A Step by Step Guide to Build an Interactive Health Data Monitoring Tool Using Hugging Face Transformers and Open Source Model Bio_ClinicalBERT

In this tutorial, we will learn how to build an interactive health data monitoring tool using Hugging Face’s transformer models, Google Colab, and ipywidgets. We walk you through setting up your Colab environment, loading a clinical model (like Bio_ClinicalBERT), and creating a user-friendly interface that accepts health data input and returns interpretable disease predictions. This step-by-step guide highlights the capabilities of advanced NLP models in healthcare and makes these powerful tools accessible, even for those new to machine learning and interactive programming.

!pip install transformers torch ipywidgets

First, we install three essential libraries: transformers for working with state-of-the-art NLP models, torch for deep learning computations, and ipywidgets for creating interactive widgets within Colab.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import ipywidgets as widgets
from IPython.display import display, clear_output

Now we import essential modules: it brings in classes and functions from the Hugging Face Transformers library for model handling and text classification. We also import ipywidgets and IPython display functions to create and manage interactive outputs within Google Colab.

# Using a publicly available clinical model
model_name = "emilyalsentzer/Bio_ClinicalBERT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
health_monitor = pipeline("text-classification", model=model, tokenizer=tokenizer)

We load a publicly available clinical model, “emilyalsentzer/Bio_ClinicalBERT,” along with its tokenizer, and set up a text classification pipeline named health_monitor for processing and analyzing clinical health data.

# Broad Disease Mapping
broad_disease_mapping = {
    "LABEL_0": "No significant condition",
    "LABEL_1": "Cardiovascular Diseases (e.g., hypertension, heart disease)",
    "LABEL_2": "Metabolic and Endocrine Disorders (e.g., diabetes, thyroid issues)",
    "LABEL_3": "Respiratory Diseases (e.g., asthma, COPD)",
    "LABEL_4": "Neurological Conditions (e.g., stroke, epilepsy)",
    "LABEL_5": "Infectious Diseases (e.g., influenza, COVID-19)",
    "LABEL_6": "Oncological Conditions (e.g., cancers)",
    "LABEL_7": "Gastrointestinal Disorders (e.g., IBS, Crohn’s disease)",
    "LABEL_8": "Musculoskeletal Disorders (e.g., arthritis, osteoporosis)",
    "LABEL_9": "Immunological/Autoimmune Disorders (e.g., lupus, rheumatoid arthritis)"
}

We create a dictionary that maps the model’s generic output labels (like “LABEL_0”) to specific, broad disease categories. It helps translate the model’s predictions into meaningful clinical interpretations, covering conditions from cardiovascular diseases to autoimmune disorders.

# Function to Analyze Health Data
def analyze_health_data(input_text):
    prediction = health_monitor(input_text)[0]
    disease_prediction = broad_disease_mapping.get(prediction["label"], "Unknown Condition")
    output_str = (
        f"Raw Model Output: {prediction}n"
        f"Interpreted Prediction: {disease_prediction}n"
        f"Confidence Score: {prediction['score']*100:.2f}%"
    )
    return output_str

Above function analyze_health_data, takes clinical text as input, and processes it using the health_monitor pipeline. It retrieves the model’s prediction, then maps the generic label (like “LABEL_0”) to a specific disease category from the broad_disease_mapping dictionary. Finally, it formats the raw prediction, the interpreted disease category, and the confidence score into a readable string before returning it.

# Interactive Interface Using ipywidgets
input_text = widgets.Textarea(
    value="Enter patient health data here...",
    placeholder="Type the clinical notes or patient report",
    description='Health Data:',
    disabled=False,
    layout=widgets.Layout(width="100%", height="100px")
)

We create an interactive text area widget using ipywidgets. It provides a pre-populated prompt, a placeholder for guidance, and a specified layout, allowing users to input clinical notes or patient reports in a user-friendly interface.

# Button widget to trigger the analysis
analyze_button = widgets.Button(
    description='Analyze',
    disabled=False,
    button_style="",  # Options: 'success', 'info', 'warning', 'danger' or ''
    tooltip='Click to analyze the health data',
    icon='check'
)

Then we create a button widget using ipywidgets. The button is labeled “Analyze” and includes a tooltip (“Click to analyze the health data”) and an icon (“check”) to enhance user experience. This button will trigger the health data analysis function when clicked, allowing the model to process the input clinical text.

# Output widget to display the results
output_area = widgets.Output()


def on_analyze_button_clicked(b):
    with output_area:
        clear_output()  
        input_data = input_text.value
        result = analyze_health_data(input_data)
        print(result)


analyze_button.on_click(on_analyze_button_clicked)


display(input_text, analyze_button, output_area)

Finally, we create an output widget to display the analysis results and define a callback function (on_analyze_button_clicked) triggered when the “Analyze” button is clicked. The function clears any previous output, retrieves the input data from the text area, processes it using the analyze_health_data function, and prints the result in the output area. Finally, the button’s click event is linked to this function, and all widgets (input area, button, and output display) are rendered together for interactive use.

Sample Input and Output

In conclusion, this tutorial has shown how to seamlessly integrate state-of-the-art NLP tools with an interactive interface to analyse clinical health data. You can create a system that interprets and categorises health information into actionable insights by leveraging Hugging Face’s pre-trained models and the simplicity of Google Colab and ipywidgets.

Here is the Colab Notebook. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 80k+ ML SubReddit.

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