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In this tutorial, we will learn how to build an interactive health data monitoring tool using Hugging Faces 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 ipywidgetsFirst, 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, pipelineimport ipywidgets as widgetsfrom IPython.display import display, clear_outputNow 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 modelmodel_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 Mappingbroad_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, Crohns 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 models generic output labels (like LABEL_0) to specific, broad disease categories. It helps translate the models predictions into meaningful clinical interpretations, covering conditions from cardiovascular diseases to autoimmune disorders.# Function to Analyze Health Datadef 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_strAbove function analyze_health_data, takes clinical text as input, and processes it using the health_monitor pipeline. It retrieves the models 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 ipywidgetsinput_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 analysisanalyze_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 resultsoutput_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 buttons 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 OutputIn 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 Faces pre-trained models and the simplicity of Google Colab and ipywidgets.Here is the Colab Notebook. Also,dont forget to follow us onTwitterand join ourTelegram ChannelandLinkedIn Group. Dont Forget to join our80k+ ML SubReddit. Asif RazzaqWebsite| + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Reka AI Open Sourced Reka Flash 3: A 21B General-Purpose Reasoning Model that was Trained from ScratchAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Coding Implementation of Web Scraping with Firecrawl and AI-Powered Summarization Using Google GeminiAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step by Step Guide to Build a Trend Finder Tool with Python: Web Scraping, NLP (Sentiment Analysis & Topic Modeling), and Word Cloud VisualizationAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Meet Manus: A New AI Agent from China with Deep Research + Operator + Computer Use + Lovable + Memory Parlant: Build Reliable AI Customer Facing Agents with LLMs (Promoted)
