  In this paper, we introduce a comprehensive framework for developing a
machine learning-based SOAP (Subjective, Objective, Assessment, and Plan)
classification system without manually SOAP annotated training data or with
less manually SOAP annotated training data. The system is composed of the
following two parts: 1) Data construction, 2) A neural network-based SOAP
classifier, and 3) Transfer learning framework. In data construction, since a
manual construction of a large size training dataset is expensive, we propose a
rule-based weak labeling method utilizing the structured information of an EHR
note. Then, we present a SOAP classifier composed of a pre-trained language
model and bi-directional long-short term memory with conditional random field
(Bi-LSTM-CRF). Finally, we propose a transfer learning framework that re-uses
the trained parameters of the SOAP classifier trained with the weakly labeled
dataset for datasets collected from another hospital. The proposed weakly
label-based learning model successfully performed SOAP classification (89.99
F1-score) on the notes collected from the target hospital. Otherwise, in the
notes collected from other hospitals and departments, the performance
dramatically decreased. Meanwhile, we verified that the transfer learning
framework is advantageous for inter-hospital adaptation of the model increasing
the models' performance in every cases. In particular, the transfer learning
approach was more efficient when the manually annotated data size was smaller.
We showed that SOAP classification models trained with our weakly labeling
algorithm can perform SOAP classification without manually annotated data on
the EHR notes from the same hospital. The transfer learning framework helps
SOAP classification model's inter-hospital migration with a minimal size of the
manually annotated dataset.
