Background: Classifying diseases into ICD codes has mainly relied on human reading many written materials, such as discharge diagnoses, chief complaints, medical history, and operation records as the basis for classification. Coding is both laborious and time-consuming because a disease coder with professional abilities takes about 20 minutes per case on average. Therefore, an automatic code classification system can significantly reduce human effort.ICD-10(International Classification of Diseases 10th revision) is a classification of a disease, symptom, procedure, or injury. Diseases are often described inpatients’ medical records with free texts, such as terms, phrases and paraphrases, which differ significantly from those used in ICD-10 classification. Objectives: This paper aims at constructing a machine learning model forICD-10 coding, where the model is to automatically determine the corresponding diagnosis codes solely based on free-text medical notes.Methods: This paper applies Natural Language Processing (NLP) and Recur-rent Neural Network (RNN) architecture with Self-attention mechanism and transformers to classify ICD-10 codes from natural language texts with super-vised learning. Results: Our predicting result can reach F1-score of 0.82 on ICD-10-CM code in the experiments on extensive teleconsultation data. Conclusion: The developed model can significantly reduce human resources in coding time compared with a professional coder.

1. Automatic ICD-10 Code
Assignment to Tele-Consultations
Nirav Kumar, Director of Data Science
Joinal Ahmed, Data Scientist - II

2. 1. Introduction to Halodoc.
2. Machine Learning @ Halodoc.
3. Insurance Claim Adjudication
a. How ICD-10 is useful in claim processing?
b. ICD-10 code and anatomy.
c. How AI can solve this problem?
4. Building a Solution
a. Gathering the Data
b. Leveraging Deep Learning for Multi Label multi class
classification.
Agenda

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Machine Learning @ Halodoc
● Accessing Quality of Care
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Why ICD-10 for Claim Adjudication?
● Traditional Claim Adjudication process is time consuming.
○ Turnaround time of 24+ hours
○ Needs a manual coder to go through the claim and assign
code
● ICD-10 code can describe the ailment accurately
○ can be used up understand the patients needs
○ build a health profile*
○ can be used to give a personalised experience

9. What is ICD-10 code ?
International Classification of Diseases(ICD) - 10, is a standardized way of indicating diagnoses and procedures that
were performed during the consultation. ICD-10 codes have a variety of uses, ranging from Billing, Insurance claims to
predictive modeling of patient’s conditions with over 140,000 codes combined in the ICD-10-CM and ICD-10-PCS
taxonomies.

10. ICD-10 Code Anatomy

11. ICD-10 Code Anatomy

12. Proposed Solution : Leveraging AI to tag consultations
12

13. Gathering Data at Scale
13
● Consultations are labeled with
diagnostic codes by doctors and
analysts.
● Raw data from RDS is dumped to S3
buckets and later processed and
populated in Data Lake using Apache
Spark and Hudi on Amazon EMR.

14. Model Architecture: Deep Neural Nets with Attention
Embedding BiLSTM Label Attention Layer Output Layer

15. Embedding Generation - Leveraging Word2Vec

16. Embedding Model : CBOW Word2Vec
We utilize CBOW Word2Vec architecture for creating word embeddings that uses 𝑛 future words as well as 𝑛 past
words to create a word embedding.
The objective function for CBOW is:
Using Text data from doctor notes and ICD-10 descriptions in bahasa, the Word2Vec
model is trained with embedding size=256.
This Word2Vec model is used to generate text embeddings for input doctor notes
while Training the model and making predictions while inference

17. BiLSTM Layer
We use a BiLSTM architecture to capture contextual information across input words in D. In particular,
we use the BiLSTM to learn latent feature vectors representing input words from a sequence ew1 :wn of
vectors ew1 , ew2 , ..., ewn . We compute the hidden states of the LSTMs corresponding to the ith word (i ∈
{1,...,n}) as:
The dimensionality of the LSTM hidden states is set to u, resulting in the
size of the latent vectors hi at 2u. All the hidden state vectors of words in
D are concatenated to formulate a matrix H = [h1, h2, ..., hn] ∈ R2u×n.

18. Label Attention Layer
As the clinical documents have different lengths and each document has multi-labels, our goal
is to transform H (input from embedding layer) into label-specific vectors. We achieve that goal
by proposing a label attention mechanism. Our label attention mechanism takes H as the input
and output |L| label-specific vectors representing the input doctor note D .

19. Label Classification Layer
Given the vector document representation vℓ, we compute a probability for label ℓ using another linear layer and a sigmoid
transformation:
where 𝜷ℓ s a vector of prediction weights, and 𝑏ℓ is a scalar offset.

20. Training
The training procedure minimizes the binary cross-entropy loss,
plus the L2 norm of the model weights, using the adam optimizer.

21. Results
accuracy precision recall
Training 0.93 0.93 0.87
Validation 0.96 0.96 0.93

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25. Appendix
Hierarchical Structure

26. 26
For each input document D, the model firstly produces the prediction for the first level of the ICD codes’ first three
characters (i.e. normalized codes). The predicted output of the first level “normalization” is embedded into a
vector with the projection size p. The vector is then concatenated with each label-specific vector of the second
level of the “raw” ICD codes before being fed into the feed-forward network to produce the final prediction. The
model is trained by minimizing the sum of the binary cross-entropy losses of the “normalization” and “raw” levels.