Automate Medical Coding Using BERT

Target audience: Beginner
Estimated reading time: 5'

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Transformers and self-attention models are increasingly taking center stage in the NLP toolkit of data scientists [ref 1]. This article delves into the design, deployment, and assessment of a specialized transformer tasked with extracting medical codes from Electronic Health Records (EHR) [ref 2]. The focus is on curbing development and training expenses while ensuring the model remains current.

Table of contents

Introduction

Challenges

       Extracting medical codes

       Minimizing costs

       Keeping models up-to-date

Architecture

Tokenizer

BERT encoder

       Context embedding

       Segmentation

       Transformer

       Self-attention

Classifier

Active learning

References

Glossary

Important notes: 

• This piece doesn't serve as a primer or detailed account of transformer-based encoders,  Bidirectional Encoder Representations from Transformers (BERT), multi-label classification or active learning. Detailed and technical information on these models is available in the References section. [ref 1, 3, 8, 12]. 
• The terms medical document, medical note and clinical notes are used interchangeably
• Some functionalities discussed here are protected intellectual property, hence the omission of source code.

Introduction

Autonomous medical coding refers to the use of artificial intelligence (AI) and machine learning (ML) technologies to automatically assign medical codes to patient records [ref 4]. Medical coding is the process of assigning standardized codes to diagnoses, medical procedures, and services provided during a patient's visit to a healthcare facility. These codes are used for billing, reimbursement, and research purposes.

By automating the medical coding process, healthcare organizations can improve efficiency, accuracy, and consistency, while also reducing costs associated with manual coding.

 

A health insurance claim is an indication of the service given by a provider, even though the medical records associated with this service can greatly vary in content and structure. It's crucial to precisely extract medical codes from clinical notes since outcomes, like hospitalizations, treatments, or procedures, are directly tied to these diagnostic codes. Even if there are minor variations in the codes, claims can still be valid for specific services, provided the clinical notes, patient history, diagnosis, and advised procedures align.

fig. 1 Extraction of knowledge, predictions from electronic medical records 

Medical coding is the transformation of healthcare diagnosis, procedures, medical services described in electronic health records, physician's notes or laboratory results into alphanumeric codes.  This study focuses on automated generation of medical codes and health insurance claims from a given clinical note or electronic health record.

Challenges

There are 3 issues to address:

1. How to extract medical codes reliably, given that labeling of medical codes is error prone and the clinical documents are very inconsistent?
2. How to minimize the cost of self- training complex deep models such as transformers while preserving an acceptable accuracy?
3. How to continuously keep models up to date in production environment?

Extracting medical codes

Medical codes are derived from patient records and clinical notes to forecast procedural results, determine the length of hospital stays, or generate insurance claims. The most prevalent medical coding systems include:

• International Classification of Diseases (ICD-10) for diagnosis (with roughly 72,000 codes)
• Current Procedural Terminology (CPT) for procedures and medications (encompassing around 19,000 codes)
• Along with others like Modifiers, SNOMED, and so forth.

The vast array of medical codes poses significant challenges in extraction due to:

• The seemingly endless combinations of codes linked to a specific medical document
• Varied and inconsistent formats of patient records (in terms of terminology, structure, and length.
• Complications in gleaning context from medical information systems.

Minimizing costs

A study on deep learning models suggests that training a significant language model (LLM) results in the emission of 626,155 pounds of CO2, comparable to the total emissions from five vehicles over their lifespan.

To illustrate, GPT-3/ChatGPT underwent training on 500 billion words with a model size of 175 billion parameters. A single training session would require 355 GPU-years and bear a cost of no less than $4.6M. Efforts are currently being made to fine-tune resource utilization for the development of upcoming models [ref 5].

Keeping models up-to-date

Customer data in real-time is continuously changing, often deviating from the distribution patterns the models were originally trained on (due to concept and covariate shifts).

This challenge is particularly pronounced for transformers that need task-specific fine-tuning and might even necessitate restarting the pre-training process — both of which are resource-intensive actions.

Architecture

To tackle the challenges highlighted earlier, the proposed solution should encompass four essential AI/NLP elements:

• Tokenizer to extract tokens, segments & vocabulary from a corpus of medical documents.
• Bidirectional Encoder Representations from Transformers (BERT) to generate a representation (embedding) of the documents [ref 3].
• Neural-based classifier to predict a set of diagnostic codes or insurance claim given the embeddings.
• Active/transfer learning framework to update model through optimized selection/sampling of training data from production environment.

From a software engineering perspective, the system architecture should provide a modular integration capability with current IT infrastructures. It also requires an asynchronous messaging system with streaming capabilities, such as Kafka, and REST API endpoints to facilitate testing and seamless production deployment.

fig. 2  Architecture for integration of AI components with external medical IT systems 

Tokenizer 

The effectiveness of a transformer encoder's output hinges on the quality of its input: tokens and segments or sentences derived from clinical documents. Several pressing questions need addressing:

1. Which vocabulary is most suitable for token extraction from these notes? Do we consider domain-specific terms, abbreviations, Tf-Idf scores, etc.?
2. What's the best approach to segmenting a note into coherent units, such as sections or sentences?
3. How do we incorporate or embed pertinent contextual data about the patient or provider into the encoder?

Tokens play a pivotal role in formulating a dynamic vocabulary. This vocabulary can be enriched by incorporating words or N-grams from various sources like:

• Terminology from the American Medical Association (AMA)
• Common medical terms with high TF-IDF scores
• Different senses of words
• Abbreviations
• Semantic descriptions
• Stems
• .....

fig. 3 Generation of a vocabulary using training corpus and knowledge base

Our optimal approach is based on utilizing uncased words from the American Medical Association, coupled with the top 85% of terms derived from training medical notes, ranked by their highest TF-IDF scores. It's worth noting that this method can be resource-intensive.

BERT encoder

In NLP, words and documents are represented in the form of numeric vectors allowing similar words to have similar vector representations [ref 6].

The objective is to generate embeddings for medical documents including contextual data to be feed into a deep learning classifier to extract diagnostic codes or generate a medical insurance claim [ref 7].

Context embedding 

Contextual information such as patient data (age, gender,...), medical service provider, specialty, or location is categorized (or bucked for continuous values) and added to the tokens extracted from the medical note. 

Segmentation

Structuring electronic health records into logical or random groups of segments/sentences presents a significant challenge. Segmentation involves dividing a medical document into segments (or sections), each with an equal number of tokens that consist of sentences and relevant contextual data.

Several methods can be employed to segment a document:

1. Isolating the contextual data as a standalone segment.
2. Integrating the contextual data into the document's initial segment.
3. Embedding the contextual data into any arbitrarily chosen segment [Ref 6].

fig. 4 Embedding of medical note with contextual data using 2 segments

Our study show the option 2 provides the best embedding for the feed forward neural network classifier.

Interestingly, treating the entire note as a single sentence and using the AMA vocabulary leads to diminished accuracy in subsequent classification tasks.

Transformer

We employ the self-supervised Bidirectional Representation for Transformer (BERT) with the objectives to:

• Grasp the contextual significance of medical phrases.
• Create embeddings/representations that merge clinical notes with contextual data.

The model construction involves two phases:

1. Pretraining on an extensive, domain-specific corpus [ref 8].
2. Fine-tuning tailored for specific tasks, like classification [ref 9].

After the pretraining phase concludes, the document embedding is introduced to the classifier training. This can be sourced:

1. Directly from the output of the pretrained model (document embeddings).
2. During the fine-tuning process of the pretrained model. Concurrently, fine-tuning operates alongside active learning for model updates."\

fig. 5 Model weights update with features extraction vs fine tuning

It's strongly advised to utilize one of the pretrained BERT models like ClinicalBERT [ref 10] or GatorTron [ref 11], and then adapt the transformer for classification purposes. However, for this particular project, we initiated BERT's pretraining on a distinct set of clinical notes to gauge the influence of vocabulary and segmentation on prediction accuracy.

Self-attention

Here's a concise overview of the multi-head self-attention model for context:
The foundation of a transformer module is the self-attention block that processes token, position, and type embeddings prior to normalization. Multiple such modules are layered together to construct the encoder. A similar architecture is employed for the decoder.

fig. 6 Schematic for transformer encoder block

Classifier

The classifier is structured as a straightforward feed-forward neural network (fully connected), since a more intricate design might not considerably enhance prediction accuracy. In addition to the standard hyper-parameter optimization, different network configurations were assessed.
The network's structure, including the number and dimensions of hidden layers, doesn't have a significant influence on the overall predictive performance.

Active learning

The goal is to modify models to tackle the issue of covariate shifts observed in the distribution of real-time/production data during inference.

The dual-faceted approach involves:

1. Selecting data samples with labels that deviate from the distribution initially employed during training (Active learning) [ref 12].
2. Adjusting the transformer for the classification objective using these samples (Transfer learning)

A significant obstacle in predicting diagnostic codes or medical claims is the steep labeling expense. In this context, learning algorithms can proactively seek labels from domain experts. This iterative form of supervised learning is known as active learning.

Because the learning algorithm selectively picks the examples, the quantity of samples needed to grasp a concept is frequently less than that required in traditional supervised learning. In this aspect, active learning parallels optimal experimental design, a standard approach in data analysis [ref 13].

fig. 6 Simplified data pipeline for active learning.

In our scenario, the active learning algorithm picks an unlabeled medical note, termed note-91, and sends it to a human coder who assigns it the diagnostic code S31.623A. Once a substantial number of notes are newly labeled, the model undergoes retraining. Subsequently, the updated model is rolled out and utilized to forecast diagnostic codes on notes in production.

Thank you for reading this article. For more information ...

References

[1] Attention is all you need

[2] What is medical coding?

[3] Towards data science: BERT Explained: State of the art language model for NLP

[4] Automated clinical coding: what, why, and where we are?

[5] Mitigating the Water Consumption and Carbon Emissions of ChatGPT

[6] Towards data science: What Is Embedding and What Can You Do with It

[7] A Guide on Word Embeddings in NLP

[8] Hugging face: State Of The Art NLP Model Explained
[9] What Is Fine-Tuning and How Does It Work in Neural Networks?

[10] ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
[11] A Large Language Model for Electronic Health Records
[12] Towards data science: Active Learning in Machine Learning

[13] Active learning strategies

A formal presentation of this project is available at

Autonomous Medical Coding with Discriminative Transformers

Glossary

• Electronic health record (EHR):  An Electronic version of a patients medical history, that is maintained by the provider over time, and may include all of the key administrative clinical data relevant to that persons care under a particular provider, including demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports.
• Medical document: Any medical artifact related to the health of a patient. Clinical note, X-rays, lab analysis results,...
• Clinical note: Medical document written by physicians following a visit. This is a textual description of the visit, focusing on vital signs, diagnostic, recommendation and follow-up.
• ICD (International Classification of Diseases):  Diagnostic codes that serve a broad range of uses globally and provides critical knowledge on the extent, causes and consequences of human disease and death worldwide via data that is reported and coded with the ICD. Clinical terms coded with ICD are the main basis for health recording and statistics on disease in primary, secondary and tertiary care, as well as on cause of death certificates
• CPT (Current Procedural Terminology):  Codes that offer health care professionals a uniform language for coding medical services and procedures to streamline reporting, increase accuracy and efficiency. CPT codes are also used for administrative management purposes such as claims processing and developing guidelines for medical care review.

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Patrick Nicolas has over 25 years of experience in software and data engineering, architecture design and end-to-end deployment and support with extensive knowledge in machine learning. 
He has been director of data engineering at Aideo Technologies since 2017 and he is the author of "Scala for Machine Learning" Packt Publishing ISBN 978-1-78712-238-3

Compare Python, NumPy and PyTorch Performance

Target audience: Beginner
Estimated reading time: 4'

Posts history

Recently, I embarked on a healthcare project that involved extracting diagnostic information from Electronic Health Records. While fine-tuning a BERT model, I noticed some atypical latency behaviors. This prompted me to conduct a performance comparison between Python lists, NumPy arrays, and PyTorch tensors.

The implementation relies on a timer decorator to collect latency values.

Table of contents

Introduction

Timing with decorator

Benchmark implementation

Evaluation

References

Appendix

Notes: 

• The implementation uses Python 3.11
• To enhance the readability of the algorithm implementations, we have omitted non-essential code elements like error checking, comments, exceptions, validation of class and method arguments, scoping qualifiers, and import statements.

Introduction

I assume that most readers are familiar with the various Python, NumPy and PyTorch containers used in this article. But just in case, here is a quick refresh:

Python list: A list in Python is similar to an array in C, C++, Java or Scala except that its elements can have different types.

Python arrays: Array is a container which can hold a fix number of items or elements. Contrary to lists, items of an array should be of the same type. Most of the data structures make use of arrays to implement their algorithms [ref 1].

NumPy arrays: A numPy array represents a multidimensional, homogeneous array of fixed-size items. It is implemented as a static buffer of contiguous values of identical types which index can be dynamically modified to generate matrix, tensor or higher dimension numerical structures [ref 2].

PyTorch tensors: Similarly to numpy array, PyTorch tensors are multi-dimensional arrays containing elements of a single data type. The tensors share the same semantic and operators as NumPy arrays but also support automatic differentiation and support GPU/Cuda math libraries [ref 3].

Timing with decorator

Decorators are very powerful tools in Python since it allows programmers to modify the behavior of a function, method or even a class. Decorators wrap another function in order to extend the behavior of the wrapped function, without permanently modifying it [ref 4].

def timeit(func):
    ''' Decorator for timing execution of methods'''

    def wrapper(*args, **kwargs):
        start = time.time()
        func(*args, **kwargs)
        duration = '{:.3f}'.format(time.time() - start)
        logging.info(f'{args[1]}:{args[3]}\t{duration} secs.')
        return 0

    return wrapper

Benchmark implementation

The objective is to automate the comparison of the various framework and functions by creating a wrapper EvalFunction class.

The evaluation class has two arguments:

• Descriptive name of the function, func_name used to evaluate the data structures
• The signature of the function , func used to evaluate the data structures

import array as ar
import time

import numpy as np
from random import Random
from typing import List, AnyStr, Callable, Any, NoReturn
import math
import torch
from dataclasses import dataclass

import logging
from matplotlib import pyplot as plt


collector = {}

@dataclass
class EvalFunction:
    """
     Data class for evaluation of Python lists, Array, Numpy array and torch tensor
     :param func_name Description of the function to execute
     :param func Lambda to be executed
    """
   func_name: AnyStr
   func: Callable[[Any], float]

   

   def compare(self, input_list: List[float], fraction: float = 0.0) -> NoReturn:

       input_max: int = \
               math.floor(len(input_list)*fraction) if 0.0 < fraction <= 1.0 \
               else len(input_list)

       input_data = input_list[:input_max]

       # Execute lambda through Python list
       self.__execute('python', input_data, 'list:      ')

       # Execute lambda through Python array
       input_array = ar.array('d', input_data)
       self.__execute('python', input_array, 'array:      ')

       # Execute lambda through numpy array
       np_input = np.array(input_list, dtype=np.float32)
       self.__execute('python', np_input, 'lambda: ')

       # Execute native numpy methods
       self.__execute('numpy', np_input, 'native:   ')

       # Execute PyTorch method on CPU
       tensor = torch.tensor(np_input, dtype=torch.float32, device='cpu')
       self.__execute('pytorch', tensor, '(CPU):    ')

       # Execute PyTorch method on GPU
       tensor = torch.tensor(np_input, dtype=torch.float32, device='cuda:0')
       self.__execute('pytorch', tensor, '(CUDA)')

The implementation of the supporting, private method, __execute is described in the Appendix

Evaluation

We've chosen a collection of mathematical transformations that vary in complexity and computational demand to evaluate different frameworks. These transformations involve calculating the mean values produced by the subsequent functions:

\[x_{i}=1+rand{[0, 1]}\]

\[average(x)=\frac{1}{n}\sum_{1}^{n}x_{i}\]

\[sine(x) = average\left ( \sum_{1}^{n}sin\left ( x_{i} \right ) \right )\]

\[sin.exp(x) = average\left ( \sum_{1}^{n}sin\left ( x_{i} \right ) e^{-x_{i}^{2}} \right )\]

\[sin.exp.log(x) = average\left ( \sum_{1}^{n}sin\left ( x_{i} \right ) e^{-x_{i}^{2}} + log(1 + x_{i}))\right )\]

# Functions to evaluate data structures

def average(x) -> float:
    return sum(x)/len(x)
def sine(x) -> float:
    return sum([math.sin(t) for t in x])/len(x)
def sin_exp(x) -> float:
    return sum([math.sin(t)*math.exp(-t) for t in x])/len(x)

# Random value generator
rand = Random(42)
num_values = 500_000_000
my_list: List[float] = [1.0 + rand.uniform(0.0, 0.1)] * num_values

# Fraction of the original data set of 500 million data points

fractions = [0.2, 0.4, 0.6, 0.8, 1.0]

# Evaluate the latency for sub data sets of size , len(my_list)*fraction

for fraction in fractions:
    eval_average = EvalFunction('sin_exp', average)
    eval_average.compare(my_list, fraction)

# x-axis values as size=  len(my_list)*fraction
data_sizes = [math.floor(num_values*fraction) for fraction in fractions]

# Invoke the plotting methodplotter = Plotter(data_sizes, collector)
plotter.plot('Sin*exp 500M')

We conducted the test on an AWS EC2 instance of type p3.2xlarge, equipped with 8 virtual cores, 64GB of memory, and an Nvidia V100 GPU. A basic method for plotting the results is provided in the appendix.

Study 1
We compared the computation time required to determine the {x} -> average{x}  of 500 million real numbers within a Python list, array, NumPy array, and PyTorch tensor.

We compared the computation time required to apply the {x} -> sin{x}.exp{-x} function to 500 million real numbers within a Python list, array, NumPy array, and PyTorch tensor.

Conclusion

• The performance difference between executing on the GPU versus the CPU becomes more pronounced as the dataset size grows.
• Predictably, the runtime for both the 'average' and 'sin_exp' functions scales linearly with the size of the dataset when using Python lists or arrays.
• When executed on the CPU, PyTorch tensors show a 20% performance improvement over NumPy arrays.

Study 2

Le't compare the relative performance of GPU and GPU during the processing of a large PyTorch tensor.

Conclusion

The size of dataset has a very limited impact on the performance of processing PyTorch tensor on GPU while the execution time increases linearly on CPU.

Thank you for reading this article. For more information ...

References

[1] Python arrays
[2] NumPy array
[3] PyTorch tensors
[4] Primer on Python decorators

Appendix

The __execute method take two arguments used in the structural pattern match:

• The framework used to identify the
• The input data to be processed

The private method __numpy_func applies each the functions (average, sine,...) to a NumPy array, np_array generated from the original list.

The method, __pytorch_func applies each function to a torch tensor derived from np_array.

def __execute(self, framework: AnyStr, input: Any) -> float:
    match framework:
        case 'python':
           return self.func(input)
        case 'numpy':
           return self.__numpy_func(input)
        case 'pytorch':
           return self.__pytorch_func(input)
        case _
           return -1.0

def __numpy_func(self, np_array: np.array) -> float:
   match self.func_name:
      case 'average':
          return np.average(np_array).item()
      case 'sine':
          return np.average(np.sin(np_array)).item()
      case 'sin_exp':
          return np.average(np.sin(np_array)*np.exp(-np_array)).item()

def __pytorch_func(self, tensor: torch.tensor) -> float:
    match self.func_name:
       case 'average':
          return torch.mean(tensor).float()
       case 'sine':
          return torch.mean(torch.sin(tensor)).float()
       case 'sin_exp':
          return torch.mean(torch.sin(tensor) * torch.exp(-tensor)).float()

A simple class, Plotter, to wraps the creation and display of plots using matplotlib.

class Plotter(object):
    markers = ['r--', '^-', '+--', '--', '*-']

    def __init__(self, dataset_sizes: List[int], results_map):
        self.sizes = dataset_sizes
        self.results_map = results_map

    def plot(self, title: AnyStr) -> NoReturn:
        index = 0
        np_sizes = np.array(self.sizes)
        for key, values in self.results_map.items():
            np_values = np.array(values)
            plt.plot(np_sizes, np_values, Plotter.markers[index % len(Plotter.markers)])
            index += 1

        plt.title(title, fontsize=16, fontstyle='italic')
        plt.xlabel('Dataset size', fontsize=13, fontstyle='normal')
        plt.ylabel('time secs', fontsize=13, fontstyle='normal')
        plt.legend(['Python List', 'Python Array', 'Numpy native', 'PyTorch CPU', 'PyTorch GPU'])
        plt.show()

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Patrick Nicolas has over 25 years of experience in software and data engineering, architecture design and end-to-end deployment and support with extensive knowledge in machine learning. 
He has been director of data engineering at Aideo Technologies since 2017 and he is the author of "Scala for Machine Learning" Packt Publishing ISBN 978-1-78712-238-3