4.7 Letter

Machine Learning Compared With Pathologist Assessment

Journal

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
Volume 319, Issue 16, Pages 1725-1726

Publisher

AMER MEDICAL ASSOC
DOI: 10.1001/jama.2018.1466

Keywords

-

Ask authors/readers for more resources

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Medicine, General & Internal

Assessing Performance and Clinical Usefulness in Prediction Models With Survival Outcomes: Practical Guidance for Cox Proportional Hazards Models

David J. McLernon, Daniele Giardiello, Ben Van Calster, Laure Wynants, Nan van Geloven, Maarten van Smeden, Terry Therneau, Ewout W. Steyerberg, STRATOS Initiative

Summary: Risk prediction models need validation to assess their performance. This article focuses on evaluating predictions and improving clinical decision making using survival models based on Cox proportional hazards regression. The authors present a case study on breast cancer patients, where a Cox regression model is developed and validated for prediction of recurrence or death.

ANNALS OF INTERNAL MEDICINE (2023)

Article Cardiac & Cardiovascular Systems

Applicability of European Society of Cardiology guidelines according to gross national income

Wouter B. van Dijk, Ewoud Schuit, Rieke van der Graaf, Rolf H. H. Groenwold, Sara Laurijssen, Barbara Casadei, Marco Roffi, Seye Abimbola, Martine C. de Vries, Diederick E. Grobbee

Summary: This study evaluated the feasibility of implementing ESC guidelines on general cardiology in 102 countries and found that compliance varied based on the country's income level. High-income countries had better compliance, while low-income countries had lower compliance, largely due to lack of reimbursement and financial barriers.

EUROPEAN HEART JOURNAL (2023)

Editorial Material Endocrinology & Metabolism

Is it a risk factor, a predictor, or even both? The multiple faces of multivariable regression analysis

Rolf H. H. Groenwold, Olaf M. Dekkers

Summary: The medical research literature often includes regression analyses with multiple covariates, known as multivariable regression models. However, the interpretation of their results is often unclear or lacks justification. This article outlines different research objectives that justify using multivariable regression analysis, as well as caveats in interpreting the results.

EUROPEAN JOURNAL OF ENDOCRINOLOGY (2023)

Article Mathematical & Computational Biology

Comparison of likelihood penalization and variance decomposition approaches for clinical prediction models: A simulation study

Anna Lohmann, Rolf H. H. Groenwold, Maarten van Smeden

Summary: Logistic regression is commonly used in developing clinical risk prediction models. Penalization and variance decomposition techniques have been shown to improve predictive performance of the logistic model. In this study, we compared the predictive performance of risk prediction models derived from elastic net, Lasso, ridge, incomplete principal component regression, and incomplete partial least squares regression using simulation. Our results demonstrate that models developed using penalization approaches outperform those developed using maximum likelihood estimation, particularly in terms of calibration.

BIOMETRICAL JOURNAL (2023)

Article Medicine, General & Internal

There is no such thing as a validated prediction model

Ben Van Calster, Ewout W. W. Steyerberg, Laure Wynants, Maarten van Smeden

Summary: Clinical prediction models should be validated before implementation, as the performance of a model may vary across patient populations and measurement procedures. Validation studies should focus on understanding and quantifying heterogeneity and monitoring performance over time, to ensure that prediction models remain up-to-date and safe for clinical decision-making.

BMC MEDICINE (2023)

Review Health Care Sciences & Services

Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review

Paula Dhiman, Jie Ma, Constanza L. Andaur Navarro, Benjamin Speich, Garrett Bullock, Johanna A. A. Damen, Lotty Hooft, Shona Kirtley, Richard D. Riley, Ben Van Calster, Karel G. M. Moons, Gary S. Collins

Summary: This article conducted a systematic review on oncology-related studies that developed and validated prognostic models using machine learning. The findings revealed the presence of spin, i.e., overinterpretation of findings, in these studies. The inconsistent reporting and use of overly strong or leading words in the publications indicate the need for caution when reading and using prognostic models in oncology.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2023)

Article Cardiac & Cardiovascular Systems

Cardiovascular Risk Prediction in Men and Women Aged Under 50 Years Using Routine Care Data

Hendrikus J. A. van Os, Jos P. Kanning, Tobias N. Bonten, Margot M. Rakers, Hein Putter, Mattijs E. Numans, Ynte M. Ruigrok, Rolf H. H. Groenwold, Marieke J. H. Wermer

Summary: This study aimed to develop prediction models for the risk of first-ever cardiovascular events in men and women aged 30 to 49 years. The study found that the models based on traditional cardiovascular predictors had moderate discriminative performance in terms of gender differences. However, the addition of nontraditional cardiovascular predictors improved the performance of the models.

JOURNAL OF THE AMERICAN HEART ASSOCIATION (2023)

Editorial Material Ethics

Enabling the Nonhypothesis-Driven Approach: On Data Minimalization, Bias, and the Integration of Data Science in Medical Research and Practice

C. W. Safarlou, M. van Smeden, R. Vermeulen, K. R. Jongsma

AMERICAN JOURNAL OF BIOETHICS (2023)

Article Mathematical & Computational Biology

The marginality principle revisited: Should higher-order terms always be accompanied by lower-order terms in regression analyses?

Tim P. Morris, Maarten van Smeden, Tra My Pham

Summary: The marginality principle guides analysts to include lower-order terms in models to avoid omitting higher-order terms. However, the determination of lower-order terms depends on the scale of measurement and is subjective.

BIOMETRICAL JOURNAL (2023)

Article Cardiac & Cardiovascular Systems

Diagnostic management of acute pulmonary embolism: a prediction model based on a patient data meta-analysis

Nick van Es, Toshihiko Takada, Noemie Kraaijpoel, Frederikus A. Klok, Milou A. M. Stals, Harry R. Buller, D. Mark Courtney, Yonathan Freund, Javier Galipienzo, Gregoire Le Gal, Waleed Ghanima, Menno Huisman, Jeffrey A. Kline, Karel G. M. Moons, Sameer Parpia, Arnaud Perrier, Marc Righini, Helia Robert-Ebadi, Pierre-Marie Roy, Phil S. Wells, Kerstin de Wit, Maarten van Smeden, Geert-Jan Geersing

Summary: The aim of this study was to develop a clinical prediction model for acute pulmonary embolism (PE) based on readily available clinical items and D-dimer concentrations. The model showed good discrimination and calibration, and outperformed the currently widely-used Wells score and D-dimer testing methods.

EUROPEAN HEART JOURNAL (2023)

Review Health Care Sciences & Services

Systematic review finds risk of bias and applicability concerns for models predicting central line-associated bloodstream infection

Shan Gao, Elena Albu, Krizia Tuand, Veerle Cossey, Frank Rademakers, Ben Van Calster, Laure Wynants

Summary: This systematic review assessed the risk of bias and applicability of published prediction models for central line-associated bloodstream infection (CLA-BSI) in hospitalized patients. The results revealed a lack of prediction models with potential clinical use and highlighted the urgent need for the development of an applicable model for CLA-BSI.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2023)

Letter Multidisciplinary Sciences

Provide public access to ethics-approved study protocols

Ben Van Calster

NATURE (2023)

Article Health Care Sciences & Services

Multiclass risk models for ovarian malignancy: an illustration of prediction uncertainty due to the choice of algorithm

Ashleigh Ledger, Jolien Ceusters, Lil Valentin, Antonia Testa, Caroline Van Holsbeke, Dorella Franchi, Tom Bourne, Wouter Froyman, Dirk Timmerman, Ben Van Calster

Summary: Assessing the malignancy risk of ovarian tumors is crucial for selecting appropriate management. In this study, six algorithms were compared to estimate the probabilities of different types of ovarian tumors. The models developed showed good performance, but individual probability estimates varied substantially.

BMC MEDICAL RESEARCH METHODOLOGY (2023)

Article Obstetrics & Gynecology

A blended preconception lifestyle programme for couples undergoing IVF: lessons learned from a multicentre randomized controlled trial

Tessy Boedt, Eline Dancet, Diane De Neubourg, Sofie Vereeck, Jan Seghers, Katleen van der Gucht, Ben Van Calster, Carl Spiessens, Sharon Lie Fong, Christophe Matthys

Summary: This randomized controlled trial aimed to assess the impact of a blended preconception lifestyle programme on reproductive and lifestyle outcomes of couples undergoing IVF. However, the trial was prematurely stopped due to the Covid-19 pandemic and the results indicate that the programme did not have a meaningful effect on time to ongoing pregnancy or other reproductive and lifestyle outcomes.

HUMAN REPRODUCTION OPEN (2023)

No Data Available