Regimens Under Uncertainty In High-Grade Gliomas, Anirban Chaudhuri, Graham Pash, David A Hormuth, Guillermo Lorenzo, Michael Kapteyn, Chengyue Wu, Ernesto A B F Lima, Thomas E Yankeelov, Karen Willcox

Faculty, Staff and Student Publications

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data. The calibrated digital twin is used to propose optimal …

Machine Learning-Based Prediction Of Acute Mortality In Emergency Department Patients Using Twelve-Lead Electrocardiogram, Po-Cheng Chang, Zhi-Yong Liu, Yu-Chang Huang, Yu-Chun Hsu, Jung-Sheng Chen, Ching-Heng Lin, Richard Tsai, Chung-Chuan Chou, Ming-Shien Wen, Hung-Ta Wo, Wen-Chen Lee, Hao-Tien Liu, Chun-Chieh Wang, Chang-Fu Kuo

Faculty, Staff and Student Publications

BACKGROUND: The risk of mortality is relatively high among patients who visit the emergency department (ED), and stratifying patients at high risk can help improve medical care. This study aimed to create a machine-learning model that utilizes the standard 12-lead ECG to forecast acute mortality risk in ED patients.

METHODS: The database included patients who visited the EDs and underwent standard 12-lead ECG between October 2007 and December 2017. A convolutional neural network (CNN) ECG model was developed to classify survival and mortality using 12-lead ECG tracings acquired from 345,593 ED patients. For machine learning model development, the patients were …

Artificial Intelligence-Enabled Electrocardiographic Screening For Left Ventricular Systolic Dysfunction And Mortality Risk Prediction, Yu-Chang Huang, Yu-Chun Hsu, Zhi-Yong Liu, Ching-Heng Lin, Richard Tsai, Jung-Sheng Chen, Po-Cheng Chang, Hao-Tien Liu, Wen-Chen Lee, Hung-Ta Wo, Chung-Chuan Chou, Chun-Chieh Wang, Ming-Shien Wen, Chang-Fu Kuo

Faculty, Staff and Student Publications

BACKGROUND: Left ventricular systolic dysfunction (LVSD) characterized by a reduced left ventricular ejection fraction (LVEF) is associated with adverse patient outcomes. We aimed to build a deep neural network (DNN)-based model using standard 12-lead electrocardiogram (ECG) to screen for LVSD and stratify patient prognosis.

METHODS: This retrospective chart review study was conducted using data from consecutive adults who underwent ECG examinations at Chang Gung Memorial Hospital in Taiwan between October 2007 and December 2019. DNN models were developed to recognize LVSD, defined as LVEF

RESULTS: The mean age of patients in the testing dataset was 63.7 ± 16.3 years (46.3% …

Efficient Federated Kinship Relationship Identification, Xinyue Wang, Leonard Dervishi, Wentao Li, Xiaoqian Jiang, Erman Ayday, Jaideep Vaidya

Faculty, Staff and Student Publications

Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and …

Sensitive Data Detection With High-Throughput Machine Learning Models In Electrical Health Records, Kai Zhang, Xiaoqian Jiang

Faculty, Staff and Student Publications

In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based …

Large Language Models For Healthcare Data Augmentation: An Example On Patient-Trial Matching, Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security …

Split Learning For Distributed Collaborative Training Of Deep Learning Models In Health Informatics, Zhuohang Li, Chao Yan, Xinmeng Zhang, Gharib Gharibi, Zhijun Yin, Xiaoqian Jiang, Bradley A Malin

Faculty, Staff and Student Publications

Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We …

Integrating Comorbidity Knowledge For Alzheimer's Disease Drug Repurposing Using Multi-Task Graph Neural Network, Ko-Hong Lin, Kang-Lin Hsieh, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, …

Local Contrastive Learning For Medical Image Recognition, Syed A Rizvi, Ruixiang Tang, Xiaoqian Jiang, Xiaotian Ma, Xia Hu

Faculty, Staff and Student Publications

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality …

Text Classification Of Cancer Clinical Trial Eligibility Criteria, Yumeng Yang, Soumya Jayaraj, Ethan Ludmir, Kirk Roberts

Faculty, Staff and Student Publications

Automatic identification of clinical trials for which a patient is eligible is complicated by the fact that trial eligibility are stated in natural language. A potential solution to this problem is to employ text classification methods for common types of eligibility criteria. In this study, we focus on seven common exclusion criteria in cancer trials: prior malignancy, human immunodeficiency virus, hepatitis B, hepatitis C, psychiatric illness, drug/substance abuse, and autoimmune illness. Our dataset consists of 764 phase III cancer trials with these exclusions annotated at the trial level. We experiment with common transformer models as well as a new pre-trained …

A Novel Nih Research Grant Recommender Using Bert, Jie Zhu, Braja Gopal Patra, Hulin Wu, Ashraf Yaseen

Faculty, Staff and Student Publications

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the …

Data Mining Pipeline For Covid-19 Vaccine Safety Analysis Using A Large Electronic Health Record, Yan Huang, Xiaojin Li, Deepa Dongarwar, Hulin Wu, Guo-Qiang Zhang

Faculty, Staff and Student Publications

We developed a novel data mining pipeline that automatically extracts potential COVID-19 vaccine-related adverse events from a large Electronic Health Record (EHR) dataset. We applied this pipeline to Optum® de-identified COVID-19 EHR dataset containing COVID-19 vaccine records between December 11, 2020 and January 20, 2022. We compared post-vaccination diagnoses between the COVID-19 vaccine group and the influenza vaccine group among 553,682 individuals without COVID-19 infection. We extracted 1,414 ICD-10 diagnosis categories (first three ICD10 digits) within 180 days after the first dose of the COVID-19 vaccine. We then ranked the diagnosis codes using the adverse event rates and adjusted odds …

Transiently Impaired Endothelial Function During Thyroid Hormone Withdrawal In Differentiated Thyroid Cancer Patients, Li-Ying Hou, Xiao Li, Guo-Qiang Zhang, Chuang Xi, Chen-Tian Shen, Hong-Jun Song, Wen-Kun Bai, Zhong-Ling Qiu, Quan-Yong Luo

Faculty, Staff and Student Publications

PURPOSE: Endothelial dysfunction, which was associated with chronic hypothyroidism, was an early event in atherosclerosis. Whether short-term hypothyroidism following thyroxine withdrawal during radioiodine (RAI) therapy was associated with endothelial dysfunction in patients with differentiated thyroid cancer (DTC) was unclear. Aim of the study was to assess whether short-term hypothyroidism could impair endothelial function and the accompanied metabolic changes in the whole process of RAI therapy.

METHODS: We recruited fifty-one patients who underwent total thyroidectomy surgery and would accept RAI therapy for DTC. We analyzed thyroid function, endothelial function and serum lipids levels of the patients at three time points: the …

Revisiting The Type Species Of The Genus Homidia (Collembola, Entomobryidae), Guo-Qiang Zhang, Yu-Xin Zhao, Feng Zhang

Faculty, Staff and Student Publications

Homidiacingula Börner, 1906, the type species of the genus Homidia Börner, 1906, is widespread from India to Southeast Asia, but its detailed morphological characteristics have not yet been described. We examined the morphology of specimens of H.cingula from Indonesia and southwestern China and confirmed their conspecific status by comparing their DNA barcoding sequences. We also compared the morphology of H.cingula with other two closely related species, confirming the valid species status of H.subcingula Denis, 1948. Our study provides new taxonomic and molecular data for the genus Homidia.

Selection And Validation Of Optimal Reference Genes For Rt-Qpcr Analyses In Aphidoletes Aphidimyza Rondani (Diptera: Cecidomyiidae), Xiu-Xian Shen, Guo-Qiang Zhang, Yu-Xin Zhao, Xiao-Xiao Zhu, Xiao-Fei Yu, Mao-Fa Yang, Feng Zhang

Faculty, Staff and Student Publications

Aphidoletes aphidimyza is a predator that is an important biological agent used to control agricultural and forestry aphids. Although many studies have investigated its biological and ecological characteristics, few molecular studies have been reported. The current study was performed to identify suitable reference genes to facilitate future gene expression and function analyses via quantitative reverse transcription PCR. Eight reference genes glyceraldehyde-3-phosphate dehydrogenase (GAPDH), RPS13, RPL8, RPS3, α-Tub, β-actin, RPL32, and elongation factor 1 alpha (EF1-α) were selected. Their expression levels were determined under four different experimental conditions (developmental stages, adult …

A Gcn-Based Approach To Uncover Misaligned Synonymous Terms In The Umls Metathesaurus, Xubing Hao, Rashmie Abeysinghe, Jay Shi, Licong Cui

Faculty, Staff and Student Publications

The Unified Medical Language System (UMLS), a large repository of biomedical vocabularies, has been used for supporting various biomedical applications. Ensuring the quality of the UMLS is critical to maintain both the accuracy of its content and the reliability of downstream applications. In this work, we present a Graph Convolutional Network (GCN)-based approach to identify misaligned synonymous terms organized under different UMLS concepts. We used synonymous terms grouped under the same concept as positive samples and top lexically similar terms as negative samples to train the GCN model. We applied the model to a test set and suggested those negative …

Gene Expression In Mice With Endothelium-Specific Telomerase Knockout, Zhanguo Gao, Yongmei Yu, Yulin Dai, Zhongming Zhao, Kristin Eckel-Mahan, Mikhail G Kolonin

Faculty, Staff and Student Publications

No abstract provided.

Investigating Cellular Heterogeneity At The Single-Cell Level By The Flexible And Mobile Extrachromosomal Circular Dna, Jiajinlong Kang, Yulin Dai, Jinze Li, Huihui Fan, Zhongming Zhao

Faculty, Staff and Student Publications

Extrachromosomal circular DNA (eccDNA) is a special class of DNA derived from linear chromosomes. It coexists independently with linear chromosomes in the nucleus. eccDNA has been identified in multiple organisms, including Homo sapiens, and has been shown to play important roles relevant to tumor progression and drug resistance. To date, computational tools developed for eccDNA detection are only applicable to bulk tissue. Investigating eccDNA at the single-cell level using a computational approach will elucidate the heterogeneous and cell-type-specific landscape of eccDNA within cellular context. Here, we performed the first eccDNA analysis at the single-cell level using data generated by single-cell …

Digital Solutions Observed In Clinical Trials: A Formative Feasibility Scoping Review, Taylor M Harrison, Sungrim Moon, Liwei Wang, Sunyang Fu, Hongfang Liu

Faculty, Staff and Student Publications

Growing digital access accelerates digital transformation of clinical trials where digital solutions (DSs) are increasingly and widely leveraged for improving trial efficiency, effectiveness, and accessibility. Many factors impact DS success including technology barriers, privacy concerns, or user engagement activities. It is unclear how those factors are considered or reported in the literature. Here, we perform a formative feasibility scoping review to identify gaps impacting DS quality and reproducibility in trials. Articles containing digital terms published in English from 2009 to 2022 were collected (n=4,167). 130 articles published between 2016 and 2022 were randomly selected for full-text review. Eligible articles (n=100) …

Contextual Variation Of Clinical Notes Induced By Ehr Migration, Kurt Miller, Sungrim Moon, Sunyang Fu, Hongfang Liu

Faculty, Staff and Student Publications

The structure and semantics of clinical notes vary considerably across different Electronic Health Record (EHR) systems, sites, and institutions. Such heterogeneity hampers the portability of natural language processing (NLP) models in extracting information from the text for clinical research or practice. In this study, we evaluate the contextual variation of clinical notes by measuring the semantic and syntactic similarity of the notes of two sets of physicians comprising four medical specialties across EHR migrations at two Mayo Clinic sites. We find significant semantic and syntactic variation imposed by the context of the EHR system and between medical specialties whereas only …

Characterizing Performance Gaps Of A Code-Based Dementia Algorithm In A Population-Based Cohort Of Cognitive Aging, Maria Vassilaki, Sunyang Fu, Luke R Christenson, Muskan Garg, Ronald C Petersen, Jennifer St Sauver, Sunghwan Sohn

Faculty, Staff and Student Publications

BACKGROUND: Multiple algorithms with variable performance have been developed to identify dementia using combinations of billing codes and medication data that are widely available from electronic health records (EHR). If the characteristics of misclassified patients are clearly identified, modifying existing algorithms to improve performance may be possible.

OBJECTIVE: To examine the performance of a code-based algorithm to identify dementia cases in the population-based Mayo Clinic Study of Aging (MCSA) where dementia diagnosis (i.e., reference standard) is actively assessed through routine follow-up and describe the characteristics of persons incorrectly categorized.

METHODS: There were 5,316 participants (age at baseline (mean (SD)): 73.3 …

Segmentation Of Acute Stroke Infarct Core Using Image-Level Labels On Ct-Angiography, Luca Giancardo, Arash Niktabe, Laura Ocasio, Rania Abdelkhaleq, Sergio Salazar-Marioni, Sunil A Sheth

Faculty, Staff and Student Publications

Acute ischemic stroke is a leading cause of death and disability in the world. Treatment decisions, especially around emergent revascularization procedures, rely heavily on size and location of the infarct core. Currently, accurate assessment of this measure is challenging. While MRI-DWI is considered the gold standard, its availability is limited for most patients suffering from stroke. Another well-studied imaging modality is CT-Perfusion (CTP) which is much more common than MRI-DWI in acute stroke care, but not as precise as MRI-DWI, and it is still unavailable in many stroke hospitals. A method to determine infarct core using CT-Angiography (CTA), a much …

Keystroke-Dynamics For Parkinson's Disease Signs Detection In An At-Home Uncontrolled Population: A New Benchmark And Method, Shikha Tripathi, Teresa Arroyo-Gallego, Luca Giancardo

Faculty, Staff and Student Publications

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease disorder in the world. A prompt diagnosis would enable clinical trials for disease-modifying neuroprotective therapies. Recent research efforts have unveiled imaging and blood markers that have the potential to be used to identify PD patients promptly, however, the idiopathic nature of PD makes these tests very hard to scale to the general population. To this end, we need an easily deployable tool that would enable screening for PD signs in the general population. In this work, we propose a new set of features based on keystroke dynamics, i.e., the time …

Multidrug Resistance In The Standardized Treatment Of Colon Cancer Harboring A Rare Fibrosarcoma B-Type (Braf) Pn581i Mutation: A Case Report, Xiaoyan Wang, Chenyi Zhao, Yang Gong, Ying Wang, Feng Guo

Faculty, Staff and Student Publications

BRAF non-V600 mutations are a distinct molecular subset of colorectal cancer (CRC) that has little to no clinical similarity to the BRAF V600 mutations. It is generally considered that the BRAF non-V600 mutations correlate with better survival of CRC patients. In this report, we present an unusual case of that a midlife female patient who was initially diagnosed with stage IIIC colon cancer, and multiple metastases were found 25 months after radical surgery. Next-generation sequencing (NGS) revealed the BRAF p.N581I (c.1742A>T) mutation. She received chemotherapy, targeted therapy, and immunotherapy. However, the disease progressed rapidly with rare metastasis of the …

Annotation And Information Extraction Of Consumer-Friendly Health Articles For Enhancing Laboratory Test Reporting, Zhe He, Shubo Tian, Arslan Erdengasileng, Karim Hanna, Yang Gong, Zhan Zhang, Xiao Luo, Mia Liza A Lustria

Faculty, Staff and Student Publications

Viewing laboratory test results is patients' most frequent activity when accessing patient portals, but lab results can be very confusing for patients. Previous research has explored various ways to present lab results, but few have attempted to provide tailored information support based on individual patient's medical context. In this study, we collected and annotated interpretations of textual lab result in 251 health articles about laboratory tests from AHealthyMe.com. Then we evaluated transformer-based language models including BioBERT, ClinicalBERT, RoBERTa, and PubMedBERT for recognizing key terms and their types. Using BioPortal's term search API, we mapped the annotated terms to concepts in …

The Current Status And Future Prospects For Conversion Therapy In The Treatment Of Hepatocellular Carcinoma, Jinfeng Bai, Ming Huang, Bohan Song, Wei Luo, Rong Ding

Faculty, Staff and Student Publications

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths worldwide. In China, most HCC patients are diagnosed with advanced disease and in these cases surgery is challenging. Conversion therapy can be used to change unresectable HCC into resectable disease and is a potential breakthrough treatment strategy. The resection rate for unresectable advanced HCC has recently improved as a growing number of patients have benefited from conversion therapy. While conversion therapy is at an early stage of development, progress in patient selection, optimum treatment methods, and the timing of surgery have the potential to deliver significant benefits. In …

The Health-Promoting Effects And The Mechanism Of Intermittent Fasting, Simin Liu, Min Zeng, Weixi Wan, Ming Huang, Xiang Li, Zixian Xie, Shang Wang, Yu Cai

Faculty, Staff and Student Publications

Intermittent fasting (IF) is an eating pattern in which individuals go extended periods with little or no energy intake after consuming regular food in intervening periods. IF has several health-promoting effects. It can effectively reduce weight, fasting insulin levels, and blood glucose levels. It can also increase the antitumor activity of medicines and cause improvement in the case of neurological diseases, such as memory deficit, to achieve enhanced metabolic function and prolonged longevity. Additionally, IF activates several biological pathways to induce autophagy, encourages cell renewal, prevents cancer cells from multiplying and spreading, and delays senescence. However, IF has specific adverse …

Emulate Randomized Clinical Trials Using Heterogeneous Treatment Effect Estimation For Personalized Treatments: Methodology Review And Benchmark, Yaobin Ling, Pulakesh Upadhyaya, Luyao Chen, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of causal effect, assuming that all populations have the same effect size. However, no "one-size-fits-all" treatment seems to work in some complex diseases. Treatment effects may vary by patient. Estimating heterogeneous treatment effects (HTE) may have a high impact on developing personalized treatment. Lots of advanced machine learning models for estimating HTE have emerged in recent years, but there has been …

Survey Of West Nile And Banzi Viruses In Mosquitoes, South Africa, 2011-2018, Caitlin Macintyre, Milehna Mara Guarido, Megan Amy Riddin, Todd Johnson, Leo Braack, Maarten Schrama, Erin Gorsich, Antonio Paulo Gouveia Almeida, Marietjie Venter

Faculty, Staff and Student Publications

We collected >40,000 mosquitoes from 5 provinces in South Africa during 2011-2018 and screened for zoonotic flaviviruses. We detected West Nile virus in mosquitoes from conservation and periurban sites and potential new mosquito vectors; Banzi virus was rare. Our results suggest flavivirus transmission risks are increasing in South Africa.

Skin Lesion Segmentation In Dermoscopic Images With Noisy Data, Norsang Lama, Jason Hagerty, Anand Nambisan, Ronald Joe Stanley, William Van Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

We Propose a Deep Learning Approach to Segment the Skin Lesion in Dermoscopic Images. the Proposed Network Architecture Uses a Pretrained Efficient Net Model in the Encoder and Squeeze-And-Excitation Residual Structures in the Decoder. We Applied This Approach on the Publicly Available International Skin Imaging Collaboration (ISIC) 2017 Challenge Skin Lesion Segmentation Dataset. This Benchmark Dataset Has Been Widely Used in Previous Studies. We Observed Many Inaccurate or Noisy Ground Truth Labels. to Reduce Noisy Data, We Manually Sorted All Ground Truth Labels into Three Categories — Good, Mildly Noisy, and Noisy Labels. Furthermore, We Investigated the Effect of Such …