title: A Comparative Study on Automatic Coding of Medical Letters with
    Explainability
summary: This study aims to explore the implementation of Natural Language Processing
  (NLP) and machine learning (ML) techniques to automate the coding of medical
  letters with visualised explainability and light-weighted local computer
  settings. Currently in clinical settings, coding is a manual process that
  involves assigning codes to each condition, procedure, and medication in a
  patient's paperwork (e.g., 56265001 heart disease using SNOMED CT code). There
  are preliminary research on automatic coding in this field using
  state-of-the-art ML models; however, due to the complexity and size of the
  models, the real-world deployment is not achieved. To further facilitate the
  possibility of automatic coding practice, we explore some solutions in a local
  computer setting; in addition, we explore the function of explainability for
  transparency of AI models. We used the publicly available MIMIC-III database
  and the HAN/HLAN network models for ICD code prediction purposes. We also
  experimented with the mapping between ICD and SNOMED CT knowledge bases. In our
  experiments, the models provided useful information for 97.98\% of codes. The
  result of this investigation can shed some light on implementing automatic
  clinical coding in practice, such as in hospital settings, on the local
  computers used by clinicians , project page
  https://github.com/Glenj01/Medical-Coding.
paper_authors._id: 6699ee22aa8e2bb22e4c4303
paper_authors.name: Jamie Glen
paper_authors._id: 6699ee22aa8e2bb22e4c4304
paper_authors.name: Lifeng Han
paper_authors._id: 6699ee22aa8e2bb22e4c4305
paper_authors.name: Paul Rayson
paper_authors._id: 6699ee22aa8e2bb22e4c4306
paper_authors.name: Goran Nenadic
paper_title: A Comparative Study on Automatic Coding of Medical Letters with
    Explainability
paper_summary: This study aims to explore the implementation of Natural Language Processing
  (NLP) and machine learning (ML) techniques to automate the coding of medical
  letters with visualised explainability and light-weighted local computer
  settings. Currently in clinical settings, coding is a manual process that
  involves assigning codes to each condition, procedure, and medication in a
  patient's paperwork (e.g., 56265001 heart disease using SNOMED CT code). There
  are preliminary research on automatic coding in this field using
  state-of-the-art ML models; however, due to the complexity and size of the
  models, the real-world deployment is not achieved. To further facilitate the
  possibility of automatic coding practice, we explore some solutions in a local
  computer setting; in addition, we explore the function of explainability for
  transparency of AI models. We used the publicly available MIMIC-III database
  and the HAN/HLAN network models for ICD code prediction purposes. We also
  experimented with the mapping between ICD and SNOMED CT knowledge bases. In our
  experiments, the models provided useful information for 97.98\% of codes. The
  result of this investigation can shed some light on implementing automatic
  clinical coding in practice, such as in hospital settings, on the local
  computers used by clinicians , project page
  https://github.com/Glenj01/Medical-Coding.
paper_ai_keywords: HAN/HLAN network models ICD code prediction SNOMED CT knowledge bases