AIMC Topic: Causality

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Molecular causality in the advent of foundation models.

Molecular systems biology Jun 18, 2024
Correlation is not causation: this simple and uncontroversial statement has far-reaching implications. Defining and applying causality in biomedical research has posed significant challenges to the scientific community. In this perspective, we attemp...
Humans Machine Learning Causality Models, Biological Biomedical Research Systems Biology
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Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.

BMC medical informatics and decision making May 27, 2024
BACKGROUND: Modeling causality through graphs, referred to as causal graph learning, offers an appropriate description of the dynamics of causality. The majority of current machine learning models in clinical decision support systems only predict ass...
Humans Algorithms Machine Learning Causality
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Long-term causal effects estimation via latent surrogates representation learning.

Neural networks : the official journal of the International Neural Network Society Apr 29, 2024
Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications such as marketing and medicine. Most existing methods estimate causal effects in an idealistic and simplistic ...
Humans Neural Networks, Computer Causality Machine Learning Algorithms
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Causal machine learning for predicting treatment outcomes.

Nature medicine Apr 19, 2024
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating in...
Humans Treatment Outcome Causality Electronic Health Records Clinical Decision-Making Machine Learning
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Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.

European journal of clinical investigation Feb 20, 2024
BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer th...
Glasgow Coma Scale Humans Causality Bilirubin Intensive Care Units Gastrointestinal Hemorrhage Female Organ Dysfunction Scores Length of Stay Aged Middle Aged Prognosis Hospital Mortality Male Machine Learning Critical Illness Algorithms
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Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities.

Clinical pharmacology and therapeutics Jan 12, 2024
The use of data from randomized clinical trials to justify treatment decisions for real-world patients is the current state of the art. It relies on the assumption that average treatment effects from the trial can be extrapolated to patients with per...
Humans Machine Learning Causality
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Flexible Machine Learning Estimation of Conditional Average Treatment Effects: A Blessing and a Curse.

Epidemiology (Cambridge, Mass.) Oct 25, 2023
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning method...
Humans Random Forest Machine Learning Causality
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Causal inference and observational data.

BMC medical research methodology Oct 11, 2023
Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from obser...
Big Data Humans Causality Machine Learning
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"Shortcuts" Causing Bias in Radiology Artificial Intelligence: Causes, Evaluation, and Mitigation.

Journal of the American College of Radiology : JACR Jul 27, 2023
Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with disparate outputs for various subgroups limit the usefulness of AI in improving patients' lives. Many ...
Bias Radiology Humans Artificial Intelligence Causality Radiography
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Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems.

GigaScience Jul 3, 2023
BACKGROUND: Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformat...
Computer Simulation Causality Neural Networks, Computer
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