Physical Sciences and Mathematics Commons^™

Peptidomics Analysis Reveals Changes In Small Urinary Peptides In Patients With Interstitial Cystitis/Bladder Pain Syndrome, Md Shadman Ridwan Abid, Haowen Qiu, Bridget Tripp, Aline De Lima Leite, Heidi E. Roth, Jiri Adamec, Robert Powers, James W. Checco

Chemistry Department: Faculty Publications

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic and debilitating pain disorder of the bladder and urinary tract with poorly understood etiology. A definitive diagnosis of IC/BPS can be challenging because many symptoms are shared with other urological disorders. An analysis of urine presents an attractive and non-invasive resource for monitoring and diagnosing IC/BPS. The antiproliferative factor (APF) peptide has been previously identified in the urine of IC/BPS patients and is a proposed biomarker for the disorder. Nevertheless, other small urinary peptides have remained uninvestigated in IC/BPS primarily because protein biomarker discovery efforts employ protocols that remove small endogenous peptides. …

Robust Testing Of Paired Outcomes Incorporating Covariate Effects In Clustered Data With Informative Cluster Size, Sandipan Dutta

Mathematics & Statistics Faculty Publications

Paired outcomes are common in correlated clustered data where the main aim is to compare the distributions of the outcomes in a pair. In such clustered paired data, informative cluster sizes can occur when the number of pairs in a cluster (i.e., a cluster size) is correlated to the paired outcomes or the paired differences. There have been some attempts to develop robust rank-based tests for comparing paired outcomes in such complex clustered data. Most of these existing rank tests developed for paired outcomes in clustered data compare the marginal distributions in a pair and ignore any covariate effect on …

Refinement Of Alphafold2 Models Against Experimental And Hybrid Cryo-Em Density Maps, Maytha Alshammari, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Recent breakthroughs in deep learning-based protein structure prediction show that it is possible to obtain highly accurate models for a wide range of difficult protein targets for which only the amino acid sequence is known. The availability of accurately predicted models from sequences can potentially revolutionise many modelling approaches in structural biology, including the interpretation of cryo-EM density maps. Although atomic structures can be readily solved from cryo-EM maps of better than 4 Å resolution, it is still challenging to determine accurate models from lower-resolution density maps. Here, we report on the benefits of models predicted by AlphaFold2 (the best-performing …

Quantitative Raman Analysis Of Carotenoid Protein Complexes In Aqueous Solution, Joy Udensi, Ekaterina Loskutova, James Loughman, Hugh Byrne

Datasets

Carotenoids are naturally abundant fat-soluble pigmented compounds, with dietary, antioxidant and vision protection advantages. The dietary carotenoids, Beta Carotene, Lutein and Zeaxanthin, complexed with in bovine serum albumin (BSA) in aqueous solution, were explored using Raman spectroscopy to differentiate and quantify their spectral signatures. UV visible absorption spectroscopy was employed to confirm the linearity of responses over the concentration range employed (0.05-1mg/ml) and, of the 4 source wavelengths, 785nm, 660nm, 532nm, 473nm, 532nm was chosen to provide the optimal response. After preprocessing to remove water and BSA contributions, and correct for self-absorption, a partial least squares model with R^{2 …}

Why Do Family Members Reject Ai In Health Care? Competing Effects Of Emotions, Eun Hee Park, Karl Werder, Lan Cao, Balasubramaniam Ramesh

Information Technology & Decision Sciences Faculty Publications

Artificial intelligence (AI) enables continuous monitoring of patients’ health, thus improving the quality of their health care. However, prior studies suggest that individuals resist such innovative technology. In contrast to prior studies that investigate individuals’ decisions for themselves, we focus on family members’ rejection of AI monitoring, as family members play a significant role in health care decisions. Our research investigates competing effects of emotions toward the rejection of AI monitoring for health care. Based on two scenario-based experiments, our study reveals that emotions play a decisive role in family members’ decision making on behalf of their parents. We find …

Construction Of A Repeatable Framework For Prostate Cancer Lesion Binary Semantic Segmentation Using Convolutional Neural Networks, Ian Vincent O. Mirasol, Patricia Angela R. Abu, Rosula Sj Reyes

Department of Information Systems & Computer Science Faculty Publications

Prostate cancer is the 3rd most diagnosed cancer overall. Current screening methods such as the prostate-specific antigen test could result in overdiagonosis and overtreatment while other methods such as a transrectal ultrasonography are invasive. Recent medical advancements have allowed the use of multiparametric MRI — a noninvasive and reliable screening process for prostate cancer. However, assessment would still vary from different professionals introducing subjectivity. While con-volutional neural network has been used in multiple studies to ob-jectively segment prostate lesions, due to the sensitivity of datasets and varying ground-truth established used in these studies, it is not possible to reproduce and …

Automated Classification Model With Otsu And Cnn Method For Premature Ventricular Contraction Detection, Liang-Hung Wang, Lin-Juan Ding, Chao-Xin Xie, Su-Ya Jiang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Pao-Cheng Huang, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm …

Aptamer-Based Voltammetric Biosensing For The Detection Of Codeine And Fentanyl In Sweat And Saliva, Rosa Lashantez Cromartie

FIU Electronic Theses and Dissertations

Despite the many governmental and medicinal restrictions created to combat the opioid epidemic in the United States, opioid abuse and overdose rates continue to rise. The development of an aptamer-based voltammetric sensor and biosensor is described in this dissertation. The aim was to develop a low-cost, sensitive, and specific aptamer-based sensor for on-site, label-free determination of codeine and fentanyl in biological fluids. To do this, the surfaces of screen-printed carbon electrodes (SPCE) were modified with gold nanoparticles (AuNPs), followed by the addition of single-stranded DNA aptamers. These were covalently bound to the electrode surface. Operations of the sensors were collected …

Detection Of Dental Apical Lesions Using Cnns On Periapical Radiograph, Chun-Wei Li, Szu-Yin Lin, He-Sheng Chou, Tsung-Yi Chen, Yu-An Chen, Sheng-Yu Liu, Yu-Lin Liu, Chiung-An Chen, Yen-Cheng Huang, Shih-Lun Chen, Yi-Cheng Mao, Patricia Angela R. Abu, Wei-Yuan Chiang, Wen-Shen Lo

Department of Information Systems & Computer Science Faculty Publications

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or …

Cyclic Peptide-Gadolinium Nanocomplexes As Sirna Delivery Tools, Amir Nasrolahi Shirazi, Muhammad Imran Sajid, Dindyal Mandal, David Stickley, Stephanie Nagasawa, Joshua Long, Sandeep Lohan, Keykavous Parang, Rakesh Kumar Tiwari

Pharmacy Faculty Articles and Research

We have recently reported that a cyclic peptide containing five tryptophan, five arginine, and one cysteine amino acids [(WR)₅C], was able to produce peptide-capped gadolinium nanoparticles, [(WR)₅C]-GdNPs, in the range of 240 to 260 nm upon mixing with an aqueous solution of GdCl₃. Herein, we report [(WR)₅C]-GdNPs as an efficient siRNA delivery system. The peptide-based gadolinium nanoparticles (50 µM) did not exhibit significant cytotoxicity (~93% cell viability at 50 µM) in human leukemia T lymphoblast cells (CCRF-CEM) and triple-negative breast cancer cells (MDA-MB-231) after 48 h. Fluorescence-activated cell sorting (FACS) analysis indicated …

From Mathematics To Medicine: A Practical Primer On Topological Data Analysis (Tda) And The Development Of Related Analytic Tools For The Functional Discovery Of Latent Structure In Fmri Data, Andrew Salch, Adam Regalski, Hassan Abdallah, Raviteja Suryadevara, Michael J. Catanzaro, Vaibhav A. Diwadkar

Mathematics Faculty Research Publications

fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. …

A High-Precision Machine Learning Algorithm To Classify Left And Right Outflow Tract Ventricular Tachycardia, Jianwei Zhang, Guohua Fu, Islam Abudayyeh, Magdi Yacoub, Anthony Chang, William Feaster, Louis Ehwerhemuepha, Hesham El-Askary, Xianfeng Du, Bin He, Mingjun Feng, Yibo Yu, Binhao Wang, Jing Liu, Hai Yao, Hulmin Chu, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Introduction: Multiple algorithms based on 12-lead ECG measurements have been proposed to identify the right ventricular outflow tract (RVOT) and left ventricular outflow tract (LVOT) locations from which ventricular tachycardia (VT) and frequent premature ventricular complex (PVC) originate. However, a clinical-grade machine learning algorithm that automatically analyzes characteristics of 12-lead ECGs and predicts RVOT or LVOT origins of VT and PVC is not currently available. The effective ablation sites of RVOT and LVOT, confirmed by a successful ablation procedure, provide evidence to create RVOT and LVOT labels for the machine learning model.

Methods: We randomly sampled training, validation, and testing …

The Potential Of Raman Spectroscopy In The Diagnosis Of Dysplastic And Malignant Oral Lesions, Ola Ibrahim, M. Toner, Steven Flint, Hugh Byrne, Fiona Lyng

Articles

Early diagnosis, treatment and/or surveillance of oral premalignant lesions are important in preventing progression to oral squamous cell carcinoma (OSCC). The current gold standard is through histopathological diagnosis, which is limited by inter and intra observer and sampling errors. The objective of this work was to use Raman spectroscopy to discriminate between benign, mild, moderate and severe dysplasia and OSCC in formalin fixed paraffin preserved (FFPP) tissues. The study included 72 different pathologies from which 17 were benign lesions, 20 mildly dysplastic, 20 moderately dysplastic, 10 severely dysplastic and 5 invasive OSCC. The glass substrate and paraffin wax background were …

Biomedical Applications Of Vibrational Spectroscopy: Oral Cancer Diagnostics, Hugh Byrne, Isha Behl, Genecy Calado, Ola Ibrahim, M. Toner, Sheila Galvin, Claire M. Healy, Steven Flint, Fiona Lyng

Articles

Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.

Resident Heart Rate Variability During Cataract Surgery, Ahmad Baiyasi, Shibandri Das, Ferris Bayasi, Faisal Ridha Al-Timimi

Medical Student Research Symposium

Purpose: To evaluate ophthalmology resident anxiousness and cardiovascular response by tracking resident heart rate (HR) when performing cataract surgery during their last year of residency.

Methods: A prospective analysis of 31 cataract cases, completed by three residents (two females and one male), at the Kresge Eye Institute in August and September 2020 was performed. Inclusion criteria for cases included all cataract cases performed by PGY-4 residents at the Kresge Eye Institute who downloaded the Heart Graph app supported by iOS. Residents with android mobile devices were excluded from the study. Informed consent was obtained from all residents who utilized the …

Adaptive Physics-Based Non-Rigid Registration For Immersive Image-Guided Neuronavigation Systems, Fotis Drakopoulos, Christos Tsolakis, Angelos Angelopoulos, Yixun Liu, Chengjun Yao, Kyriaki Rafailia Kavazidi, Nikolaos Foroglou, Andrey Fedorov, Sarah Frisken, Ron Kikinis, Alexandra Golby, Nikos Chrisochoides

Computer Science Faculty Publications

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A …

The Enlightening Role Of Explainable Artificial Intelligence In Chronic Wound Classification, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Umit Cali, Ozgur Guler

Engineering Technology Faculty Publications

Artificial Intelligence (AI) has been among the most emerging research and industrial application fields, especially in the healthcare domain, but operated as a black-box model with a limited understanding of its inner working over the past decades. AI algorithms are, in large part, built on weights calculated as a result of large matrix multiplications. It is typically hard to interpret and debug the computationally intensive processes. Explainable Artificial Intelligence (XAI) aims to solve black-box and hard-to-debug approaches through the use of various techniques and tools. In this study, XAI techniques are applied to chronic wound classification. The proposed model classifies …

Tri-Molybdenum Phosphide (Mo3P) And Multi-Walled Carbon Nanotube Junctions For Volatile Organic Compounds (Vocs) Detection, Baleeswaraiah Muchharla, Praveen Malali, Brenna Daniel, Alireza Kondori, Mohammad Asadi, Wei Cao, Hani E. Elsayed-Ali, Mickaël Castro, Mehran Elahi, Adetayo Adedeji, Kishor Kumar Sadasivuni, Muni Raj Mauya, Kapil Kumar, Abdennaceur Karoui, Bijandra Kumar

Electrical & Computer Engineering Faculty Publications

Detection and analysis of volatile organic compounds’ (VOCs) biomarkers lead to improvement in healthcare diagnosis and other applications such as chemical threat detection and food quality control. Here, we report on tri-molybdenum phosphide (Mo₃P) and multi- walled carbon nanotube (MWCNT) junction-based vapor quantum resistive sensors (vQRSs), which exhibit more than one order of magni- tude higher sensitivity and superior selectivity for biomarkers in comparison to pristine MWCNT junctions based vQRSs. Transmission electron microscope/scanning tunneling electron microscope with energy dispersive x-ray spectroscopy, x-ray diffraction, and x-ray photo- electron spectroscopy studies reveal the crystallinity and the presence of Mo and …

A Neutrosophic Clinical Decision-Making System For Cardiovascular Diseases Risk Analysis, Florentin Smarandache, Shaista Habib, Wardat-Us- Salam, M. Arif Butt, Muhammad Akram

Branch Mathematics and Statistics Faculty and Staff Publications

Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirtyfive symptoms as input parameters, which are, gender, …

A Single-Center Comparison Using Exoskeleton Rehabilitation For Cerebrovascular Accidents And Traumatic Brain Injury In A Cohort Of Hispanic Patients, Lisa R. Trevino, Kristina Vatcheva, Michael E. Auer, Angela Morales, Lama M. Abdurrahman, Sarajova Viswamitra, Annelyn Torres-Reveron

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Background Traumatic brain injury (TBI) is one of the leading causes of disability in the United States. The EKSO GT Bionics® (EKSO®) is a robotic exoskeleton approved by the Federal Drug Administration (FDA) for rehabilitation following a cerebrovascular accident (CVA or stroke) and recently received approval for use in patients with TBI. The aim of the study was to examine if the use of exoskeleton rehabilitation in patients with TBI will produce beneficial outcomes.

Methods This retrospective chart-review reports the use of the (EKSO®) robotic device in the rehabilitation of patients with TBI compared to patients with CVA. We utilized …

Mapping Gadolinium Contrast In A Complex Ionic And Photosynthesis Environment Of Pineapple By Near-Infrared And X-Ray Imaging, Subhendra Sarkar, Zoya Vinokur, Chen Xu, Tetiana Soloviova, Amina Shahbaz, Aldona Gjoni

Publications and Research

This work explores the diffusivity of a lanthanide complex, Eovist (Gadolinium-Ethoxy Benzyl Diethylenetriamine pentaacetate) that is stable in neutral media but is not in acidic environment. In the current work an acidic fruit model like pineapple that is rich in transition metals was used and a possible transmetallation reaction among Eovist and transition metal complexes was tested using X-ray imaging. Another goal of this work was to perturb the usual and the unusual photosynthesis systems that pineapple has maintained for millions of years during the evolution of circadian genes for efficient water conservation by dark photosynthesis. To detect such photosynthesis …

Variability In The Analysis Of A Single Neuroimaging Dataset By Many Teams, Rotem Botvinik-Nezer, Tom Schonberg, Russell A. Poldrack, Zachary J. Cole, Matthew R. Johnson, Phui Cheng Lim, Evan N. Linz, Douglas H. Schultz, Joshua E. Zosky, Narps Management Team, Jean M. Vettel, More Than 100 Other Co-Authors

Department of Psychology: Faculty Publications

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, …

A Review Of Major Role Of Vitamin D3 In Human Immune System And Its Possible Use For Novel Corona Virus Treatment, Florentin Smarandache, Victor Christianto

Branch Mathematics and Statistics Faculty and Staff Publications

the evidences showing major role of Vitamin D3 in human immune system and its potential use for novel corona virus treatment. Our argument is based on research finding that corona virus has viral envelope glycoproteins. In this regard, Vitamin D3 proves to offer various beneficial effects, including immunomodulatory effect, in order to break the glycoproteins envelope of the virus. One of the greatest benefit of vitamin D3 is the fact that it is easy to get 10,000 - 20,000 IU of daily intake requirement, by sunbathing for more or less twenty minutes. Such a method is likely applicable in many …

Development Of Gaussian Learning Algorithms For Early Detection Of Alzheimer's Disease, Chen Fang

FIU Electronic Theses and Dissertations

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer's Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot …

Artificial Intelligence: A New Paradigm In Obstetrics And Gynecology Research And Clinical Practice, Pulwasha Iftikhar, Marcela V. Kuijpers, Azadeh Khayyat, Aqsa Iftikhar, Maribel Degouvia De Sa

Publications and Research

Artificial intelligence (AI) is growing exponentially in various fields, including medicine. This paper reviews the pertinent aspects of AI in obstetrics and gynecology (OB/GYN) and how these can be applied to improve patient outcomes and reduce the healthcare costs and workload for clinicians.

Herein, we will address current AI uses in OB/GYN, and the use of AI as a tool to interpret fetal heart rate (FHR) and cardiotocography (CTG) to aid in the detection of preterm labor, pregnancy complications, and review discrepancies in its interpretation between clinicians to reduce maternal and infant morbidity and mortality. AI systems can be used …

Optimal Multi-Stage Arrhythmia Classification Approach, Jianwei Zhang, Huimin Chu, Daniele Struppa, Jianming Zhang, Sir Magdi Yacoub, Hesham El-Askary, Anthony Chang, Louis Ehwerhemuepha, Islam Abudayyeh, Alexander Barrett, Guohua Fu, Hai Yao, Dongbo Li, Hangyuan Guo, Cyril Rakovski

Mathematics, Physics, and Computer Science Faculty Articles and Research

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead …

Color-Based Template Selection For Detection Of Gastric Abnormalities In Video Endoscopy, Hussam Ali, Muhammad Sharif, Mussarat Yasmin, Mubashir Husain Rehmani

Publications

Computer-aided diagnosis of gastric diseases from endoscopy frames is an important task. It facilitates both the patient and gastroenterologist in terms of time, money and most important health. Colors are the basic visual features of endoscopic images and also provide clues about abnormal regions in endoscopy frames. A variety of color spaces available for representation of color frames. However, we are not certain about which color space is more suitable for representing color features of gastric images. This paper presents a comparison of color features in different color spaces for detection of abnormal areas in chromoendoscopy (CH) frames. In addition, …

Robotically Steered Needles: A Survey Of Neurosurgical Applications And Technical Innovations, Michel A. Audette, Stéphane P.A. Bordas, Jason E. Blatt

Computational Modeling & Simulation Engineering Faculty Publications

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies. This paper also describes foundational mathematical models for steering, where potential fields, nonholonomic bicycle-like models, spring models, and stochastic approaches are cited. In addition, practical path planning systems are also addressed, where we cite uncertainty modeling in path planning, intraoperative soft tissue …

A Short Remark On Multipurpose Laser Therapy “Helios” In Ukraine And Its Potential Application For Treatment Of Neurology Disorders, Florentin Smarandache, Volodymyr Krasnoholovets, Victor Christianto, Rizha Vitania, The Houw Liong

Branch Mathematics and Statistics Faculty and Staff Publications

However, a truly multipurpose laser therapy method is very rarely available. Here we introduce a multipurpose laser therapy device in Ukraine, which is capable to take care a multitude of diseases. It is called “Helios”, by one of us (VK). We also give a case where a patient who suffered from Covid-19 has been treated successfully until he is recovered to healthy condition. In the last section we also discuss potential future application of Helios for other fields; i.e. neurology disorders.

Quantifying The Varying Predictive Value Of Physical Activity Measures Obtained From Wearable Accelerometers On All-Cause Mortality Over Short To Medium Time Horizons In Nhanes 2003-2006, Lucia Tabacu, Mark Ledbetter, Andrew Leroux, Ciprian Crainiceanu, Ekaterina Smirnova

Mathematics & Statistics Faculty Publications

Physical activity measures derived from wearable accelerometers have been shown to be highly predictive of all-cause mortality. Prediction models based on traditional risk factors and accelerometry-derived physical activity measures are developed for five time horizons. The data set contains 2978 study participants between 50 and 85 years old with an average of 13.08 years of follow-up in the NHANES 2003–2004 and 2005–2006. Univariate and multivariate logistic regression models were fit separately for five datasets for one- to five-year all-cause mortality as outcome (number of events 46, 94, 155, 218, and 297, respectively). In univariate models the total activity count (TAC) …