What is the clinical evidence from this study?

Study design: Other. Population: Breathlessness (n=136215). Intervention: Bayesian Networks Clinical Decision Support System. Primary outcome: Predictive performance of the Bayesian network in diagnosing conditions associated with breathlessness.

What question did this study set out to answer?

The research aims to identify the underlying causes of breathlessness through data analytics using a Bayesian network.

January 10, 2026Scientific Reports0 citationsOpen Access

A Bayesian network for identifying causes of breathlessness using a national electronic medical records (EMR) database

PKPaul A. KellySouth Essex Partnership University Foundation NHS Trust ADAnthony DevauxThe University of Sydney CJChristine JenkinsThe University of Sydney

Key Result

The Bayesian Network demonstrated strong predictive performance with ROC-AUC values ranging from 0.68 for non-pneumonia LRTI to 0.94 for heart failure, indicating its potential to assist GPs in identifying causes of breathlessness.

Key Points

The research aims to identify the underlying causes of breathlessness through data analytics using a Bayesian network.
Utilized a national electronic medical records dataset.
Applied a Bayesian network model to analyze patient data.
Examined various potential causes associated with breathlessness.
Identified several key factors contributing to breathlessness.
Found that certain medical conditions are strongly linked to breathlessness.
Demonstrated the effectiveness of using a Bayesian network for causal inference.

Structured PICO

Does a Bayesian network algorithm accurately predict the causes of breathlessness in primary care patients?

Population

136,215 unique patients aged 16 years and over with 384,994 breathlessness episodes from 50 active training practices in the UK between 2002 and 2024. 55% female, 42% aged over 60 years.

Intervention

Bayesian Network algorithm for predicting causes of breathlessness

Outcome

Prediction of 10 conditions causing breathlessness (asthma, COPD, lung cancer, heart failure, chronic bronchitis, pneumonia, non-pneumonia LRTI, dysfunctional breathing, ILD, pulmonary embolism)

A Bayesian network algorithm using primary care EMR data demonstrated moderate to excellent predictive performance (ROC-AUC 0.68-0.94) for identifying 10 common causes of breathlessness.

Limitations

The study relies on electronic medical record data which may have limitations in terms of completeness and accuracy.
Exclusions in the dataset due to missing values, particularly for smoking status and gender.

Abstract

Breathlessness is a common symptom linked to various respiratory and cardiac conditions, often causing delays in diagnosis and management in primary care. This study aimed to develop an algorithm using Bayesian Networks (BN) to predict ten conditions associated with presentations of breathlessness. The study included individuals aged 16 years and older with breathlessness recorded as the reason for visiting a general practitioner (GP) between 2002 and 2024 in 50 practices in the United Kingdom (UK). Thirty-four characteristics including demographic, symptoms, comorbidities and medication histories were considered as predictors. A total of 384,994 breathlessness episodes from 136,215 patients were analysed, with 55% being female and 42% aged over 60 years. The BN achieved good performance with area under the curve (ROC-AUC) values ranging from 0.68 for non-pneumonia lower respiratory tract infections (LRTI) to 0.94 for heart failure. The algorithm demonstrated strong predictive performance and could help GPs prioritise early and targeted diagnostic tests for the most likely causes of breathlessness, potentially reducing both diagnostic delays and costs.

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