AIMC Topic: Aged, 80 and over

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A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.

Scientific reports Oct 15, 2021
High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value...
Aged, 80 and over Male Middle Aged Emergency Medical Services Stroke Humans Machine Learning Prospective Studies Female Aged
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A new prognostic score for predicting survival in patients treated with robotic stereotactic radiotherapy for brain metastases.

Scientific reports Oct 13, 2021
The study aimed to analyze potential prognostic factors in patients treated with robotic radiosurgery for brain metastases irrespective of primary tumor location and create a simple prognostic score that can be used without a full diagnostic workup. ...
Humans Retrospective Studies Female Aged Aged, 80 and over Follow-Up Studies Robotics Disease-Free Survival Neoplasm Metastasis Radiosurgery Adult Survival Rate Brain Neoplasms Male Middle Aged
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Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database.

Computational and mathematical methods in medicine Oct 13, 2021
In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the prediction of clinical outcomes. With advances in the processing of natural language, information begi...
Sepsis Hospital Mortality Aged Computational Biology Proportional Hazards Models Humans Natural Language Processing Aged, 80 and over Databases, Factual Multivariate Analysis Critical Care Adult Prognosis Nursing Records Male Intensive Care Units Sentiment Analysis Middle Aged Female
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Deep learning can predict lymph node status directly from histology in colorectal cancer.

European journal of cancer (Oxford, England : 1990) Oct 11, 2021
BACKGROUND: Lymph node status is a prognostic marker and strongly influences therapeutic decisions in colorectal cancer (CRC).
Rectum ROC Curve Image Processing, Computer-Assisted Lymph Nodes Lymphatic Metastasis Neoplasm Staging Prognosis Female Aged Aged, 80 and over Colorectal Neoplasms Male Middle Aged Colon Case-Control Studies Deep Learning Cohort Studies Humans
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Association of TLR 9 gene polymorphisms with remission in patients with rheumatoid arthritis receiving TNF-α inhibitors and development of machine learning models.

Scientific reports Oct 11, 2021
Toll-like receptor (TLR)-4 and TLR9 are known to play important roles in the immune system, and several studies have shown their association with the development of rheumatoid arthritis (RA) and regulation of tumor necrosis factor alpha (TNF-α). Howe...
Humans Genetic Predisposition to Disease Polymorphism, Single Nucleotide Genotype Follow-Up Studies Aged, 80 and over Antirheumatic Agents Young Adult Toll-Like Receptor 9 Adult Male Middle Aged Arthritis, Rheumatoid Machine Learning Tumor Necrosis Factor-alpha Prognosis Female Aged
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Application of Medical Imaging Based on Deep Learning in the Treatment of Lumbar Degenerative Diseases and Osteoporosis with Bone Cement Screws.

Computational and mathematical methods in medicine Oct 11, 2021
OBJECTIVE: To explore the application value of magnetic resonance spectroscopy (MRS) and GSI-energy spectrum electronic computed tomography (CT) medical imaging based on the deep convolutional neural network (CNN) in the treatment of lumbar degenerat...
Aged, 80 and over Adult Male Middle Aged Deep Learning Computational Biology Humans Bone Density Neural Networks, Computer Osteoporosis Tomography, X-Ray Computed Absorptiometry, Photon Female Bone Cements Aged Adipose Tissue Magnetic Resonance Spectroscopy Bone Screws Lumbar Vertebrae Bone Diseases Image Interpretation, Computer-Assisted
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A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

JAMA network open Oct 1, 2021
IMPORTANCE: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic.
Young Adult Adult Male Middle Aged Infant Severity of Illness Index Area Under Curve Female Adolescent Benchmarking Infant, Newborn Child, Preschool Humans COVID-19 Testing Hospitalization Child Machine Learning Crowdsourcing Aged ROC Curve COVID-19 Washington Aged, 80 and over Models, Statistical Algorithms Clinical Decision Rules Prognosis
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Artificial intelligence-based detection of epimacular membrane from color fundus photographs.

Scientific reports Sep 29, 2021
Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Al...
Humans Retrospective Studies Female Aged Aged, 80 and over Male Middle Aged Deep Learning Image Processing, Computer-Assisted Tomography, Optical Coherence Fundus Oculi Datasets as Topic Ophthalmoscopy Photography Epiretinal Membrane
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A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images.

NeuroImage Sep 25, 2021
A tool was developed to automatically segment several subcortical limbic structures (nucleus accumbens, basal forebrain, septal nuclei, hypothalamus without mammillary bodies, the mammillary bodies, and fornix) using only a T1-weighted MRI as input. ...
Humans Adult Magnetic Resonance Imaging Female Male Nucleus Accumbens Reproducibility of Results Middle Aged Limbic System Aged Deep Learning Septal Nuclei Aged, 80 and over Adolescent Basal Forebrain Young Adult Fornix, Brain
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Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering.

Medical sciences (Basel, Switzerland) Sep 24, 2021
BACKGROUND: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters.
Bicarbonates Male Middle Aged Hospital Mortality Consensus Humans Cluster Analysis Machine Learning Female Chlorides Acute Kidney Injury Hospitalization Aged Aged, 80 and over Adult
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