Copertina

Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zahoor, Sheresh, Constantinou, Anthony C., Curtis, Tim M, Hasanuzzaman, Mohammed
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Data
di: Zahoor, Sheresh, et al.
Pubblicazione: (2025)

Investigating potential causes of Sepsis with Bayesian network structure learning
di: Petrungaro, Bruno, et al.
Pubblicazione: (2024)

Decoding the mechanisms of the Hattrick football manager game using Bayesian network structure learning
di: Constantinou, Anthony C., et al.
Pubblicazione: (2025)

Stable Structure Learning with HC-Stable and Tabu-Stable Algorithms
di: Kitson, Neville K., et al.
Pubblicazione: (2025)

The Impact of Variable Ordering on Bayesian Network Structure Learning
di: Kitson, Neville K, et al.
Pubblicazione: (2022)

Time series causal discovery with variable lags
di: Petrungaro, Bruno, et al.
Pubblicazione: (2026)

Seeing Through VisualBERT: A Causal Adventure on Memetic Landscapes
di: Bandyopadhyay, Dibyanayan, et al.
Pubblicazione: (2024)

Using GPT-4 to guide causal machine learning
di: Constantinou, Anthony C., et al.
Pubblicazione: (2024)

Statistical and structural identifiability in representation learning
di: Nelson, Walter, et al.
Pubblicazione: (2026)

Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
di: Petrungaro, Bruno, et al.
Pubblicazione: (2026)

Statistical curriculum learning: An elimination algorithm achieving an oracle risk
di: Cohen, Omer, et al.
Pubblicazione: (2024)

On the importance of structural identifiability for machine learning with partially observed dynamical systems
di: Norden, Janis, et al.
Pubblicazione: (2025)

From algorithms to action: improving patient care requires causality
di: van Amsterdam, Wouter A. C., et al.
Pubblicazione: (2022)

Training speedups via batching for geometric learning: an analysis of static and dynamic algorithms
di: Speckhard, Daniel T., et al.
Pubblicazione: (2025)

Practical considerations when designing an online learning algorithm for an app-based mHealth intervention
di: Gonzalez, Rachel T, et al.
Pubblicazione: (2025)

Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks
di: Anthes, Daniel, et al.
Pubblicazione: (2023)

Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records
di: Wang, Bing, et al.
Pubblicazione: (2023)

Active, anytime-valid risk controlling prediction sets
di: Xu, Ziyu, et al.
Pubblicazione: (2024)

Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus
di: Vershinina, Olga, et al.
Pubblicazione: (2025)

Achievable distributional robustness when the robust risk is only partially identified
di: Kostin, Julia, et al.
Pubblicazione: (2025)

How to sketch a learning algorithm
di: Gunn, Sam
Pubblicazione: (2026)

Everything that can be learned about a causal structure with latent variables by observational and interventional probing schemes
di: Ansanelli, Marina Maciel, et al.
Pubblicazione: (2024)

A Goemans-Williamson type algorithm for identifying subcohorts in clinical trials
di: Worah, Pratik
Pubblicazione: (2025)

Machine learning algorithms to predict the risk of rupture of intracranial aneurysms: a systematic review
di: Daga, Karan, et al.
Pubblicazione: (2024)

A novel method for identifying rice seed purity based on hybrid machine learning algorithms
di: Thi-Thu-Hong, Phan, et al.
Pubblicazione: (2024)

Evaluating the Usability of LLMs in Threat Intelligence Enrichment
di: Srikanth, Sanchana, et al.
Pubblicazione: (2024)

Investigating Data Usage for Inductive Conformal Predictors
di: Fang, Yizirui, et al.
Pubblicazione: (2024)

Can a Quantum Support Vector Machine algorithm be utilized to identify Key Biomarkers from Multi-Omics data of COVID19 patients?
di: Choi, Junggu, et al.
Pubblicazione: (2025)

Strong identifiability and parameter learning in regression with heterogeneous response
di: Do, Dat, et al.
Pubblicazione: (2022)

Discrimination in machine learning algorithms
di: Pappadà, Roberta, et al.
Pubblicazione: (2022)

On the use of adversarial validation for quantifying dissimilarity in geospatial machine learning prediction
di: Wang, Yanwen, et al.
Pubblicazione: (2024)

Explainable quantum regression algorithm with encoded data structure
di: Wang, C. -C. Joseph, et al.
Pubblicazione: (2023)

Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures
di: Korolev, Denis, et al.
Pubblicazione: (2025)

Patient foundation model for risk stratification in low-risk overweight patients
di: Flamholz, Zachary N., et al.
Pubblicazione: (2026)

Transformers learn factored representations
di: Shai, Adam, et al.
Pubblicazione: (2026)

Fault Diagnosis on Induction Motor using Machine Learning and Signal Processing
di: Samiullah, Muhammad, et al.
Pubblicazione: (2024)

Clustering ensemble algorithm with high-order consistency learning
di: Gan, Jianwen, et al.
Pubblicazione: (2024)

Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors
di: González-Castro, Ana, et al.
Pubblicazione: (2025)

Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters
di: Saha, Saheli, et al.
Pubblicazione: (2024)

Statistical Agnostic Regression: a machine learning method to validate regression models
di: Gorriz, Juan M, et al.
Pubblicazione: (2024)