AIMC Topic: Male

Clear Filters Showing 15721 to 15730 of 27433 articles

Machine learning approaches for sex estimation using cranial measurements.

International journal of legal medicine Nov 11, 2020
The aim of the present study is to apply support vector machines (SVM) and artificial neural network (ANN) as sex classifiers and to generate useful classification models for sex estimation based on cranial measurements. Besides, the performance of t...
Anatomic Landmarks Cephalometry Datasets as Topic Sex Determination by Skeleton Humans Bulgaria Skull Neural Networks, Computer Female Middle Aged Reproducibility of Results Adolescent Aged Logistic Models Aged, 80 and over Support Vector Machine Adult Male
View on PubMed DOI

Characterization of specific and distinct patient types in clinical trials of acute schizophrenia using an uncorrelated PANSS score matrix transform (UPSM).

Psychiatry research Nov 11, 2020
Understanding the specificity of symptom change in schizophrenia can facilitate the evaluation antipsychotic efficacy for different symptom domains. Previous work identified a transform of PANSS using an uncorrelated PANSS score matrix (UPSM) to redu...
Antipsychotic Agents Humans Male Machine Learning Psychiatric Status Rating Scales Schizophrenic Psychology Female Treatment Outcome Adult Double-Blind Method Schizophrenia
View on PubMed DOI

Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.

Scientific reports Nov 11, 2020
We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506...
Retinal Detachment Vitreoretinopathy, Proliferative Postoperative Complications Diagnosis, Computer-Assisted Vitrectomy Female Ophthalmologists Risk Factors Humans Aged Machine Learning Male Algorithms Middle Aged Retrospective Studies
View on PubMed DOI

Differentiation of low and high grade renal cell carcinoma on routine MRI with an externally validated automatic machine learning algorithm.

Scientific reports Nov 11, 2020
Pre-treatment determination of renal cell carcinoma aggressiveness may help guide clinical decision-making. We aimed to differentiate low-grade (Fuhrman I-II) from high-grade (Fuhrman III-IV) renal cell carcinoma using radiomics features extracted fr...
Female Aged Aged, 80 and over Adult Male Middle Aged ROC Curve Magnetic Resonance Imaging Diagnosis, Differential Bayes Theorem Humans Machine Learning Retrospective Studies Neoplasm Grading Kidney Neoplasms Carcinoma, Renal Cell
View on PubMed DOI

Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.

Scientific reports Nov 11, 2020
Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery associated with various physical disorders and cognitive dysfunction. Consequently, post-stroke symptoms ...
Neural Networks, Computer Middle Aged Machine Learning ROC Curve Female Humans Cognition Aged Movement Male Stroke Stroke Rehabilitation Mood Disorders
View on PubMed DOI

Use of Patient-Reported Symptoms from an Online Symptom Tracking Tool for Dementia Severity Staging: Development and Validation of a Machine Learning Approach.

Journal of medical Internet research Nov 11, 2020
BACKGROUND: SymptomGuide Dementia (DGI Clinical Inc) is a publicly available online symptom tracking tool to support caregivers of persons living with dementia. The value of such data are enhanced when the specific dementia stage is identified.
Dementia Patient Reported Outcome Measures Cyberbullying Female Aged Male Cognitive Dysfunction Humans Machine Learning Algorithms
View on PubMed DOI

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.

Journal of medical Internet research Nov 11, 2020
BACKGROUND: Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients' prognosis early and administer precise treatment are of great significance.
Humans Intensive Care Units Machine Learning Female COVID-19 Reproducibility of Results Algorithms Risk Factors Prognosis Male Retrospective Studies
View on PubMed DOI

A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies.

Clinical cancer research : an official journal of the American Association for Cancer Research Nov 10, 2020
PURPOSE: Gastric cancer remains the leading cause of cancer-related deaths in Northeast Asia. Population-based endoscopic screenings in the region have yielded successful results in early detection of gastric tumors. Endoscopic screening rates are co...
Observer Variation Gastroscopy Gastric Mucosa Biopsy Feasibility Studies Pathologists Image Interpretation, Computer-Assisted Retrospective Studies Female Aged Aged, 80 and over Adult Stomach Neoplasms Sensitivity and Specificity Humans Male Middle Aged Prospective Studies Deep Learning
View on PubMed DOI

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

Circulation research Nov 10, 2020
RATIONALE: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.
Cardiomyopathies Ventricular Fibrillation Death, Sudden, Cardiac Electrophysiologic Techniques, Cardiac Predictive Value of Tests Signal Processing, Computer-Assisted Myocardial Infarction Tachycardia, Ventricular Risk Factors Neural Networks, Computer Risk Assessment Aged Humans Phenotype Diagnosis, Computer-Assisted Aged, 80 and over Prospective Studies Time Factors Male Prognosis Support Vector Machine Middle Aged Female Action Potentials
View on PubMed DOI

Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease.

British journal of haematology Nov 10, 2020
Reducing preventable hospital re-admissions in Sickle Cell Disease (SCD) could potentially improve outcomes and decrease healthcare costs. In a retrospective study of electronic health records, we hypothesized Machine-Learning (ML) algorithms may out...
Humans Machine Learning Young Adult Adult Male Middle Aged Risk Assessment Algorithms Female Hospitalization Aged Aged, 80 and over Adolescent Anemia, Sickle Cell Patient Readmission
View on PubMed DOI
Popular Topics
Recent Journals