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Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jamanetworkopen.2023.17651openalex-snapshot

Reminding Peer Reviewers of Reporting Guideline Items to Improve Completeness in Published Articles

inferredcites →https://doi.org/10.1136/bmj.c869openalex-snapshot

CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials

inferredcites →https://doi.org/10.1186/1745-6215-11-32openalex-snapshot

CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials

inferredcites →https://doi.org/10.1186/s41073-018-0052-4openalex-snapshot

Reported use of reporting guidelines among JNCI: Journal of the National Cancer Institute authors, editorial outcomes, and reviewer ratings related to adherence to guidelines and clarity of presentation

inferredcites →https://doi.org/10.1371/journal.pone.0128061openalex-snapshot

CONSORT Item Reporting Quality in the Top Ten Ranked Journals of Critical Care Medicine in 2011: A Retrospective Analysis

inferredcites →https://doi.org/10.2196/23026openalex-snapshot

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study

inferredcites →https://doi.org/10.2196/38053openalex-snapshot

A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study

inferredcites →https://doi.org/10.2196/38557openalex-snapshot

Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

inferredcites →https://doi.org/10.2196/40755openalex-snapshot

Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework

inferredcites →https://doi.org/10.2196/41030openalex-snapshot

Chronic Disease Prediction Using the Common Data Model: Development Study

inferredcites →https://doi.org/10.2196/44909openalex-snapshot

Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study

inferredcites →https://doi.org/10.2196/46694openalex-snapshot

iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations

inferredcites →https://doi.org/10.2196/47095openalex-snapshot

Combinatorial Use of Machine Learning and Logistic Regression for Predicting Carotid Plaque Risk Among 5.4 Million Adults With Fatty Liver Disease Receiving Health Check-Ups: Population-Based Cross-Sectional Study

inferredcites →https://doi.org/10.2196/47592openalex-snapshot

An “All-Data-on-Hand” Deep Learning Model to Predict Hospitalization for Diabetic Ketoacidosis in Youth With Type 1 Diabetes: Development and Validation Study

inferredcites →https://doi.org/10.2196/47859openalex-snapshot

Synthetic Tabular Data Based on Generative Adversarial Networks in Health Care: Generation and Validation Using the Divide-and-Conquer Strategy

inferredcites →https://doi.org/10.2196/47995openalex-snapshot

Reporting and Methodological Observations on Prognostic and Diagnostic Machine Learning Studies

inferredcites →https://doi.org/10.2196/48763openalex-snapshot

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation

inferredcites →https://doi.org/10.2196/49023openalex-snapshot

Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial

inferredcites →https://doi.org/10.2196/49283openalex-snapshot

An Artificial Intelligence Model for Predicting Trauma Mortality Among Emergency Department Patients in South Korea: Retrospective Cohort Study

inferredcites →https://doi.org/10.2196/jmir.1923openalex-snapshot

CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health Interventions

inferredcites →https://doi.org/10.7326/0003-4819-152-11-201006010-00232openalex-snapshot

CONSORT 2010 Statement: Updated Guidelines for Reporting Parallel Group Randomized Trials

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1155/ijod/6730710openalex-snapshot

Mapping and Quality Appraisal of Artificial Intelligence Preferential Reporting Checklists, Items, Guidelines, and Consensus in Healthcare: An Altmetric, Bibliometric, and Systematic Review

inferred← citeshttps://doi.org/10.1177/20552076251380652openalex-snapshot

Application of artificial intelligence in medical risk prediction: Bibliometric analysis

inferred← citeshttps://doi.org/10.17816/dd680847openalex-snapshot

Good publication practices: declaration, approval, and now enforcement of reporting standards

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Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis

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Evaluation Frameworks for Clinical AI Incorporating Validation Strategies, Real-World Applicability, and Ethical Principles: Scoping Review

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An approach for diagnosis of diarrhea in neonatal piglets based on the core gut microbiota and machine learning

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Methodological and reporting quality of machine learning studies on cancer diagnosis, treatment, and prognosis

inferred← citeshttps://doi.org/10.3389/fonc.2025.1632026openalex-snapshot

Establishment and comparison of three fear of progression risk prediction models for gynecological malignancies patients based on machine learning

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Systematic literature review and narrative synthesis of the use of natural language processing to triage outpatient referrals

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Exploring Artificial Intelligence Biases in Predictive Models for Cancer Diagnosis

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Limitations of Retrospective Machine Learning Models for Predicting Tracheostomy After Cardiac Surgery

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