503 results match your criteria area precision-recall


A Machine Learning-Based Screening Test for Sarcopenic Dysphagia Using Image Recognition.

Nutrients 2021 Nov 10;13(11). Epub 2021 Nov 10.

Graduate School of Public Health, St. Luke's International University, Tokyo 104-0044, Japan.

Background: Sarcopenic dysphagia, a swallowing disorder caused by sarcopenia, is prevalent in older patients and can cause malnutrition and aspiration pneumonia. This study aimed to develop a simple screening test using image recognition with a low risk of droplet transmission for sarcopenic dysphagia.

Methods: Older patients admitted to a post-acute care hospital were enrolled in this cross-sectional study. Read More

View Article and Full-Text PDF
November 2021

A Comparison of Models Predicting One-Year Mortality at Time of Admission.

J Pain Symptom Manage 2021 Nov 23. Epub 2021 Nov 23.

Department of Family and Community Medicine, University of Missouri, Columbia, MO.

Context: Hospitalization provides an opportunity to address end-of-life care (EoLC) preferences if patients at risk of death can be accurately identified while in the hospital. The modified Hospital One-Year Mortality Risk (mHOMR) uses demographic and admission data in a logistic regression algorithm to identify patients at risk of death one year from admission.

Objective: This project sought to validate mHOMR and identify superior models. Read More

View Article and Full-Text PDF
November 2021

Comparison of early warning scores for sepsis early identification and prediction in the general ward setting.

JAMIA Open 2021 Jul 2;4(3):ooab062. Epub 2021 Aug 2.

Institute for Informatics, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.

The objective of this study was to directly compare the ability of commonly used early warning scores (EWS) for early identification and prediction of sepsis in the general ward setting. For general ward patients at a large, academic medical center between early-2012 and mid-2018, common EWS and patient acuity scoring systems were calculated from electronic health records (EHR) data for patients that both met and did not meet Sepsis-3 criteria. For identification of sepsis at index time, National Early Warning Score 2 (NEWS 2) had the highest performance (area under the receiver operating characteristic curve: 0. Read More

View Article and Full-Text PDF

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma.

BMC Cancer 2021 Nov 24;21(1):1268. Epub 2021 Nov 24.

Department of Radiology, The Affiliated Hospital of Southwest Medical University, and Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, 646000, Sichuan, China.

Background: Radiomics may provide more objective and accurate predictions for extrahepatic cholangiocarcinoma (ECC). In this study, we developed radiomics models based on magnetic resonance imaging (MRI) and machine learning to preoperatively predict differentiation degree (DD) and lymph node metastasis (LNM) of ECC.

Methods: A group of 100 patients diagnosed with ECC was included. Read More

View Article and Full-Text PDF
November 2021

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

Pediatrics 2021 Dec;148(6)

Departments of Ophthalmology.

Background And Objectives: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. Read More

View Article and Full-Text PDF
December 2021

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling.

BMC Bioinformatics 2021 Nov 19;22(1):557. Epub 2021 Nov 19.

Department of Systems Biology and Bioinformatics, University of Rostock, 18057, Rostock, Germany.

Background: The research landscape of single-cell and single-nuclei RNA-sequencing is evolving rapidly. In particular, the area for the detection of rare cells was highly facilitated by this technology. However, an automated, unbiased, and accurate annotation of rare subpopulations is challenging. Read More

View Article and Full-Text PDF
November 2021

Development and validation of a prognostic tool: Pulmonary embolism short-term clinical outcomes risk estimation (PE-SCORE).

PLoS One 2021 18;16(11):e0260036. Epub 2021 Nov 18.

Professor Emeritus of Biostatistics, Atrium Health's Carolinas Medical Center, Charlotte, NC, United States of America.

Objective: Develop and validate a prognostic model for clinical deterioration or death within days of pulmonary embolism (PE) diagnosis using point-of-care criteria.

Methods: We used prospective registry data from six emergency departments. The primary composite outcome was death or deterioration (respiratory failure, cardiac arrest, new dysrhythmia, sustained hypotension, and rescue reperfusion intervention) within 5 days. Read More

View Article and Full-Text PDF
November 2021

Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records.

JAMA Netw Open 2021 Nov 1;4(11):e2135174. Epub 2021 Nov 1.

Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.

Importance: Detecting cognitive decline earlier among older adults can facilitate enrollment in clinical trials and early interventions. Clinical notes in longitudinal electronic health records (EHRs) provide opportunities to detect cognitive decline earlier than it is noted in structured EHR fields as formal diagnoses.

Objective: To develop and validate a deep learning model to detect evidence of cognitive decline from clinical notes in the EHR. Read More

View Article and Full-Text PDF
November 2021

Prediction of Gastric Cancer-Related Proteins Based on Graph Fusion Method.

Front Cell Dev Biol 2021 1;9:739715. Epub 2021 Nov 1.

Endoscopy Center, China-Japan Union Hospital of Jilin University, Changchun, China.

Gastric cancer is a common malignant tumor of the digestive system with no specific symptoms. Due to the limited knowledge of pathogenesis, patients are usually diagnosed in advanced stage and do not have effective treatment methods. Proteome has unique tissue and time specificity and can reflect the influence of external factors that has become a potential biomarker for early diagnosis. Read More

View Article and Full-Text PDF
November 2021

Promotech: a general tool for bacterial promoter recognition.

Genome Biol 2021 Nov 17;22(1):318. Epub 2021 Nov 17.

Department of Computer Science, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, Newfoundland, A1C 5S7, Canada.

Promoters are genomic regions where the transcription machinery binds to initiate the transcription of specific genes. Computational tools for identifying bacterial promoters have been around for decades. However, most of these tools were designed to recognize promoters in one or few bacterial species. Read More

View Article and Full-Text PDF
November 2021

Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.

BMC Med Genomics 2021 11 17;14(Suppl 3):225. Epub 2021 Nov 17.

Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam.

Background: Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations.

Methods: In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. Read More

View Article and Full-Text PDF
November 2021

Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study.

Lab Invest 2021 Nov 15. Epub 2021 Nov 15.

Glioma Multidisciplinary Research Group, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.

Determination of 1p/19q co-deletion status is important for the classification, prognostication, and personalized therapy in diffuse lower-grade gliomas (LGG). We developed and validated a deep learning imaging signature (DLIS) from preoperative magnetic resonance imaging (MRI) for predicting the 1p/19q status in patients with LGG. The DLIS was constructed on a training dataset (n = 330) and validated on both an internal validation dataset (n = 123) and a public TCIA dataset (n = 102). Read More

View Article and Full-Text PDF
November 2021

A deep learning model for burn depth classification using ultrasound imaging.

J Mech Behav Biomed Mater 2021 Oct 29;125:104930. Epub 2021 Oct 29.

Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.

Identification of burn depth with sufficient accuracy is a challenging problem. This paper presents a deep convolutional neural network to classify burn depth based on altered tissue morphology of burned skin manifested as texture patterns in the ultrasound images. The network first learns a low-dimensional manifold of the unburned skin images using an encoder-decoder architecture that reconstructs it from ultrasound images of burned skin. Read More

View Article and Full-Text PDF
October 2021

Machine learning based texture analysis of patella from X-rays for detecting patellofemoral osteoarthritis.

Int J Med Inform 2022 Jan 30;157:104627. Epub 2021 Oct 30.

Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland; Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland. Electronic address:

Objective: To assess the ability of texture features for detecting radiographic patellofemoral osteoarthritis (PFOA) from knee lateral view radiographs.

Design: We used lateral view knee radiographs from The Multicenter Osteoarthritis Study (MOST) public use datasets (n  = 5507 knees). Patellar region-of-interest (ROI) was automatically detected using landmark detection tool (BoneFinder), and subsequently, these anatomical landmarks were used to extract three different texture ROIs. Read More

View Article and Full-Text PDF
January 2022

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy.

Cancers (Basel) 2021 Nov 3;13(21). Epub 2021 Nov 3.

Department of Dermatology and Allergy, University Hospital, LMU Munich, 80337 Munich, Germany.

Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. Read More

View Article and Full-Text PDF
November 2021

Factors Associated with Thyroid-Related Adverse Events in Patients Receiving PD-1 or PD-L1 Inhibitors Using Machine Learning Models.

Cancers (Basel) 2021 Oct 30;13(21). Epub 2021 Oct 30.

College of Pharmacy, Chungbuk National University, Cheongju 28160, Korea.

Targets of immune checkpoint inhibitors (ICIs) regulate immune homeostasis and prevent autoimmunity by downregulating immune responses and by inhibiting T cell activation. Although ICIs are widely used in immunotherapy because of their good clinical efficacy, they can also induce autoimmune-related adverse events. Thyroid-related adverse events are frequently associated with anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. Read More

View Article and Full-Text PDF
October 2021

A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites.

Front Genet 2021 26;12:752732. Epub 2021 Oct 26.

School of Electrical Engineering, Shaoyang University, Shaoyang, China.

Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. Read More

View Article and Full-Text PDF
October 2021

A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury.

J Neurosurg Anesthesiol 2021 Nov 11. Epub 2021 Nov 11.

Anesthesiology and Pain Medicine, University of Washington The Mountain-Whisper-Light: Statistics & Data Science, Seattle, WA.

Background: Traumatic brain injury (TBI) is a major cause of death and disability. Episodes of hypotension are associated with worse TBI outcomes. Our aim was to model the real-time risk of intraoperative hypotension in TBI patients, compare machine learning and traditional modeling techniques, and identify key contributory features from the patient monitor and medical record for the prediction of intraoperative hypotension. Read More

View Article and Full-Text PDF
November 2021

Identification of successful cerebral reperfusions (mTICI ≥2b) using an artificial intelligence strategy.

Neuroradiology 2021 Nov 9. Epub 2021 Nov 9.

Department of Neurology, Texas Tech University Medical Sciences Center, Room 3A105, 3601 4th street, Lubbock, TX, 79430, USA.

Background: The modified thrombolysis in cerebral infarction (mTICI) scale is a widely used and validated qualitative tool to evaluate angiographic intracerebral inflow following endovascular thrombectomy (EVT). We validated a machine-learning (ML) algorithm to grade digital subtraction angiograms (DSA) using the mTICI scale.

Materials And Methods: We included angiograms of identified middle cerebral artery (MCA) occlusions who underwent EVT. Read More

View Article and Full-Text PDF
November 2021

Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study.

JMIR Med Inform 2021 Nov 4;9(11):e26426. Epub 2021 Nov 4.

Division of Pulmonology, Department of Internal Medicine, Yonsei University Health System, Seoul, Republic of Korea.

Background: In the era of artificial intelligence, event prediction models are abundant. However, considering the limitation of the electronic medical record-based model, including the temporally skewed prediction and the record itself, these models could be delayed or could yield errors.

Objective: In this study, we aim to develop multiple event prediction models in intensive care units to overcome their temporal skewness and evaluate their robustness against delayed and erroneous input. Read More

View Article and Full-Text PDF
November 2021

External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.

Addiction 2021 Nov 2. Epub 2021 Nov 2.

Rush Medical College, Rush University, Chicago, IL, USA.

Background And Aims: Unhealthy alcohol use (UAU) is one of the leading causes of global morbidity. A machine learning approach to alcohol screening could accelerate best practices when integrated into electronic health record (EHR) systems. This study aimed to validate externally a natural language processing (NLP) classifier developed at an independent medical center. Read More

View Article and Full-Text PDF
November 2021

Predicting miRNA-disease associations using improved random walk with restart and integrating multiple similarities.

Sci Rep 2021 Oct 26;11(1):21071. Epub 2021 Oct 26.

Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam.

Predicting beneficial and valuable miRNA-disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA-disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). Read More

View Article and Full-Text PDF
October 2021

Prediction of pandemic risk for animal-origin coronavirus using a deep learning method.

Infect Dis Poverty 2021 Oct 24;10(1):128. Epub 2021 Oct 24.

Institute of Computing Science and Technology, Guangzhou University, Guangzhou, 510006, China.

Background: Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. Read More

View Article and Full-Text PDF
October 2021

The colors of our brain: an integrated approach for dimensionality reduction and explainability in fMRI through color coding (i-ECO).

Brain Imaging Behav 2021 Oct 24. Epub 2021 Oct 24.

Psychiatry Unit, Department of Health Sciences, University of Florence, viale della Maternità, Padiglione 8b, AOU Careggi, Firenze, Florence, FI, 50134, Italy.

Several systematic reviews have highlighted the role of multiple sources in the investigation of psychiatric illness. For what concerns fMRI, the focus of recent literature preferentially lies on three lines of research, namely: functional connectivity, network analysis and spectral analysis. Data was gathered from the UCLA Consortium for Neuropsychiatric Phenomics. Read More

View Article and Full-Text PDF
October 2021

Towards threshold invariance in defining hippocampal ripples.

J Neural Eng 2021 Nov 15;18(6). Epub 2021 Nov 15.

Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan.

Hippocampal ripples are transient neuronal features observed in high-frequency oscillatory bands of local field potentials (LFPs), and they occur primarily during periods of behavioral immobility and slow-wave sleep. Ripples have been defined based on mathematically engineered features, such as magnitudes, durations, and cycles per event. However, the 'ripple' could vary from laboratory to laboratory because their definition is subject to human bias, including the arbitrary choice of parameters and thresholds. Read More

View Article and Full-Text PDF
November 2021

iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding.

J Chem Inf Model 2021 Oct 21. Epub 2021 Oct 21.

Computational Biology Center, International University-VNU HCMC, Ho Chi Minh City 700000, Vietnam.

The human cytochrome P450 (CYP) superfamily holds responsibilities for the metabolism of both endogenous and exogenous compounds such as drugs, cellular metabolites, and toxins. The inhibition exerted on the CYP enzymes is closely associated with adverse drug reactions encompassing metabolic failures and induced side effects. In modern drug discovery, identification of potential CYP inhibitors is, therefore, highly essential. Read More

View Article and Full-Text PDF
October 2021

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

Brief Bioinform 2021 Oct 20. Epub 2021 Oct 20.

School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Read More

View Article and Full-Text PDF
October 2021

Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.

BMC Med Inform Decis Mak 2021 10 20;21(1):288. Epub 2021 Oct 20.

Institute of Data Science, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan (R.O.C.).

Background: Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions. Read More

View Article and Full-Text PDF
October 2021

Ensemble learning to predict opioid-related overdose using statewide prescription drug monitoring program and hospital discharge data in the state of Tennessee.

J Am Med Inform Assoc 2021 Oct 19. Epub 2021 Oct 19.

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Objective: To develop and validate algorithms for predicting 30-day fatal and nonfatal opioid-related overdose using statewide data sources including prescription drug monitoring program data, Hospital Discharge Data System data, and Tennessee (TN) vital records. Current overdose prevention efforts in TN rely on descriptive and retrospective analyses without prognostication.

Materials And Methods: Study data included 3 041 668 TN patients with 71 479 191 controlled substance prescriptions from 2012 to 2017. Read More

View Article and Full-Text PDF
October 2021

Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.

PLoS One 2021 14;16(10):e0258339. Epub 2021 Oct 14.

Microsoft Research Cambridge, Cambridge, United Kingdom.

Background: Despite increased testing efforts and the deployment of vaccines, COVID-19 cases and death toll continue to rise at record rates. Health systems routinely collect clinical and non-clinical information in electronic health records (EHR), yet little is known about how the minimal or intermediate spectra of EHR data can be leveraged to characterize patient SARS-CoV-2 pretest probability in support of interventional strategies.

Methods And Findings: We modeled patient pretest probability for SARS-CoV-2 test positivity and determined which features were contributing to the prediction and relative to patients triaged in inpatient, outpatient, and telehealth/drive-up visit-types. Read More

View Article and Full-Text PDF
October 2021