Pharmaceutical Outsourcing Q2 2024 - 15

CLINICAL TRIALS
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this library typically takes years to build and requires significant
ongoing effort to be maintained.
This resource-intensive process can be significantly streamlined
with the help of ML. For example, using an ML solution to perform
the first-line coding review. With this model, a rules-based coding
tool, which does not rely on a lengthy to develop and arduous to
maintain synonym list, runs to tackle the coding of easier terms.
Remaining verbatim terms that cannot be coded can be sent to
an ML model that finds the best match in the dictionary. The ML
coding recommendation would then be reviewed by the second-line
medical coder, who will accept or reject the suggestion. Overall, this
solution removes the need for a first-line medical coding review, and
for building and maintaining a synonym list.
For this ML model to yield accurate suggestions, it needs to read the
input term and the dictionary terms, understand their meaning, and
retrieve the proper entry. As such, it needs to be able to process and
understand natural language, making medical coding a perfect NLP
use case.
ML and DL are particularly well suited to this task, but what do we
really mean when we talk about leveraging ML and DL for NLP, and
what outcomes can we expect to see? (see Figure 2)
Techniques for Natural
Language Processing
There are different existing techniques available to tackle an NLP use
case. Among them, three main groups can be highlighted:
* Symbolic methods, for example, rule-based parsing or
approximate string matching.
* ML, for example, support vector machines or
logistic regression.
* DL, for example, recurrent neural network or large
language model.
While sharing the same goal of processing text, each of these
techniques has different levels of complexity and requires different
efforts. For example, symbolic methods require deep investigations
of the use case to be able to build complex logic that captures all
possible scenarios. ML - and even more so DL - techniques do not
require every single scenario to be encoded. Instead, they learn them
through data.
While DL is a subset of ML, it is helpful to separate the two as they have
different capabilities and limitations. ML typically requires a heavier
data preparation in which domain knowledge is used to incorporate
assumptions that will help the model learn the task. For example, text
can be cleansed to remove useless parts. With DL, the idea is to send
everything with minimal data preparation and let the model learn
what is useful. For this, the model needs to be more complex and,
thus, the amount of training data needed is greater.
When describing an NLP use case, it is interesting to specify which
technique is involved as each of them has pros and cons which
impact on the way the solution will be used. Some core differences in
the techniques include:
Figure 2.
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Pharmaceutical Outsourcing Q2 2024

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