Medicine and Health Sciences Commons^™

Nurses’ Perceptions Of Artificial Intelligence (Ai) Integration Into Practice: An Integrative Review, Lester Lora, Paula Foran

Journal of Perioperative Nursing

Introduction: The integration of artificial intelligence (AI) technologies into health care is revolutionising nursing practice, substantially impacting patient care, clinical decision-making and health system efficiency. This integrative literature review explores the perceptions, attitudes and concerns of nurses regarding the use of AI use in clinical settings.

Review methods: A comprehensive literature search was undertaken using Health Source: Nursing/Academic Edition, EBSCO: MEDLINE Complete, CINAHL and Scopus. Search terms included ‘artificial intelligence’, ‘AI’, ‘A.I.’, ‘machine learning’, ‘nurse’ and ‘nursing practice, and ‘opinion’, ‘idea’, ‘insight’, ‘perspective’, ‘concern’ and ‘perception’. The terms were combined using Boolean operators (AND, OR) to refine the search. ‘Citing …

Bridging The Global Gap Of Blindness Through Artificial Intelligence - Exploring The Tools Of Ai To Address The Top Causes Of Blindness In Under-Resourced Communities Worldwide, Nathan Delacth, Bs

inSIGHT

Technological advancements have allowed us to submerge in a sea of innovation and excellence in medicine. Electronic health records transformed the healthcare landscape, improving portability of patient information while streamlining communication and fostering collaboration.¹ Imaging technologies, such as magnetic resonance imaging (MRI) and Optical computed tomography (OCT), granted us the ability to view internal structures using non-invasive methods. In a similar vein, artificial intelligence (AI) has emerged as an impactful force in various fields of medicine, and its influence on ophthalmology is no exception.

Serum Metabolomic Profiling For Colorectal Cancer Using Machine Learning, Ria Nur Puspa Sari, Diah Balqis Ikfi Hidayati, Arleni Bustami

Indonesian Journal of Medical Chemistry and Bioinformatics

Background: Colorectal cancer is one of the deadliest diseases with a high prevalence worldwide and is characterized by the appearance of adenomatous polyps in the colon mucosa which are at high risk of developing into colorectal cancer. This study aims to use serum metabolites as promising non-invasive biomarkers for colorectal cancer detection and prognostication. Differences in serum metabolites in patients with adenomatous polyps, colorectal cancer, and healthy controls are considered to be able to support the prognosis of colorectal cancer. Methods: Metabolite dataset is taken from the Metabolomic Workbench. Analysis and validation are carried out in silico using machine learning …

Exploration Of Data Science Toolbox And Predictive Models To Detect And Prevent Medicare Fraud, Waste, And Abuse, Benjamin P. Goodwin, Adam Canton, Babatunde Olanipekun

SMU Data Science Review

The Federal Department of Health and Human Services spends approximately $830 Billion annually on Medicare of which an estimated $30 to $110 billion is some form of fraud, waste, or abuse (FWA). Despite the Federal Government’s ongoing auditing efforts, fraud, waste, and abuse is rampant and requires modern machine learning approaches to generalize and detect such patterns. New and novel machine learning algorithms offer hope to help detect fraud, waste, and abuse. The existence of publicly accessible datasets complied by The Centers for Medicare & Medicaid Services (CMS) contain vast quantities of structured data. This data, coupled with industry standardized …

Biomarker Metabolite Discovery For Pancreatic Cancer Using Machine Learning, Immanuelle Kezia, Linda Erlina, Aryo Tedjo, Fadilah Fadilah

Indonesian Journal of Medical Chemistry and Bioinformatics

Pancreatic cancer is one of the deadliest cancers in the world. This cancer is caused by multiple factors and mostly detected at late stadium. Biomarker is a marker that can identify some diseases very specific. For pancreatic cancer, biomarker has been recognized using blood sample known as liquid biopsy, breath, pancreatic secret, and tumor marker CA19-9. Those biomarkers are invasive, so we want to identify the disease using a very convenient method. Metabolite is product from cell metabolism. Metabolites can become a biomarker especially from difficult diseases. In this paper, we want to find biomarker from metabolite using machine learning …

The Real-Time Classification Of Competency Swimming Activity Through Machine Learning, Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

International Journal of Aquatic Research and Education

Every year, an average of 3,536 people die from drowning in America. The significant factors that cause unintentional drowning are people’s lack of water safety awareness and swimming proficiency. Current industry and research trends regarding swimming activity recognition and commercial motion sensors focus more on lap swimming utilized by expert swimmers and do not account for freeform activities. Enhancing swimming education through wearable technology can aid people in learning efficient and effective swimming techniques and water safety. We developed a novel wearable system capable of storing and processing sensor data to categorize competitive and survival swimming activities on a mobile …

Electrocardiogram-Based Machine Learning Emulator Model For Predicting Novel Echocardiography-Derived Phenogroups For Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study, Heenaben B. Patel, Naveena Yanamala, Brijesh Patel, Sameer Raina, Peter D. Farjo, Srinidhi Sunkara, Márton Tokodi, Nobuyuki Kagiyama, Grace Casaclang-Verzosa, Partho P. Sengupta

Journal of Patient-Centered Research and Reviews

Purpose: Electrocardiography (ECG)-derived machine learning models can predict echocardiography (echo)-derived indices of systolic or diastolic function. However, systolic and diastolic dysfunction frequently coexists, which necessitates an integrated assessment for optimal risk-stratification. We explored an ECG-derived model that emulates an echo-derived model that combines multiple parameters for identifying patient phenogroups at risk for major adverse cardiac events (MACE).

Methods: In this substudy of a prospective, multicenter study, patients from 3 institutions (n = 727) formed an internal cohort, and the fourth institution was reserved as an external test set (n = 518). A previously validated patient similarity analysis model was used …

Toward Suicidal Ideation Detection With Lexical Network Features And Machine Learning, Ulya Bayram, William Lee, Daniel Santel, Ali Minai, Peggy Clark, Tracy Glauser, John Pestian

Northeast Journal of Complex Systems (NEJCS)

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts …

Impact Of Sleep And Training On Game Performance And Injury In Division-1 Women’S Basketball Amidst The Pandemic, Samah Senbel, S. Sharma, S. M. Raval, Christopher B. Taber, Julie K. Nolan, N. S. Artan, Diala Ezzeddine, Kaya Tolga

School of Computer Science & Engineering Faculty Publications

We investigated the impact of sleep and training load of Division - 1 women’s basketball players on their game performance and injury prediction using machine learning algorithms. The data was collected during a pandemic-condensed season with unpredictable interruptions to the games and athletic training schedules. We collected data from sleep monitoring devices, training data from coaches, injury reports from medical staff, and weekly survey data from athletes for 22 weeks.With proper data imputation, interpretable feature set, data balancing, and classifiers, we showed that we could predict game performance and injuries with more than 90% accuracy. More importantly, our F1 and …

Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho

SMU Data Science Review

Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …

Examining The Viability Of Computational Psychiatry: Approaches Into The Future, Mitchell Ostrow

The Yale Undergraduate Research Journal

As modern medicine becomes increasingly personalized, psychiatry lags behind, using poorly-understood drugs and therapies to treat mental disorders. With the advent of methods that capture large quantities of data, such as genome-wide analyses or fMRI, machine learning (ML) approaches have become prominent in neuroscience. This is promising for studying the brain’s function, but perhaps more importantly, these techniques can potentially predict the onset of disorder and treatment response. Experimental approaches that use naive machine learning algorithms have dominated research in computational psychiatry over the past decade. In a critical review and analysis, I argue that biologically realistic approaches will be …

Validation Of A Single Channel Eeg For The Athlete: A Machine Learning Protocol To Accurately Detect Sleep Stages, Kayla Thompson, Kamil Celoch, Frankie Pizzo, Ana I. Fins, Jaime Tartar

Journal for Sports Neuroscience

There is a large and growing movement towards the use of wearable technologies for sleep assessment. This trend is largely due to the desire for comfortable, burden free, and inexpensive technology. In tandem, given the competitive nature of professional athletes enduring high training load, sleep is often jeopardized which can result in adverse outcomes. Wearable devices hold the promise of increasing the ease of monitoring sleep in athletes which can inform health and recovery status, as well as aid performance optimization. However, wearable devices typically lack sufficient validity to assess sleep – and especially sleep stages. To address this concern, …

Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova

SMU Data Science Review

Cardiovascular diseases, Congestive Heart Failure in particular, are a leading cause of deaths worldwide. Congestive Heart Failure has high mortality and morbidity rates. The key to decreasing the morbidity and mortality rates associated with Congestive Heart Failure is determining a method to detect high-risk individuals prior to the development of this often-fatal disease. Providing high-risk individuals with advanced knowledge of risk factors that could potentially lead to Congestive Heart Failure, enhances the likelihood of preventing the disease through implementation of lifestyle changes for healthy living. When dealing with healthcare and patient data, there are restrictions that led to difficulties accessing …

Conference Proceedings: Aurora Scientific Day 2020

Journal of Patient-Centered Research and Reviews

Abstracts published in this supplement were among those presented at the 46th annual Aurora Scientific Day research symposium, held virtually on May 20, 2020. The symposium provides a forum for describing research studies conducted by faculty, fellows, residents, and allied health professionals affiliated with Wisconsin-based Aurora Health Care, a part of the Advocate Aurora Health health system, which publishes the Journal of Patient-Centered Research and Reviews.

Comparison Of Machine Learning Models: Gesture Recognition Using A Multimodal Wrist Orthosis For Tetraplegics, Charlie Martin

The Journal of Purdue Undergraduate Research

Many tetraplegics must wear wrist braces to support paralyzed wrists and hands. However, current wrist orthoses have limited functionality to assist a person’s ability to perform typical activities of daily living other than a small pocket to hold utensils. To enhance the functionality of wrist orthoses, gesture recognition technology can be applied to control mechatronic tools attached to a novel fabricated wrist brace. Gesture recognition is a growing technology for providing touchless human-computer interaction that can be particularly useful for tetraplegics with limited upper-extremity mobility. In this study, three gesture recognition models were compared—two dynamic time-warping models and a hidden …

Heart Rate Variability: A Possible Machine Learning Biomarker For Mechanical Circulatory Device Complications And Heart Recovery, Theodore Lin, Zain Khalpey, Shravan Aras

The VAD Journal

Cardiovascular disease continues to be the number one cause of death in the United States, with heart failure patients expected to increase to >8 million by 2030. Mechanical circulatory support (MCS) devices are now better able to manage acute and chronic heart failure refractory to medical therapy, both as bridge to transplant or as bridge to destination. Despite significant advances in MCS device design and surgical implantation technique, it remains difficult to predict response to device therapy. Heart rate variability (HRV), measuring the variation in time interval between adjacent heartbeats, is an objective device diagnostic regularly recorded by various MCS …

Clinical Research In Pneumonia: Role Of Artificial Intelligence, Timothy L. Wiemken, Robert R. Kelley, William A. Mattingly, Julio A. Ramirez

The University of Louisville Journal of Respiratory Infections

No abstract provided.

Predicting The Body Weight Of Balochi Sheep Using A Machine Learning Approach, Zil E. Huma, Farhat Iqbal

Turkish Journal of Veterinary & Animal Sciences

Various machine learning algorithms have been used to model and predict the body weight of rams of the Balochi sheep breed of Pakistan. The traditional generalized linear model along with regression trees, support vector machine, and random forests methods have been used to develop models for the prediction of the body weight of animals. The independent variables (inputs) include the body (body length, heart girth, withers height) and testicular (scrotal diameter, scrotal circumference, scrotal length, and testicular length) measurements of 131 male sheep 2-36 months of age. The performance of the models is assessed based on evaluation criteria of mean …

Predicting Adverse Outcomes In End Stage Renal Disease: Machine Learning Applied To The United States Renal Data System, Zeid Khitan, Alexis D. Jacob, Courtney Balentine, Adam N. Jacob, Juan R. Sanabria, Joseph I. Shapiro

Marshall Journal of Medicine

We examined machine learning methods to predict death within six months using data derived from the United States Renal Data System (USRDS). We specifically evaluated a generalized linear model, a support vector machine, a decision tree and a random forest evaluated within the context of K-10 fold validation using the CARET package available within the open source architecture R program. We compared these models with the feed forward neural network strategy that we previously reported on with this data set.

Outcomes Of Transcutaneous Aortic Valve Replacement Among High Risk Wv Sample Population., George M. Yousef, Julia Poe, Cameron Killmer, Basel Edris, Jason Mader, Ellen A. Thompson, Daniel Snavely, Silvestre Cansino, Joseph I. Shapiro, Mark A. Studeny

Marshall Journal of Medicine

Introduction:Transcatheter aortic valve replacement (TAVR) is a relatively new strategy for replacing the aortic valve. We elected to review our early experience to see if we could identify clinical characteristics at baseline or immediately following the procedure that would predict death within one year.

Methods:Charts for all patients assigned to receive TAVR procedure at St Mary’s medical center, Huntington, West Virginia between April, 2013 till November, 2016 were identified and reviewed. A total of seventy-two (72) cases were included.

Results: All cause mortality rate at index hospitalization, 30 days, and 12 months was 5.6%(N=4), 6.9%(N=5), 19.4%(N=14) respectively. Stroke …

Open Cycle: Forecasting Ovulation For Family Planning, Karen Clark, Mridul Jain, Araya Messa, Vinh Le, Eric C. Larson

SMU Data Science Review

Abstract: Forecasting the length and different phases of a woman’s menstrual cycle, especially ovulation, is an important aspect of family planning. Predicting fertility has many uses in family planning including avoiding pregnancy and assisting couples in becoming pregnant. Past methods have focused on monitoring basal body temperature (BBT), cervical mucus changes, and hormonal levels to determine fertility. While these methods can provide an accurate prediction of ovulation these tests can become expensive, time-consuming, and do not provide prediction until after ovulation has occurred. In this paper, we compare conventional fertility assessment that is based on a rule known as “three-over-six” …

Predicting Adverse Outcomes In Chronic Kidney Disease Using Machine Learning Methods: Data From The Modification Of Diet In Renal Disease, Zeid Khitan, Anna P. Shapiro, Preeya T. Shah, Juan R. Sanabria, Prasanna Santhanam, Komal Sodhi, Nader G. Abraham, Joseph I. Shapiro

Marshall Journal of Medicine

Background: Understanding factors which predict progression of renal failure is of great interest to clinicians.

Objectives: We examined machine learning methods to predict the composite outcome of death, dialysis or doubling of serum creatinine using the modification of diet in renal disease (MDRD) data set.

Methods: We specifically evaluated a generalized linear model, a support vector machine, a decision tree, a feed-forward neural network and a random forest evaluated within the context of 10 fold validation using the CARET package available within the open source architecture R program.

Results: We found that using clinical parameters available at entry into the …

Predicting 30-Day Mortality In Hospitalized Patients With Community-Acquired Pneumonia Using Statistical And Machine Learning Approaches, Timothy L. Wiemken, Stephen P. Furmanek, William A. Mattingly, Brian E. Guinn, Rodrigo Cavallazzi, Rafael Fernandez-Botran, Leslie A Wolf, Connor L. English, Julio A. Ramirez

The University of Louisville Journal of Respiratory Infections

Background: Predicting if a hospitalized patient with community-acquired pneumonia (CAP) will or will not survive after admission to the hospital is important for research purposes as well as for institution of early patient management interventions. Although population-level mortality prediction scores for these patients have been around for many years, novel patient-level algorithms are needed. The objective of this study was to assess several statistical and machine learning models for their ability to predict 30-day mortality in hospitalized patients with CAP.

Methods: This was a secondary analysis of the University of Louisville (UofL) Pneumonia Study database. Six different statistical and/or machine …