Pharmaceutical Outsourcing Q2 2024 - 16

CLINICAL TRIALS
* Performance.
* The kind of mistakes made.
* Interpretability, for example, large language models have
difficulties providing interpretability while symbolic methods
provide it easily.
* Maintenance, for example, rule-based algorithms typically
need much more manual updating.
Leveraging Deep Learning for
Medical Coding
When implementing a solution for a new use case, its specific
challenges should guide the choice of the technology. Medical
coding brings specific challenges including:
* Complex vocabulary - medical terminology, drug names.
* Frequent new words - COVID-19, new treatments.
* Many possible outputs - dictionary entries.
Thanks to its capabilities, DL can address these challenges. Firstly, as
explained above, the idea when leveraging DL is to send the input
term with minimal data preparation and rely on the flexibility of the
model to adapt to any scenario. This allows, for example, the model
to learn any relationship across words within the input term and not
rely on assumptions that might be wrong for specific cases. This also
removes the need to implement organization-specific scenarios.
However, as this flexibility is obtained by increasing the complexity
of the model, it comes with the cost of requiring enough training
data, typically hundreds of thousands of samples in the case of
medical coding.
Secondly, the DL model can automatically learn new words when
it encounters them. This means, when training the model with data
containing an unknown word, the model automatically remembers
it and can properly handle it afterwards. As there is no manual
intervention for this step, it increases the ability of the model to adapt
to the change of medical coding vocabulary.
DL can also leverage the semantics. This is done using embeddings
that encode the meaning of words, i.e., vectors of real numbers. For
example, 'ache' and 'pain' have very similar vectors which allows the
model to understand that their meanings are close to each other.
This helps the model properly select the right dictionary entry from
many choices and deal with high variability from the input terms in
expressing the same concept.
Thanks to this technique, and others like transfer learning, it is also
possible to include a priori medical knowledge - knowledge the
DL model has prior to being trained for the task of medical coding.
This means we can leverage external sources of information for the
model to know in advance medical concepts and terms. In addition
to making the model globally better for the task of medical coding, it
also allows it to handle terms never seen in training data.
The ability to handle unseen dictionary entries means the solution is
able to handle different dictionary versions without retraining. The
only required change is to point the model to the new version of
the dictionary.
Instead of outputting only one dictionary entry, the solution can also
suggest several entries to review together with a confidence score.
This can be used to bring attention to terms that have a low confidence
score and may even result in them being handled differently to those
with a high score.
DL solutions can achieve accuracies higher than 90% in both adverse
events and medications. For example, when seeing the input term
'Probable covid-19 infection', some models can properly code it into
'Suspected COVID-19'. They can ignore the less meaningful 'infection'
to focus on 'covid-19' as well as understanding 'probable' is similar to
'suspected' in this situation. Such models have also been able to code
the input term 'Honeydew melon allergy' to the dictionary term of
'Fruit allergy', as they understand that melon is a fruit.
These results demonstrate that DL is suitable for medical coding. It
performs well on both adverse events and medications, allowing
teams to use the same solution for both applications, by training on
different data sets.
Next Steps
The next steps for ML and DL in medical coding are query detection
and direct coding of high-confidence terms.
Currently, auto coded input terms that should have a query are not
found until there is a quality control process. ML and DL can be used
to support the creation of queries within the medical coding process
itself. A simple example is when an input term is found in the wrong
dataset, such as when the surgical operation 'Wisdom tooth removal'
is found in the AE dataset.
DL also offers the opportunity for more efficient final coding review
due to higher accuracy direct coding of some terms and queries being
raised automatically. This requires achieving a high confidence in the
model output, leveraging the confidence score described above. The
idea is to establish a confidence score threshold above which terms
can be directly coded and approved without manual review. Lowerconfidence
terms would still require human coding.
Automated query detection and direct coding, combined with
removing the need for auto-coder tools, first-line coding review and
more efficient processes for dictionary updates, make a compelling
case for the role of DL and ML in the future of medical coding. The
use case reinforces that, when leveraged properly, DL can bring
significant value in clinical data management.
Pharmaceutical Outsourcing | 16 | April/May/June 2024
Pharmaceutical Outsourcing Q2 2024

Table of Contents for the Digital Edition of Pharmaceutical Outsourcing Q2 2024
