Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU

Gani, Md Osman; Kethireddy, Shravan; Adib, Riddhiman; Hasan, Uzma; Griffin, Paul M.; Adibuzzaman, Mohammad

doi:10.1016/j.artmed.2023.102493

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…• Gani et al, 2023 studied the effect of liberal -target SpO2 greater than 96%-vs conservative oxygenation -target SpO2 between 88-95%-in 4,062 mechanically ventilated patients on 90-day mortality. They found an advantage of the liberal strategy over liberal (ATE=0.13) by adjusting on age and apsii.…”

Section: Appendix B Estimation Of Treatment Effect With Mimic Datamentioning

confidence: 99%

Step-by-step causal analysis of Electronic Health Records to ground decision making

Doutreligne,

Struja,

Abecassis

et al. 2023

Preprint

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Accurate predictions, as with machine learning, may not suffice to provide optimal healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such as racial biases. Causal thinking is needed for data-driven decisions. Here, we give an introduction to the key elements, focusing on routinely-collected data, electronic health records (EHRs) and claims data. Using such data to assess the value of an intervention requires care: temporal dependencies and existing practices easily confound the causal effect. We present a step-by-step framework to help build valid decision making from real-life patient records by emulating a randomized trial before individualizing decisions, eg with machine learning. Our framework highlights the most important pitfalls and considerations in analysing EHRs or claims data to draw causal conclusions. We illustrate the various choices in studying the effect of albumin on sepsis mortality in the Medical Information Mart for Intensive Care database (MIMIC-IV). We study the impact of various choices at every step, from feature extraction to causal-estimator selection. In a tutorial spirit, the code and the data are openly available.

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Section: Appendix B Estimation Of Treatment Effect With Mimic Datamentioning

confidence: 99%

Step-by-step causal analysis of Electronic Health Records to ground decision making

Doutreligne,

Struja,

Abecassis

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Genetic programming optimizes CNN structures for Covid-19 diagnosis from X-ray images [34]. Causal inference techniques unveil a framework for estimating interventions' impact in clinical settings [35]. MultiCoFusion transforms cancer diagnosis by integrating histopathological images and genomic data for survival analysis and grade classification [36].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Graph Neural Network in Healthcare-Based Applications: Recent Advances, Trends, and Future Directions

Paul,

Saha,

Hasan

et al. 2024

IEEE Access

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Graph neural network (GNN) is a formidable deep learning framework that enables the analysis and modeling of intricate relationships present in data structured as graphs. In recent years, a burgeoning interest has arisen in exploiting the latent capabilities of GNN for healthcare-based applications, capitalizing on their aptitude for modeling complex relationships and unearthing profound insights from graph-structured data. However, to the best of our knowledge, no study has systemically reviewed the GNN studies conducted in the healthcare domain. This study has furnished an all-encompassing and erudite overview of the prevailing cutting-edge research on GNN in healthcare. Through analysis and assimilation of studies, current research trends, recurrent challenges, and promising future opportunities in GNN for healthcare applications have been identified. China emerged as the leading country to conduct GNN-based studies in the healthcare domain, followed by the USA, UK, and Turkey. Among various aspects of healthcare, disease prediction and drug discovery emerge as the most prominent areas of focus for GNN application, indicating the potential of GNN for advancing diagnostic and therapeutic approaches. This study proposed research questions regarding diverse aspects of GNN in the healthcare domain and addressed them through an in-depth analysis. This study can provide practitioners and researchers with profound insights into the current landscape of GNN applications in healthcare and can guide healthcare institutes, researchers, and governments by demonstrating the ways in which GNN can contribute to the development of effective and efficient healthcare systems.

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Quantitative causality assessment between traffic states and crash risk in freeway segments with closely spaced entrance and exit ramps

Zhao,

Liu

2024

Physica A: Statistical Mechanics and its Applications

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Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU

Cited by 3 publications

References 60 publications

Step-by-step causal analysis of Electronic Health Records to ground decision making

Step-by-step causal analysis of Electronic Health Records to ground decision making

A Systematic Review of Graph Neural Network in Healthcare-Based Applications: Recent Advances, Trends, and Future Directions

Quantitative causality assessment between traffic states and crash risk in freeway segments with closely spaced entrance and exit ramps

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