AIMC Topic: Bias

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State of the Art Causal Inference in the Presence of Extraneous Covariates: A Simulation Study.

AMIA ... Annual Symposium proceedings. AMIA Symposium Feb 21, 2022
The central task of causal inference is to remove (via statistical adjustment) confounding bias that would be present in naive unadjusted comparisons of outcomes in different treatment groups. Statistical adjustment can roughly be broken down into tw...
Bias Humans Computer Simulation Causality Models, Statistical
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Bias In, Bias Out: Ethical Considerations for the Application of Machine Learning in Pediatrics.

The Journal of pediatrics Feb 2, 2022
Bias Pediatrics Morals Machine Learning Child Humans
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Machine Learning Reimagined: The Promise of Interpretability to Combat Bias.

Annals of surgery Jan 25, 2022
Machine Learning Bias Humans
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Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?

Medical physics Jan 12, 2022
PURPOSE: Over the last 2 years, the artificial intelligence (AI) community has presented several automatic screening tools for coronavirus disease 2019 (COVID-19) based on chest radiography (CXR), with reported accuracies often well over 90%. However...
Radiography Bias SARS-CoV-2 Artificial Intelligence Deep Learning Humans COVID-19
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Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review.

Journal of clinical epidemiology Nov 16, 2021
OBJECTIVES: Missing data is a common problem during the development, evaluation, and implementation of prediction models. Although machine learning (ML) methods are often said to be capable of circumventing missing data, it is unclear how these metho...
Bias Models, Statistical Data Interpretation, Statistical Machine Learning Prognosis Humans
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Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias.

British journal of anaesthesia Nov 9, 2021
BACKGROUND: Artificial intelligence (AI) has the potential to personalise mechanical ventilation strategies for patients with respiratory failure. However, current methodological deficiencies could limit clinical impact. We identified common limitati...
Randomized Controlled Trials as Topic Bias Respiration, Artificial Models, Theoretical Respiratory Insufficiency Research Report Humans Algorithms Research Design Artificial Intelligence
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Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective.

Sensors (Basel, Switzerland) Nov 1, 2021
In this article, we study activity recognition in the context of sensor-rich environments. In these environments, many different constraints arise at various levels during the data generation process, such as the intrinsic characteristics of the sens...
Humans Machine Learning Reproducibility of Results Human Activities Bias Motion
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On learning disentangled representations for individual treatment effect estimation.

Journal of biomedical informatics Oct 30, 2021
OBJECTIVE: Estimating the individualized treatment effect (ITE) from observational data is a challenging task due to selection bias, which results from the distributional discrepancy between different treatment groups caused by the dependence between...
Selection Bias Machine Learning Prognosis Learning Bias
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Predicting response to immunotherapy plus chemotherapy in patients with esophageal squamous cell carcinoma using non-invasive Radiomic biomarkers.

BMC cancer Oct 30, 2021
OBJECTIVES: To develop and validate a radiomics model for evaluating treatment response to immune-checkpoint inhibitor plus chemotherapy (ICI + CT) in patients with advanced esophageal squamous cell carcinoma (ESCC).
Bias Esophageal Neoplasms Carboplatin Tomography, X-Ray Computed Docetaxel Female Antibodies, Monoclonal, Humanized Combined Modality Therapy Humans Support Vector Machine Antineoplastic Combined Chemotherapy Protocols Middle Aged Esophageal Squamous Cell Carcinoma Treatment Outcome Biomarkers, Tumor Immune Checkpoint Inhibitors Nomograms Male
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Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.

BMJ (Clinical research ed.) Oct 20, 2021
OBJECTIVE: To assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties.
Bias Multivariate Analysis Clinical Decision Rules Risk Machine Learning Humans Models, Statistical Data Interpretation, Statistical
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