Results 141 to 150 of about 92,198 (268)

Deep Learning for Age Estimation and Sex Prediction Using Mandibular-Cropped Cephalometric Images: Comparative Model Development and Validation Study. [PDF]

JMIR AI
Handayani VW, Margaretha Amiatun Ruth MS, Rulaningtyas R, Kurniawan A, Yudhantorro BA, Yudianto A.   +5 more
europepmc   +1 more source

From bias to bliss: Racial preferences and worker productivity in tennis

Contemporary Economic Policy, EarlyView.
Abstract We investigate the impact of differences in consumers' racial preferences on worker productivity through the example of the home advantage (HA) effect using data on wins in men's tennis from 2001 to 2020 (pre‐COVID‐19). We identify players' racial affiliation as one of five distinct groups by combining clustering and facial recognition methods.
Carsten Creutzburg, Wolfgang Maennig, Steffen Q. Mueller   +2 more
wiley   +1 more source

Explainable machine learning-based 28-day mortality prediction model for elderly patients with acute kidney injury. [PDF]

BMC Nephrol
Jiao Y, Wu Z, Zhang Y, Liu Y, Zhi P, Ao Q, Cheng Q.   +6 more
europepmc   +1 more source

Machine Learning Model for Predicting Postoperative Pain in Cases of Irreversible Pulpitis

International Endodontic Journal, EarlyView.
ABSTRACT Aim Postoperative pain is a frequent clinical concern following endodontic treatment. This study aimed to develop and validate supervised machine learning models to predict the occurrence of postoperative pain in cases of irreversible pulpitis.
Pedro Felipe de Jesus Freitas, Ingrid Luiza Mendonça Cunha, Thalita de Paris Matos, Flares Baratto‐Filho, Bianca Marques de Mattos de Araujo, Manoel Damião Sousa‐Neto, Leandro de Moura Martins, Fernando José Herkrath, Emílio Carlos Sponchiado‐Júnior, Cristiano Miranda de Araujo   +9 more
wiley   +1 more source

Machine learning performance for a small dataset: random oversampling improves data imbalances and fairness. [PDF]

BMC Med Res Methodol
Wang L, Shi E, Meyers B, Vlachos P, Tcheng J, Denardo S.   +5 more
europepmc   +1 more source

Prevalence and Comorbidity of Dental Phobia in a Representative Population of 16‐Year‐Olds in Norway

International Journal of Paediatric Dentistry, EarlyView.
ABSTRACT Background Although dental fear and anxiety are common in children and adolescents, they are not well‐defined constructs and may not capture critical clinical aspects. Dental phobia (DP) is more well‐defined as a specific phobia that includes the criteria of functional impairment. Aim To investigate the prevalence and covariates of DP.
Robert Schibbye, Lars Wichstrøm, Göran Dahllöf   +2 more
wiley   +1 more source

Global OMI HCHO Level-3 oversampling dataset: high spatial resolution and lightweight uncertainty. [PDF]

Sci Data
Xia H, Wang D, Yang X, Li X, Zhu L, Lu T, Song Z, Mo Y, Yan C, Pu D, Zuo X, Sun W, Wang J, Gu X.   +13 more
europepmc   +1 more source

Role of a Deep‐Learning Based Convolutional Neural Network Model for Real‐Time Ventricular Tachycardia Alarm Classification

Journal of Cardiovascular Electrophysiology, EarlyView.
Our manuscript discusses the role of a Convolutional Neural network based Deep‐learning model for the classification of ventricular tachycardia alarms. Our algorithm based on input from multiple raw waveform inputs, including electrocardiogram leads, photoplethysmogram, and arterial blood pressure signals achieved a good degree of accuracy and ...
Unmesh Khanolkar, Ashish Yadav, Avdhesh Mann, Scott Pappada, Abhishek Deshmukh, Abhishek Maan   +5 more
wiley   +1 more source

Comprehensive blood glucose level prediction from HbA_1c levels using machine learning models across the biological range. [PDF]

Sci Rep
Okyay TM, Yilmaz I, Koldas M.
europepmc   +1 more source

The nature and nurture of primary and secondary callous–unemotional traits: evidence from two independent twin samples

Journal of Child Psychology and Psychiatry, EarlyView.
Background Callous–unemotional (CU) traits identify youth with more severe and chronic trajectories of conduct problems. However, the etiology of CU traits may be heterogeneous, undermining the search for effective treatments. The level of co‐occurring anxiety has been used to identify “primary” (lower anxiety) versus “secondary” (higher anxiety ...
Rachel C. Tomlinson, Patrizia Pezzoli, Essi Viding, Stephane A. De Brito, Kelly L. Klump, S. Alexandra Burt, Luke W. Hyde   +6 more
wiley   +1 more source