Results 231 to 240 of about 22,118,555 (377)

Evaluating Large Language Models for Decision Support in Minimally Invasive Spine Surgery Triage and Procedural Categories. [PDF]

Global Spine J
Kartal A, Manalil NF, Cheng CD, Chung LK, Gebhard H, Greenberg M, Härtl R, Elsayed GA.   +7 more
europepmc   +1 more source

Cluster-robust jackknife and bootstrap inference for logistic regression models [PDF]

James G. MacKinnon, Morten Ørregaard Nielsen, Matthew D. Webb   +2 more
openalex   +1 more source

A Data‐Centric Approach to Quantifying the Forward and Inverse Relationship Between Laser Powder Bed Fusion Process Parameters and as‐Built Surface Roughness of IN718 Parts

Advanced Intelligent Systems, EarlyView.
This study introduces the first inverse machine learning model to predict laser powder bed fusion process parameters for targeted surface roughness of Inconel 718 parts. Unlike prior approaches, it incorporates spatial surface characteristics for improved accuracy.
Samsul Mahmood, Bart Raeymaekers
wiley   +1 more source

Coancestry superposed on admixed populations yields measures of relatedness at individual-level resolution. [PDF]

PLoS Comput Biol
Chen D, Storey JD.
europepmc   +1 more source

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

Advanced Intelligent Systems, EarlyView.
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam   +3 more
wiley   +1 more source

A clinical prediction model for schizophrenia based on machine learning algorithms. [PDF]

Front Med (Lausanne)
Jin W, Chen S, Gao Q, Li D, Lu W, Wang M, Chen Q, Lin P.   +7 more
europepmc   +1 more source

Phenotypic stability via ammi model with bootstrap re-sampling Estabilidade fenotípica através da reamostragem “bootstrap” no modelo AMMI

, 2010
Osmir José Lavoranti, Carlos Tadeu dos Santos Dias, Wojtek J. Kraznowski   +2 more
openalex   +2 more sources

Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models

Advanced Intelligent Systems, EarlyView.
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart   +3 more
wiley   +1 more source

Validating Physiologically-Based Pharmacokinetic Models Using the Continuous Ranked Probability Score: Beyond Being Correct on Average. [PDF]

CPT Pharmacometrics Syst Pharmacol
Sluijterman L, van Borselen M, Greupink R, IntHout J.   +3 more
europepmc   +1 more source

Bootstrap pointwise confidence intervals for covariate-adjusted survivor functions in the Cox model [PDF]

, 2019
Constantin Ruhe
openalex   +1 more source