Deep learning-based aecopd with depression prognosis method, device and electronic equipment

By using deep learning methods to train a prognostic model with patient data, the problem of accurate prognosis for AECOPD with depression has been solved, and rapid and accurate prediction of CID-C has been achieved.

CN122290965APending Publication Date: 2026-06-26SHANXI BETHUNE HOSPITAL (SHANXI ACAD OF MEDICAL SCI SHANXI HOSPITAL OF TONGJI HOSPITAL AFFILIATED TO TONGJI MEDICAL COLLEGE OF HUAZHONG UNIV OF SCI & TECH SHANXI MEDICAL UNIV THIRD HOSPITAL SHANXI MEDICAL UNIV THIRD CLINICAL COLLEGE OF MEDICINE)
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHANXI BETHUNE HOSPITAL (SHANXI ACAD OF MEDICAL SCI SHANXI HOSPITAL OF TONGJI HOSPITAL AFFILIATED TO TONGJI MEDICAL COLLEGE OF HUAZHONG UNIV OF SCI & TECH SHANXI MEDICAL UNIV THIRD HOSPITAL SHANXI MEDICAL UNIV THIRD CLINICAL COLLEGE OF MEDICINE)
Filing Date
2026-03-30
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

With current technology, the prognosis of AECOPD combined with depression is difficult to determine accurately, relying heavily on the doctor's experience and presenting significant challenges.

Method used

A deep learning-based prognostic approach was adopted, utilizing inpatient and discharge data of AECOPD patients with depression, including demographic characteristics, serum neuroactive substances, arterial blood gas analysis, and pulmonary function tests, to generate training samples and train a prognostic model to predict the timing and probability of CID-C after discharge.

Benefits of technology

It enables rapid and accurate prognosis for AECOPD comorbid with depression, especially by incorporating serum neuroactive substances and inflammatory marker data, which improves the accuracy of prognosis.

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Abstract

This disclosure relates to the field of computer-aided healthcare, providing a deep learning-based prognostic method, device, and electronic device for AECOPD comorbid with depression. The method includes: acquiring inpatient and post-discharge data of sample patients with AECOPD and depression; inpatient data including examination data upon admission and upon discharge, including peripheral blood serum neuroactive substance data and peripheral blood inflammatory marker data; and post-discharge data including the interval between the first occurrence of CID-C after discharge, the number of CID-C occurrences within 6 months after discharge, and the probability of CID-C occurring within 90 days after discharge; generating training samples based on the inpatient and post-discharge data, and storing the training samples in a training dataset; training a prognostic model using the training dataset; and performing prognostic analysis on target patients with AECOPD and depression using the trained prognostic model. The prognostic method provided by this disclosure can provide rapid and accurate prognosis for AECOPD comorbid with depression.
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