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This summary is machine-generated.

The AUTOCOD deep neural network reliably determines causes of death from physician death certificates, even during periods of high mortality. Its performance remains consistent, ensuring accurate real-time mortality surveillance.

Keywords:
AIartificial intelligencedeep learningdeep neural networksevaluationmachine learningmortalitymortality statisticsunderlying cause of death

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Area of Science:

  • Public Health Surveillance
  • Artificial Intelligence in Medicine
  • Mortality Data Analysis

Background:

  • Portugal experienced over 16,000 excess deaths in 2021, highlighting the need for efficient mortality data analysis.
  • The Directorate-General of Health developed AUTOCOD, a deep neural network for automated cause of death determination from death certificates.
  • AUTOCOD's consistent performance during periods of excess mortality, a critical factor for public health, required evaluation.

Purpose of the Study:

  • To assess the sensitivity and performance metrics of AUTOCOD in classifying underlying causes of death compared to manual coding.
  • To identify specific causes of death during periods of excess mortality using AUTOCOD.
  • To evaluate AUTOCOD's reliability under varying mortality conditions.

Main Methods:

  • Analyzed 330,098 death certificates (DCs) from 2016-2019, comparing AUTOCOD classifications with manual coding (gold standard).
  • Calculated performance metrics including sensitivity, specificity, and positive predictive value (PPV) using a confusion matrix.
  • Compared AUTOCOD performance during periods of normal, excess, severe, and extreme excess mortality, defining excess mortality by Z-scores.

Main Results:

  • AUTOCOD demonstrated high sensitivity (≥0.75) for 10 ICD-10 chapters, exceeding 0.90 for prevalent chapters like Neoplasms and Circulatory/Respiratory diseases.
  • Performance metrics (sensitivity, specificity >0.96, PPV >0.75) remained consistent across periods with and without excess mortality.
  • AUTOCOD maintained high performance at the ICD-10 block level, with no significant differences observed during excess mortality periods.

Conclusions:

  • AUTOCOD's performance is unaffected by potential text quality degradation during high-pressure health service periods.
  • The deep neural network can be reliably used for real-time, cause-specific mortality surveillance, even during extreme excess mortality events.
  • AUTOCOD provides dependable data for public health decision-making and monitoring.