How to build healthcare predictive models using PyHealth?

PyHealth is a Python-based toolbox. As the name implies, this toolbox contains a variety of ML models and architecture algorithms for working with medical data and modeling.

Machine learning has been applied to many health-related tasks, such as the development of new medical treatments, the management of patient data and records, and the treatment of chronic diseases. To achieve success in those SOTA applications, we must rely on the time-consuming technique of model building evaluation. To alleviate this load, Yue Zhao et al have proposed a PyHealth, a Python-based toolbox. As the name implies, this toolbox contains a variety of ML models and architecture algorithms for working with medical data. In this article, we will go through this model to understand its working and application. Below are the major points that we are going to discuss in this article.

Table of contents

  1. Machine learning in healthcare
  2. How can PyHealth help in healthcare?
  3. Working of PyHealth
  4. PyHealth for model building

Let’s first discuss the use case of machine learning in the healthcare industry.  

Machine learning in healthcare

Machine learning is being used in a variety of healthcare settings, from case management of common chronic conditions to leveraging patient health data in conjunction with environmental factors such as pollution exposure and weather.

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Machine learning technology can assist healthcare practitioners in developing accurate medication treatments tailored to individual features by crunching enormous amounts of data. The following are some examples of applications that can be addressed in this segment:

Disease detection

The ability to swiftly and properly diagnose diseases is one of the most critical aspects of a successful healthcare organization. In high-need areas like cancer diagnosis and therapy, where hundreds of drugs are now in clinical trials, scientists and computationalists are entering the mix. One method combines cognitive computing with genetic tumour sequencing, while another makes use of machine learning to provide diagnosis and treatment in a range of fields, including oncology.

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Diagnosing using image

Medical imaging, and its ability to provide a complete picture of an illness, is another important aspect in diagnosing an illness. Deep learning is becoming more accessible as data sources become more diverse, and it may be used in the diagnostic process, therefore it is becoming increasingly important. Although these machine learning applications are frequently correct, they have some limitations in that they cannot explain how they came to their conclusions.

Drug discovery

ML has the potential to identify new medications with significant economic benefits for pharmaceutical companies, hospitals, and patients. Some of the world’s largest technology companies, like IBM and Google, have developed ML systems to help patients find new treatment options. Precision medicine is a significant phrase in this area since it entails understanding mechanisms underlying complex disorders and developing alternative therapeutic pathways.

Surgical tools

Because of the high-risk nature of surgeries, we will always need human assistance, but machine learning has proved extremely helpful in the robotic surgery sector. The da Vinci robot, which allows surgeons to operate robotic arms in order to do surgery with great detail and in confined areas, is one of the most popular breakthroughs in the profession. 

These hands are generally more accurate and steady than human hands. There are additional instruments that employ computer vision and machine learning to determine the distances between various body parts so that surgery can be performed properly.

How PyHealth can help in healthcare?

Health data is typically noisy, complicated, and heterogeneous, resulting in a diverse set of healthcare modelling issues. For instance, health risk prediction is based on sequential patient data, disease diagnosis based on medical images, and risk detection based on continuous physiological signals. 

Electroencephalogram (EEG) or electrocardiogram (ECG), for example, and multimodal clinical notes (e.g., text and images). Despite their importance in healthcare research and clinical decision making, the complexity and variability of health data and tasks need the long-overdue development of a specialized ML system for benchmarking predictive health models.

PyHealth is made up of three modules: data preprocessing, predictive modelling, and assessment. Both computer scientists and healthcare data scientists are PyHealth’s target consumers. They can run complicated machine learning processes on healthcare datasets in less than 10 lines of code using PyHealth.

The data preprocessing module converts complicated healthcare datasets such as longitudinal electronic health records, medical pictures, continuous signals (e.g., electrocardiograms), and clinical notes into machine learning-friendly formats. 

The predictive modelling module offers over 30 machine learning models, including known ensemble trees and deep neural network-based approaches, using a uniform yet flexible API geared for both researchers and practitioners. 

The evaluation module includes a number of evaluation methodologies (for example, cross-validation and train-validation-test split) as well as prediction model metrics.

There are five distinct advantages to using PyHealth. For starters, it contains more than 30 cutting-edge predictive health algorithms, including both traditional techniques like XGBoost and more recent deep learning architectures like autoencoders, convolutional based, and adversarial based models. 

Second, PyHealth has a broad scope and includes models for a variety of data types, including sequence, image, physiological signal, and unstructured text data. Third, for clarity and ease of use, PyHealth includes a unified API, detailed documentation, and interactive examples for all algorithms—complex deep learning models can be implemented in less than ten lines of code.

Fourth, unit testing with cross-platform, continuous integration, code coverage, and code maintainability checks are performed on most models in PyHealth. Finally, for efficiency and scalability, parallelization is enabled in select modules (data preprocessing), as well as fast GPU computation for deep learning models via PyTorch.

Working of PyHealth

PyHealth is a Python 3 application that uses NumPy, scipy, scikit-learn, and PyTorch. As shown in the diagram below, PyHealth consists of three major modules: First is the data preprocessing module can validate and convert user input into a format that learning models can understand; 

Second is the predictive modelling module is made up of a collection of models organized by input data type into sequences, images, EEG, and text.  For each data type, a set of dedicated learning models has been implemented, and the third is the evaluation module can automatically infer the task type, such as multi-classification, and conduct a comprehensive evaluation by task type.

Most learning models share the same interface and are inspired by the scikit-API learn to design and general deep learning design: I fit learns the weights and saves the necessary statistics from the train and validation data; load model chooses the model with the best validation accuracy, and inference predicts the incoming test data. 

For quick data and model exploration, the framework includes a library of helper and utility functions (check parameter, label check, and partition estimators). For example, a label check can check the data label and infer the task type, such as binary classification or multi-classification, automatically.

PyHealth for model building

Now below we’ll discuss how we can leverage the API of this framework. First, we need to install the package by using pip. 

! pip install pyhealth

Next, we can load the data from the repository itself. For that, we need to clone the repository. After cloning the repository inside the datasets folder there is a variety of datasets like sequenced based, image-based, etc. We are using the mimic dataset and it is in the zip form we need to unzip it. Below is the snippet clone repository, and unzip the data. 

! git clone
! unzip /content/PyHealth/datasets/

The unzipped file is saved in the current working directory with the name of the folder as a mimic. Next to use this dataset we need to load the sequence data generator function which serves as functionality to prepare the dataset for experimentation. 

from import sequencedata as expdata_generator
# initialize the dataset
# unique id for dataset
expdata_id = ''
cur_dataset = expdata_generator(expdata_id=expdata_id)
cur_dataset.get_exp_data(sel_task='phenotyping', data_root='/content/mimic')

Now we have loaded the dataset. Now we can do further modelling as below.

# load and fit the model
from pyhealth.models.sequence.embedgru import EmbedGRU
# unique id for model
expmodel_id = '2020.0811.model.phenotyping.test.v2'
clf = EmbedGRU(expmodel_id=expmodel_id, n_batchsize=5, use_gpu=False,
# fit model, cur_dataset.valid)

Here is the fitment result.

Final words

Through this article, we have discussed how machine learning can be used in the healthcare industry by observing the various applications. As this domain is being quite vast and N number application, we have discussed a Python-based toolbox that is designed to build a predictive modelling approach by using various deep learning techniques such as LSTM, GRU for sequence data, and CNN for image-based data. 


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