A Comparative Study on Automatic Coding of Medical Letters with
  Explainability
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.
6699ee22aa8e2bb22e4c4303
Jamie Glen
6699ee22aa8e2bb22e4c4304
Lifeng Han
6699ee22aa8e2bb22e4c4305
Paul Rayson
6699ee22aa8e2bb22e4c4306
Goran Nenadic
A Comparative Study on Automatic Coding of Medical Letters with
  Explainability
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.
HAN/HLAN network models were used to automate the coding of medical letters with SNOMED CT code mapping, achieving 97.98% accuracy and local machine deployment.
HAN HLAN ICD SNOMED CT explainability