19,614 results match your criteria predictive learning


Classification of first-episode psychosis using cortical thickness: A large multicenter MRI study.

Eur Neuropsychopharmacol 2021 May 3;47:34-47. Epub 2021 May 3.

Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

Machine learning classifications of first-episode psychosis (FEP) using neuroimaging have predominantly analyzed brain volumes. Some studies examined cortical thickness, but most of them have used parcellation approaches with data from single sites, which limits claims of generalizability. To address these limitations, we conducted a large-scale, multi-site analysis of cortical thickness comparing parcellations and vertex-wise approaches. Read More

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Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study.

JMIR Mhealth Uhealth 2021 May 6;9(5):e22591. Epub 2021 May 6.

Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

Background: The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are associated with an accelerated decline in lung function, diminished quality of life, and higher mortality. Accurate early detection of acute exacerbations will enable early management and reduce mortality. Read More

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A Novel 5-Cytokine Panel Outperforms Conventional Predictive Markers of Persistent Organ Failure in Acute Pancreatitis.

Clin Transl Gastroenterol 2021 May 6;12(5):e00351. Epub 2021 May 6.

Department of Medicine, Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.

Introduction: Existing laboratory markers and clinical scoring systems have shown suboptimal accuracies for early prediction of persistent organ failure (POF) in acute pancreatitis (AP). We used information theory and machine learning to select the best-performing panel of circulating cytokines for predicting POF early in the disease course and performed verification of the cytokine panel's prognostic accuracy in an independent AP cohort.

Methods: The derivation cohort included 60 subjects with AP with early serum samples collected between 2007 and 2010. Read More

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Cutting edge selection: learning from high reliability organisations for virtual recruitment in surgery during the COVID-19 pandemic.

Ann R Coll Surg Engl 2021 May 6. Epub 2021 May 6.

Portsmouth Hospitals University NHS Trust, UK.

Introduction: National selection for higher surgical training (ST3+) recruitment in the UK is competitive. The process must prioritise patient safety while being credible, impartial and fair. During the COVID-19 pandemic, all face-to-face interviews were cancelled. Read More

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Prediction of Disease Progression of COVID-19 Based upon Machine Learning.

Int J Gen Med 2021 29;14:1589-1598. Epub 2021 Apr 29.

Department of Gastroenterology, Daping Hospital, Army Medical University, Chongqing, People's Republic of China.

Background: Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression. Read More

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Deep direct likelihood knockoffs.

Adv Neural Inf Process Syst 2020 Dec;33:5036-5046

Courant Institute of Mathematical Sciences, Center for Data Science, New York University.

Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance. In scientific domains, the scientist often wishes to discover which features are actually important for making the predictions. These discoveries may lead to costly follow-up experiments and as such it is important that the error rate on discoveries is not too high. Read More

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December 2020

Tumor-associated Stromal Cellular Density as a Predictor of Recurrence and Mortality in Breast Cancer: Results from Ethnically-diverse Study Populations.

Cancer Epidemiol Biomarkers Prev 2021 May 5. Epub 2021 May 5.

Division of Cancer Epidemiology and Genetics, National Cancer Institute.

Background: Tumor-associated stroma is comprised of fibroblasts, tumor infiltrating lymphocytes (TILs), macrophages, endothelial, and other cells, that interactively influence tumor progression through inflammation and wound repair. Although gene expression signatures reflecting wound repair predict breast cancer survival, it is unclear whether combined density of tumor-associated stromal cells, a morphological proxy for inflammation and wound repair signatures on routine hematoxylin and eosin (H&E)-stained sections, is of prognostic relevance.

Methods: By applying machine learning to digitized H&E-stained sections for 2,084 breast cancer patients from China (n=596; 24-55years), Poland (n=810; 31-75years), and the United States (n=678; 55-78years), we characterized tumor-associated stromal cellular density (SCD) as the percentage of tumor-stroma that is occupied by nucleated cells. Read More

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Prediction of Chemosensitivity in Multiple Primary Cancer Patients Using Machine Learning.

Anticancer Res 2021 May;41(5):2419-2429

BK21 PLUS Project, Department of Dental Education, Yonsei University College of Dentistry, Yonsei University, Seoul, Republic of Korea

Background/aim: Many cancer patients face multiple primary cancers. It is challenging to find an anticancer therapy that covers both cancer types in such patients. In personalized medicine, drug response is predicted using genomic information, which makes it possible to choose the most effective therapy for these cancer patients. Read More

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A role for cardiopulmonary exercise testing in detecting physiological changes underlying health status in Idiopathic pulmonary fibrosis: a feasibility study.

BMC Pulm Med 2021 May 5;21(1):147. Epub 2021 May 5.

Academic Respiratory Unit, School of Clinical Sciences, Southmead Hospital, University of Bristol, Learning and Research Building, Bristol, BS10 5NB, UK.

Introduction: There is limited data available on the use of CPET as a predictive tool for disease outcomes in the setting of IPF. We investigated the feasibility of undertaking CPET and the relationship between CPET and quality of life measurements in a well-defined population of mild and moderate IPF patients.

Methods: A prospective, single-centre observational study. Read More

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DeepNetBim: deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information.

BMC Bioinformatics 2021 May 5;22(1):231. Epub 2021 May 5.

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Background: Epitope prediction is a useful approach in cancer immunology and immunotherapy. Many computational methods, including machine learning and network analysis, have been developed quickly for such purposes. However, regarding clinical applications, the existing tools are insufficient because few of the predicted binding molecules are immunogenic. Read More

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From systematic lymphadenectomy to sentinel lymph node mapping: a review on transitions and current practices in endometrial cancer staging.

Chin Clin Oncol 2021 Apr;10(2):22

Department of Obstetrics and Gynecology, University Hospital of Bern and University of Bern, Bern, Switzerland.

Endometrial cancer care has undergone major changes in the past 30 years. In 1988, staging transitioned from clinical to surgical. Moreover, the surgical approach of choice is no longer open surgery, but minimally invasive surgery. Read More

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Predicting youth at high risk of aging out of foster care using machine learning methods.

Child Abuse Negl 2021 May 2;117:105059. Epub 2021 May 2.

Children's Data Network, Suzanne Dworak-Peck School of Social Work, University of Southern California, United States; School of Social Work, University of North Carolina at Chapel Hill, United States.

Background: Youth who exit the nation's foster care system without permanency are at high risk of experiencing difficulties during the transition to adulthood.

Objective: To present an illustrative test of whether an algorithmic decision aid could be used to identify youth at risk of existing foster care without permanency.

Methods: For youth placed in foster care between ages 12 and 14, we assessed the risk of exiting care without permanency by age 18 based on their child welfare service involvement history. Read More

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Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking.

PLoS One 2021 5;16(5):e0250832. Epub 2021 May 5.

Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, Australia.

Objectives: Using a nationally-representative, cross-sectional cohort, we examined nutritional markers of undiagnosed type 2 diabetes in adults via machine learning.

Methods: A total of 16429 men and non-pregnant women ≥ 20 years of age were analysed from five consecutive cycles of the National Health and Nutrition Examination Survey. Cohorts from years 2013-2016 (n = 6673) was used for external validation. Read More

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A self-attention model for inferring cooperativity between regulatory features.

Nucleic Acids Res 2021 May 5. Epub 2021 May 5.

Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA.

Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. Read More

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Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation.

JMIR Med Inform 2021 May 5;9(5):e21347. Epub 2021 May 5.

Oak Ridge National Laboratory Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN, United States.

Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU.

Objective: We here report the development and validation of ICU length of stay and mortality prediction models. Read More

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Deep-learning models for lipid nanoparticle-based drug delivery.

Nanomedicine (Lond) 2021 May 5. Epub 2021 May 5.

Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.

Early prediction of time-lapse microscopy experiments enables intelligent data management and decision-making. Using time-lapse data of HepG2 cells exposed to lipid nanoparticles loaded with mRNA for expression of GFP, the authors hypothesized that it is possible to predict in advance whether a cell will express GFP. The first modeling approach used a convolutional neural network extracting per-cell features at early time points. Read More

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Impact of environmental factors in predicting daily severity scores of atopic dermatitis.

Clin Transl Allergy 2021 Apr;11(2):e12019

Department of Bioengineering, Imperial College London, London, UK.

Background: Atopic dermatitis (AD) is a chronic inflammatory skin disease that affects 20% of children worldwide. Environmental factors including weather and air pollutants have been shown to be associated with AD symptoms. However, the time-dependent nature of such a relationship has not been adequately investigated. Read More

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Outcome unpredictability affects outcome-specific motivation to learn.

Psychon Bull Rev 2021 May 4. Epub 2021 May 4.

Department of Psychology, Philipps-University Marburg, Gutenbergstrasse 18, 35052, Marburg, Germany.

Outcome predictability effects in associative learning paradigms describe better learning about outcomes with a history of greater predictability in a similar but unrelated task compared with outcomes with a history of unpredictability. Inspired by the similarities between this phenomenon and the effect of uncontrollability in learned helplessness paradigms, here, we investigate whether learning about unpredictability decreases outcome-specific motivation to learn. We used a modified version of the allergy task, in which participants first observe the foods eaten by a fictitious patient, followed by allergic reactions that he subsequently suffers, some of which are perfectly predictable and others unpredictable. Read More

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"P": an adaptive modeling tool for post-COVID-19 restart of surgical services.

JAMIA Open 2021 Apr 28;4(2):ooab016. Epub 2021 Apr 28.

Core of Predictive Analytics, Johns Hopkins All Children's Hospital, St. Petersburg, Florida, USA.

Objective: To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic.

Materials And Methods: Using data from 27 866 cases (May 1 2018-May 1 2020) stored in the Johns Hopkins All Children's data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs.

Results: The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios. Read More

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Clinically Applicable Machine Learning Approaches to Identify Attributes of Chronic Kidney Disease (CKD) for Use in Low-Cost Diagnostic Screening.

IEEE J Transl Eng Health Med 2021 15;9:4900511. Epub 2021 Apr 15.

WHO Collaborating Centre of eHealth, School of Public Health and Community MedicineUniversity of New South WalesSydneyNSW2052Australia.

Objective: Chronic kidney disease (CKD) is a major public health concern worldwide. High costs of late-stage diagnosis and insufficient testing facilities can contribute to high morbidity and mortality rates in CKD patients, particularly in less developed countries. Thus, early diagnosis aided by vital parameter analytics using affordable computer-aided diagnosis could not only reduce diagnosis costs but improve patient management and outcomes. Read More

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Pretreatment CT and PET radiomics predicting rectal cancer patients in response to neoadjuvant chemoradiotherapy.

Rep Pract Oncol Radiother 2021 25;26(1):29-34. Epub 2021 Feb 25.

Department of Radiation Oncology, Moffitt Cancer Center, Tampa, Florida, United States.

Background: The purpose of this study was to characterize pre-treatment non-contrast computed tomography (CT) and F-fluorodeoxyglucose positron emission tomography (PET) based radiomics signatures predictive of pathological response and clinical outcomes in rectal cancer patients treated with neoadjuvant chemoradiotherapy (NACR T).

Materials And Methods: An exploratory analysis was performed using pre-treatment non-contrast CT and PET imaging dataset. The association of tumor regression grade (TRG) and neoadjuvant rectal (NAR) score with pre-treatment CT and PET features was assessed using machine learning algorithms. Read More

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February 2021

Weakly supervised temporal model for prediction of breast cancer distant recurrence.

Sci Rep 2021 May 4;11(1):9461. Epub 2021 May 4.

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia.

Efficient prediction of cancer recurrence in advance may help to recruit high risk breast cancer patients for clinical trial on-time and can guide a proper treatment plan. Several machine learning approaches have been developed for recurrence prediction in previous studies, but most of them use only structured electronic health records and only a small training dataset, with limited success in clinical application. While free-text clinic notes may offer the greatest nuance and detail about a patient's clinical status, they are largely excluded in previous predictive models due to the increase in processing complexity and need for a complex modeling framework. Read More

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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

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Machine learning based models for prediction of subtype diagnosis of primary aldosteronism using blood test.

Sci Rep 2021 May 4;11(1):9140. Epub 2021 May 4.

Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi Higashi-ku, Fukuoka, 812-8582, Japan.

Primary aldosteronism (PA) is associated with an increased risk of cardiometabolic diseases, especially in unilateral subtype. Despite its high prevalence, the case detection rate of PA is limited, partly because of no clinical models available in general practice to identify patients highly suspicious of unilateral subtype of PA, who should be referred to specialized centers. The aim of this retrospective cross-sectional study was to develop a predictive model for subtype diagnosis of PA based on machine learning methods using clinical data available in general practice. Read More

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Deep learning for fully-automated prediction of overall survival in patients with oropharyngeal cancer using FDG PET imaging: an international retrospective study.

Clin Cancer Res 2021 May 4. Epub 2021 May 4.

Nuclear Medicine and Molecular Imaging Center, Chang Gung Memorial Hospital and Chang Gung University.

Purpose: Accurate prognostic stratification of patients with oropharyngeal squamous cell carcinoma (OPSCC) is crucial. We developed an objective and robust deep learning-based fully-automated tool called the DeepPET-OPSCC biomarker for predicting overall survival (OS) in OPSCC using [F]fluorodeoxyglucose PET imaging.

Experimental Design: The DeepPET-OPSCC prediction model was built and tested internally on a discovery cohort (n = 268) by integrating five convolutional neural network models for volumetric segmentation and ten models for OS prognostication. Read More

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Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning.

BMC Endocr Disord 2021 May 4;21(1):94. Epub 2021 May 4.

School of Life Sciences, Chinese University of Hong Kong, Hong Kong, China.

Introduction: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes.

Methods: This retrospective cohort study consists of type 1 and type 2 diabetic patients who were prescribed insulin at outpatient clinics of Hong Kong public hospitals, from 1st January to 31st December 2009. Read More

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A filter approach for feature selection in classification: application to automatic atrial fibrillation detection in electrocardiogram recordings.

BMC Med Inform Decis Mak 2021 May 4;21(Suppl 4):130. Epub 2021 May 4.

APHM, INSERM, IRD, Sciences Economiques & Sociales de la Sante & Traitement de l'Information Médicale (SESSTIM), Hop Timone, Biostatistique et Technologies de l'Information et de la Communication (BioSTIC), Aix Marseille Univ, Marseille, France.

Background: In high-dimensional data analysis, the complexity of predictive models can be reduced by selecting the most relevant features, which is crucial to reduce data noise and increase model accuracy and interpretability. Thus, in the field of clinical decision making, only the most relevant features from a set of medical descriptors should be considered when determining whether a patient is healthy or not. This statistical approach known as feature selection can be performed through regression or classification, in a supervised or unsupervised manner. Read More

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Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.

Int J Environ Res Public Health 2021 04 29;18(9). Epub 2021 Apr 29.

Duke-NUS Medical School, National University of Singapore, Singapore 169857, Singapore.

: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. : We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. Read More

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Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery.

J Pers Med 2021 Apr 29;11(5). Epub 2021 Apr 29.

Department of Orthodontics, Korea University Guro Hospital, Seoul 08308, Korea.

The aim of this study was to investigate the relationship between image patterns in cephalometric radiographs and the diagnosis of orthognathic surgery and propose a method to improve the accuracy of predictive models according to the depth of the neural networks. The study included 640 and 320 patients requiring non-surgical and surgical orthodontic treatments, respectively. The data of 150 patients were exclusively classified as a test set. Read More

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Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms.

Animals (Basel) 2021 Apr 30;11(5). Epub 2021 Apr 30.

AGROBIOCHEM Department, Research and Teaching Centre (TERRA), Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium.

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. Read More

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