Machine learning-extended sPESI for 1-year mortality prediction in pulmonary embolism

March 25, 2026Tuberkuloz ve ToraksOpen Access

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Key Points

This research aims to improve 1-year mortality predictions in pulmonary embolism using a machine learning-extended sPESI framework.
Utilized machine learning algorithms to analyze data from pulmonary embolism cases.
Focused on physiological indicators from the patient's initial presentation.
Developed a risk stratification model to assess long-term mortality risk.
The machine learning model provided enhanced accuracy in predicting 1-year mortality.
Demonstrated the ability to capture both acute and long-term health vulnerabilities.
Indicated that individualized patient management could improve outcomes.

Machine learning enhances mortality prediction in pulmonary embolism, indicating improved individualized patient management.

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Elvan Burak Verdi (2026) studied this question.

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