913 results match your criteria data-driven tools

Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics.

Front Res Metr Anal 2021 24;6:683212. Epub 2021 May 24.

Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia.

The COVID-19 pandemic constitutes an ongoing worldwide threat to human society and has caused massive impacts on global public health, the economy and the political landscape. The key to gaining control of the disease lies in understanding the genetics of SARS-CoV-2 and the disease spectrum that follows infection. This study leverages traditional and intelligent bibliometric methods to conduct a multi-dimensional analysis on 5,632 COVID-19 genetic research papers, revealing that 1) the key players include research institutions from the United States, China, Britain and Canada; 2) research topics predominantly focus on virus infection mechanisms, virus testing, gene expression related to the immune reactions and patient clinical manifestation; 3) studies originated from the comparison of SARS-CoV-2 to previous human coronaviruses, following which research directions diverge into the analysis of virus molecular structure and genetics, the human immune response, vaccine development and gene expression related to immune responses; and 4) genes that are frequently highlighted include , , , and . Read More

View Article and Full-Text PDF

Child health and the implementation of Community and District-management Empowerment for Scale-up (CODES) in Uganda: a randomised controlled trial.

BMJ Glob Health 2021 Jun;6(6)

Department of Health Policy Planning and Management, Makerere University School of Public Health, Kampala, Uganda.

Introduction: Uganda's district-level administrative units buttress the public healthcare system. In many districts, however, local capacity is incommensurate with that required to plan and implement quality health interventions. This study investigates how a district management strategy informed by local data and community dialogue influences health services. Read More

View Article and Full-Text PDF

Derivation of a Risk Assessment Tool for Prediction of Long-Term Pain Intensity Reduction After Physical Therapy.

J Pain Res 2021 28;14:1515-1524. Epub 2021 May 28.

Duke University, Department of Orthopaedic Surgery and Duke Clinical Research Institute, Durham, NC, 27701, USA.

Rationale: Risk assessment tools can improve clinical decision-making for individuals with musculoskeletal pain, but do not currently exist for predicting reduction of pain intensity as an outcome from physical therapy.

Aims And Objective: The objective of this study was to develop a tool that predicts failure to achieve a 50% pain intensity reduction by 1) determining the appropriate statistical model to inform the tool and 2) select the model that considers the tradeoff between clinical feasibility and statistical accuracy.

Methods: This was a retrospective, secondary data analysis of the Optimal Screening for Prediction of Referral and Outcome (OSPRO) cohort. Read More

View Article and Full-Text PDF

Regional brain morphology predicts pain relief in trigeminal neuralgia.

Neuroimage Clin 2021 May 25;31:102706. Epub 2021 May 25.

Division of Brain, Imaging & Behaviour, Krembil Brain Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Surgery, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Division of Neurosurgery, Krembil Neuroscience Centre, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada. Electronic address:

Background: Trigeminal neuralgia, a severe chronic neuropathic pain disorder, is widely believed to be amenable to surgical treatments. Nearly 20% of patients, however, do not have adequate pain relief after surgery. Objective tools for personalized pre-treatment prognostication of pain relief following surgical interventions can minimize unnecessary surgeries and thus are of substantial benefit for patients and clinicians. Read More

View Article and Full-Text PDF

Effect of transportation and social isolation on facial expressions of healthy horses.

PLoS One 2021 4;16(6):e0241532. Epub 2021 Jun 4.

Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.

Horses have the ability to generate a remarkable repertoire of facial expressions, some of which have been linked to the affective component of pain. This study describes the facial expressions in healthy horses free of pain before and during transportation and social isolation, which are putatively stressful but ordinary management procedures. Transportation was performed in 28 horses by subjecting them to short-term road transport in a horse trailer. Read More

View Article and Full-Text PDF

Precision Surgery In Rectal Resection With Hyperspectral and Fluorescence Imaging And Pelvic Intraoperative Neuromonitoring (With Video).

Surg Technol Int 2021 Jun 3;38. Epub 2021 Jun 3.

Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany.

Oncologic visceral surgery has recently been revolutionized by robotics, artificial intelligence (AI), sparing of functionally important structures and innovative intraoperative imaging tools. These techniques enable new dimensions of precision surgery and oncology. Currently, data-driven, cognitive operating rooms are standing at the forefront of the latest technical and didactic developments in abdominal surgery. Read More

View Article and Full-Text PDF

Machine learning-based prediction of health outcomes in pediatric organ transplantation recipients.

JAMIA Open 2021 Jan 12;4(1):ooab008. Epub 2021 Mar 12.

School of Information, College of Communication and Information, Florida State University, Florida, USA.

Objectives: Prediction of post-transplant health outcomes and identification of key factors remain important issues for pediatric transplant teams and researchers. Outcomes research has generally relied on general linear modeling or similar techniques offering limited predictive validity. Thus far, data-driven modeling and machine learning (ML) approaches have had limited application and success in pediatric transplant outcomes research. Read More

View Article and Full-Text PDF
January 2021

Machine Learning Prediction of Fall Risk in Older Adults Using Timed Up and Go Test Kinematics.

Sensors (Basel) 2021 May 17;21(10). Epub 2021 May 17.

Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA 52242, USA.

Falls among the elderly population cause detrimental physical, mental, financial problems and, in the worst case, death. The increasing number of people entering the higher risk age-range has increased clinicians' attention to intervene. Clinical tools, e. Read More

View Article and Full-Text PDF

A Compression-Based Method for Detecting Anomalies in Textual Data.

Entropy (Basel) 2021 May 16;23(5). Epub 2021 May 16.

Institute of Physical and Information Technologies (ITEFI), Spanish National Research Council (CSIC), 28006 Madrid, Spain.

Nowadays, information and communications technology systems are fundamental assets of our social and economical model, and thus they should be properly protected against the malicious activity of cybercriminals. Defence mechanisms are generally articulated around tools that trace and store information in several ways, the simplest one being the generation of plain text files coined as security logs. Such log files are usually inspected, in a semi-automatic way, by security analysts to detect events that may affect system integrity, confidentiality and availability. Read More

View Article and Full-Text PDF

Peer groups for organisational learning: Clustering with practical constraints.

PLoS One 2021 1;16(6):e0251723. Epub 2021 Jun 1.

Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.

Peer-grouping is used in many sectors for organisational learning, policy implementation, and benchmarking. Clustering provides a statistical, data-driven method for constructing meaningful peer groups, but peer groups must be compatible with business constraints such as size and stability considerations. Additionally, statistical peer groups are constructed from many different variables, and can be difficult to understand, especially for non-statistical audiences. Read More

View Article and Full-Text PDF

InfoColorizer: Interactive Recommendation of Color Palettes for Infographics.

IEEE Trans Vis Comput Graph 2021 Jun 1;PP. Epub 2021 Jun 1.

When designing infographics, general users usually struggle with getting desired color palettes using existing infographic authoring tools, which sometimes sacrifice customizability, require design expertise, or neglect the influence of elements? spatial arrangement. We propose a data-driven method that provides flexibility by considering users? preferences, lowers the expertise barrier via automation, and tailors suggested palettes to the spatial layout of elements. We build a recommendation engine by utilizing deep learning techniques to characterize good color design practices from data, and further develop InfoColorizer, a tool that allows users to obtain color palettes for their infographics in an interactive and dynamic manner. Read More

View Article and Full-Text PDF

Structural classification of proteins based on the computationally efficient recurrence quantification analysis and horizontal visibility graphs.

Bioinformatics 2021 May 28. Epub 2021 May 28.

Department of Computer Science, University of Crete, Heraklion, 700 13, Greece.

Motivation: Protein structural class prediction is one of the most significant problems in bioinformatics, as it has a prominent role in understanding the function and evolution of proteins. Designing a computationally efficient but at the same time accurate prediction method remains a pressing issue, especially for sequences that we cannot obtain a sufficient amount of homologous information from existing protein sequence databases. Several studies demonstrate the potential of utilizing chaos game representation (CGR) along with time series analysis tools such as recurrence quantification analysis (RQA), complex networks, horizontal visibility graphs (HVG) and others. Read More

View Article and Full-Text PDF

A data-driven approach to post-stroke aphasia classification and lesion-based prediction.

Brain 2021 May 27. Epub 2021 May 27.

Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK.

Aphasia is an acquired impairment in the production or comprehension of language, typically caused by left hemisphere stroke. The subtyping framework used in clinical aphasiology today is based on the Wernicke-Lichtheim model of aphasia formulated in the late 19th century, which emphasizes the distinction between language production and comprehension. The current study used a data-driven approach that combined modern statistical, machine learning, and neuroimaging tools to examine behavioural deficit profiles and their lesion correlates and predictors in a large cohort of individuals with post-stroke aphasia. Read More

View Article and Full-Text PDF

LigAdvisor: a versatile and user-friendly web-platform for drug design.

Nucleic Acids Res 2021 May 22. Epub 2021 May 22.

Department of Life Sciences, University of Modena and Reggio Emilia, Modena 41125, Italy.

Although several tools facilitating in silico drug design are available, their results are usually difficult to integrate with publicly available information or require further processing to be fully exploited. The rational design of multi-target ligands (polypharmacology) and the repositioning of known drugs towards unmet therapeutic needs (drug repurposing) have raised increasing attention in drug discovery, although they usually require careful planning of tailored drug design strategies. Computational tools and data-driven approaches can help to reveal novel valuable opportunities in these contexts, as they enable to efficiently mine publicly available chemical, biological, clinical, and disease-related data. Read More

View Article and Full-Text PDF

The Evolution of the Healthy People Initiative: A Look Through the Decades.

J Public Health Manag Pract 2021 May 13. Epub 2021 May 13.

Office of Disease Prevention and Health Promotion, Office of the Assistant Secretary for Health, and Office of the Secretary, US Department of Health & Human Services, Rockville, Maryland (Mss Ochiai, Blakey, McGowan [formerly], and Lin).

Each decade, the US Department of Health & Human Services launches a new iteration of the Healthy People initiative (Healthy People). Healthy People strives to create a healthier nation and tracks data-driven outcomes to monitor progress toward achieving the initiative's goals throughout the decade. Although the initiative's mission, vision, and goals have evolved over time, since the initiative's inception in 1979, Healthy People remains dedicated to addressing the social determinants of health and improving the nation's health and well-being. Read More

View Article and Full-Text PDF

Data-Driven and -Assisted Design of Broad Host-Range Minimal Intrinsic Terminators Adapted for Bacteria.

ACS Synth Biol 2021 May 20. Epub 2021 May 20.

Key Laboratory of Industrial Biotechnology (MOE), School of Biotechnology, Jiangnan University, Wuxi, Jiangsu 214122, China.

Efficient transcription termination relying on intrinsic terminators is critical to maintain cell fitness by avoiding unwanted read-through in bacteria. Natural intrinsic terminator (NIT) typically appears in mRNA as a hairpin followed by approximately eight conserved uridines (U-tract) at the 3' terminus. Owing to their simple structure, small size, and protein independence, assorted NITs have been redesigned as robust tools to construct gene circuits. Read More

View Article and Full-Text PDF

Predicting Prolonged Length of Hospital Stay for Peritoneal Dialysis-Treated Patients Using Stacked Generalization: Model Development and Validation Study.

JMIR Med Inform 2021 May 19;9(5):e17886. Epub 2021 May 19.

National Institute of Health Data Science, Peking University, Beijing, China.

Background: The increasing number of patients treated with peritoneal dialysis (PD) and their consistently high rate of hospital admissions have placed a large burden on the health care system. Early clinical interventions and optimal management of patients at a high risk of prolonged length of stay (pLOS) may help improve the medical efficiency and prognosis of PD-treated patients. If timely clinical interventions are not provided, patients at a high risk of pLOS may face a poor prognosis and high medical expenses, which will also be a burden on hospitals. Read More

View Article and Full-Text PDF

Evaluating kin and group selection as tools for quantitative analysis of microbial data.

Proc Biol Sci 2021 May 19;288(1951):20201657. Epub 2021 May 19.

Department of Biology, University of Missouri-St Louis, St Louis MO 63121, USA.

Kin selection and multilevel selection theory are often used to interpret experiments about the evolution of cooperation and social behaviour among microbes. But while these experiments provide rich, detailed fitness data, theory is mostly used as a conceptual heuristic. Here, we evaluate how kin and multilevel selection theory perform as quantitative analysis tools. Read More

View Article and Full-Text PDF

Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features.

J Mammary Gland Biol Neoplasia 2021 May 17. Epub 2021 May 17.

Biomedical Imaging Group, School of Engineering, Ecole Polytechnique Fédéralé de Lausanne (EPFL), Lausanne, Switzerland.

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. Read More

View Article and Full-Text PDF

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care.

Womens Health (Lond) 2021 Jan-Dec;17:17455065211018111

Southern University of Science and Technology, Shenzhen, China.

To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities. Read More

View Article and Full-Text PDF

Polymer informatics with multi-task learning.

Patterns (N Y) 2021 Apr 9;2(4):100238. Epub 2021 Apr 9.

School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Read More

View Article and Full-Text PDF

Tracking changes in behavioural dynamics using prediction error.

PLoS One 2021 12;16(5):e0251053. Epub 2021 May 12.

Section of Ecology, Behavior, and Evolution, Division of Biological Sciences, University of California San Diego, La Jolla, California, United States of America.

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. Read More

View Article and Full-Text PDF

CrepHAN: Cross-species prediction of enhancers by using hierarchical attention networks.

Bioinformatics 2021 May 12. Epub 2021 May 12.

Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Rd., Shanghai 200240, China.

Motivation: Enhancers are important functional elements in genome sequences. The identification of enhancers is a very challenging task due to the great diversity of enhancer sequences and the flexible localization on genomes. Till now, the interactions between enhancers and genes have not been fully understood yet. Read More

View Article and Full-Text PDF

Inferring Thalamocortical Monosynaptic Connectivity In-Vivo.

J Neurophysiol 2021 May 12. Epub 2021 May 12.

Wallace H. Coulter Department of Biomedical Engineering, grid.213917.fGeorgia Institute of Technology, Atlanta, Georgia, United States.

As the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex-vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in-vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Read More

View Article and Full-Text PDF

Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model.

BMC Emerg Med 2021 May 10;21(1):60. Epub 2021 May 10.

Trauma Center of West China Hospital/West China School of Medicine, Sichuan University, Guo Xue Road 37#, Chengdu, 610041, China.

Background: Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. We try to verify the accuracy of the Caprini score for predicting VTE in trauma patients, and further improve the prediction through machine learning algorithms. Read More

View Article and Full-Text PDF

Back to the future: Reintegrating biology to understand how past eco-evolutionary change can predict future outcomes.

Integr Comp Biol 2021 May 8. Epub 2021 May 8.

Department of Plant and Microbial Biology, University of Minnesota,

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. Read More

View Article and Full-Text PDF

nanosafety assessment tools and their ecosystem-level integration prospect.

Nanoscale 2021 May;13(19):8722-8739

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.

Engineered nanomaterials (ENMs) have tremendous potential in many fields, but their applications and commercialization are difficult to widely implement due to their safety concerns. Recently, in silico nanosafety assessment has become an important and necessary tool to realize the safer-by-design strategy of ENMs and at the same time to reduce animal tests and exposure experiments. Here, in silico nanosafety assessment tools are classified into three categories according to their methodologies and objectives, including (i) data-driven prediction for acute toxicity, (ii) fate modeling for environmental pollution, and (iii) nano-biological interaction modeling for long-term biological effects. Read More

View Article and Full-Text PDF

Applying Lean Six Sigma to Improve Depression Screening and Follow-Up in Oncology Clinics.

J Healthc Qual 2021 May-Jun 01;43(3):153-162

Abstract: Depression is a common and serious illness that impairs the health of individuals and societies globally. It is associated with a significant economic burden, with productivity losses exceeding $40 billion dollars annually in the United States (U.S. Read More

View Article and Full-Text PDF

A data-driven approach to violin making.

Sci Rep 2021 May 4;11(1):9455. Epub 2021 May 4.

Musical Acoustics Lab at the Violin Museum of Cremona, DEIB-Politecnico di Milano, Cremona Campus, Cremona, Italy.

Of all the characteristics of a violin, those that concern its shape are probably the most important ones, as the violin maker has complete control over them. Contemporary violin making, however, is still based more on tradition than understanding, and a definitive scientific study of the specific relations that exist between shape and vibrational properties is yet to come and sorely missed. In this article, using standard statistical learning tools, we show that the modal frequencies of violin tops can, in fact, be predicted from geometric parameters, and that artificial intelligence can be successfully applied to traditional violin making. Read More

View Article and Full-Text PDF

DeepCompete : A deep learning approach to competing risks in continuous time domain.

AMIA Annu Symp Proc 2020 25;2020:177-186. Epub 2021 Jan 25.

University of Southern California, Los Angeles, California, USA.

An increasing number of people survive longer ages leading to a growing population of people 65 years of age or older. A large percentage of this population is afflicted with multiple acute diseases (multi-morbidity). Clinicians need new tools to quantify the relative risk of an adverse event due to each competing disease and prioritize treatment among various diseases affecting a patient. Read More

View Article and Full-Text PDF