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Counterfactual Prediction Models in Medicine

Guest Editors:
Umesh Kumar Lilhore: KIET Group of Institutions, India
Matthew Sperrin: University of Manchester, UK


BMC Medical Informatics and Decision Making called for submissions to our Collection on Counterfactual Prediction Models in Medicine. Prediction models are not assured to be usable in counterfactual mode to assess hypothetical interventions. Yet, this important difference in capabilities of prediction models is sometimes overlooked. The aim of this collection was to encourage methodological and applied studies concerning the development, validation, and application of counterfactual prediction models in healthcare.

Meet the Guest Editors

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Umesh Kumar Lilhore: KIET Group of Institutions, India

Dr. Umesh Kumar Lilhore is a Professor CSE, at KIET Group of Institutions, Delhi-NCR, Ghaziabad (UP). He received his Doctoral Ph.D. in Computer Science Engineering. He has more than 17 years of experience in education, research, and consultancy. He has published more than 80 research articles in leading journals, conference proceedings, and books of high repute. His research interests mainly include Artificial Intelligence, Machine learning, IoT, Computer Security, Computational Intelligence, and Information Science.

Matthew Sperrin: University of Manchester, UK

Dr Sperrin received his PhD from Lancaster University, UK, in 2010, and has been at the University of Manchester, UK, since 2013. He has a range of interests in statistical modeling and inference, particularly in methods for clinical prediction. His current focuses are: causal inference to enable counterfactual predictions and to study fairness and generalisability; missing data, informative observation and adaptive sampling; and the updating of models over both time and space. He is the Fellow of the Alan Turing institute, and a Chartered Statistician with the Royal Statistical Society.


About the collection

Counterfactual prediction modeling is different from prediction modeling since it involves the evaluation of hypothetical scenarios where input variables can be intervened upon (i.e., changed) to modify the outcome. For instance, changing a behavior to decrease risk of disease, or changing a drug to improve disease outcome.

If data and experiments were randomized with respect to an intervention variable, prediction and counterfactual prediction would be basically the same. However, many data sources used to develop prediction models are not randomized (e.g., electronic health records) and can contain different biases (e.g., confounding, selection). Therefore, prediction models are not assured to be usable in counterfactual mode to assess hypothetical interventions. Yet, this important difference in capabilities of prediction models is sometimes overlooked.

The aim of this collection is to encourage methodological and applied studies concerning the development, validation, and application of counterfactual prediction models in healthcare.

  1. Coronary artery disease (CAD) is recognized as the leading cause of death worldwide. This study analyses CAD risk factors using an artificial neural network (ANN) to predict CAD.

    Authors: Nahid Azdaki, Fatemeh Salmani, Toba Kazemi, Neda Partovi, Saeede Khosravi Bizhaem, Masomeh Noori Moghadam, Yoones Moniri, Ehsan Zarepur, Noushin Mohammadifard, Hassan Alikhasi, Fatemeh Nouri, Nizal Sarrafzadegan, Seyyed Ali Moezi and Mohammad Reza Khazdair
    Citation: BMC Medical Informatics and Decision Making 2024 24:52
  2. This study was conducted to address the existing drawbacks of inconvenience and high costs associated with sleep monitoring. In this research, we performed sleep staging using continuous photoplethysmography (...

    Authors: Borum Nam, Beomjun Bark, Jeyeon Lee and In Young Kim
    Citation: BMC Medical Informatics and Decision Making 2024 24:50
  3. The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions ...

    Authors: Pedro Cardoso, John M. Dennis, Jack Bowden, Beverley M. Shields and Trevelyan J. McKinley
    Citation: BMC Medical Informatics and Decision Making 2024 24:12
  4. Diabetic kidney disease (DKD) has become the largest cause of end-stage kidney disease. Early and accurate detection of DKD is beneficial for patients. The present detection depends on the measurement of album...

    Authors: Shaomin Shi, Ling Gao, Juan Zhang, Baifang Zhang, Jing Xiao, Wan Xu, Yuan Tian, Lihua Ni and Xiaoyan Wu
    Citation: BMC Medical Informatics and Decision Making 2023 23:241
  5. High-dose methotrexate (HD-MTX) is a potent chemotherapeutic agent used to treat pediatric acute lymphoblastic leukemia (ALL). HD-MTX is known for cause delayed elimination and drug-related adverse events. The...

    Authors: Chang Jian, Siqi Chen, Zhuangcheng Wang, Yang Zhou, Yang Zhang, Ziyu Li, Jie Jian, Tingting Wang, Tianyu Xiang, Xiao Wang, Yuntao Jia, Huilai Wang and Jun Gong
    Citation: BMC Medical Informatics and Decision Making 2023 23:148
  6. Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative...

    Authors: Ashwini Venkatasubramaniam, Bilal A. Mateen, Beverley M. Shields, Andrew T. Hattersley, Angus G. Jones, Sebastian J. Vollmer and John M. Dennis
    Citation: BMC Medical Informatics and Decision Making 2023 23:110
  7. Magnetic resonance image (MRI) brain tumor segmentation is crucial and important in the medical field, which can help in diagnosis and prognosis, overall growth predictions, Tumor density measures, and care pl...

    Authors: Mukul Aggarwal, Amod Kumar Tiwari, M Partha Sarathi and Anchit Bijalwan
    Citation: BMC Medical Informatics and Decision Making 2023 23:78

Submission Guidelines

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This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. Articles for this Collection should be submitted via our submission system, SNAPP. During the submission process you will be asked whether you are submitting to a Collection, please select "Counterfactual Prediction Models in Medicine" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Guest Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.