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

Scientific reports
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...

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

Scientific reports
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...

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

Scientific reports
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 ...

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
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.

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

Journal of medical Internet research
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.

Effect of cross-cultural differences on thickness, firmness and sweetness sensitivity.

Food research international (Ottawa, Ont.)
Sensitivity of the somatosensory system may be influenced by multiple physiological parameters. Variations in oral physiology can arise from cross-cultural differences which may potentially affect sensory sensitivity. The aim of this case study was t...

Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome.

Endocrine
PURPOSE: Automated facial recognition technology based on deep learning has achieved high accuracy in diagnosing various endocrine diseases and genetic syndromes. This study attempts to establish a facial diagnostic system for Turner syndrome (TS) ba...

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
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...

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

British journal of haematology
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...