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With Sonatafy’s AI Engineers

Welcome to our AI Healthcare Demo, the intersection of AI, ML, NLP, and science. Our AI Featured Demo below uses a cutting-edge Natural Language Model to detect ADEs and Diseases with remarkable accuracy, showcasing the future of patient care and safety. View Our Technical Breakdown.

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Built By Leveraging

To Fine-Tune BERT For Precise Sequence Labeling.

The Power Of AI:
Why We’ve Developed AI-Driven Tools

We have developed a series of demonstrations to showcase our expertise and capabilities in creating custom Generative AI models. These demos highlight the integration of artificial intelligence, machine learning, natural language processing, and scientific innovation. Dive into our demos and detailed technical breakdown to understand the mechanics behind our cutting-edge solutions. Start by selecting your Demo below:

Rapid Adverse Drug Events (ADEs) Detection Tool

This demo is trained for the Named Entity Recognition task (NER), NER helps by scanning the text and highlighting the names of drugs (like “clozapine” or “lisinopril”) and any words that describe bad reactions people might have to those drugs (like “agranulocytosis” or “dry cough”).

So, when you use this demo that uses NER for detecting adverse drug effects, you’re essentially uploading a medical text, and the app quickly sifts through it, finding and pointing out the names of medicines and any potential side effects mentioned.

It’s like having a helpful assistant that can instantly pick out the important details about medicines and their possible drawbacks from a bunch of text! Try It Out!

Diseases & Symptoms Detection Tool

Discover the power of our Disease Detection Tool! Leveraging state-of-the-art NLP, specifically Named Entity Recognition (NER), our tool swiftly identifies disease names within medical texts. Whether it’s diabetes or a rare condition like amyotrophic lateral sclerosis (ALS), our tool ensures no disease goes unnoticed.

Simply input your text, and our tool highlights diseases with precision, streamlining research and comprehension of medical literature.

With unparalleled efficiency, it acts as your trusted assistant, empowering informed decision-making and driving advancements in healthcare with confidence. Try It Out!

Symptoms To Diagnosis Tool

This demo can read an input text, identify the symptoms and give a diagnosis associated with one of the following 22 diseases: arthritis, drug reaction, typhoid, varicose veins, gastroesophageal reflux disease, chicken pox, impetigo, fungal infection, pneumonia, dengue, bronchial asthma, allergy, migraine, psoriasis, peptic ulcer disease, hypertension, urinary tract infection, jaundice, common cold, malaria, cervical spondylosis, diabetes. This is a sophisticated large language model trained with more than 1000 clinical documents for the sequence classification task with 22 classes (diseases). Fine tuning with BERT with a careful hyperparameter tuning was carried out to achieve an accuracy of 94 %. Try It Out!

Sonatafy Healthcare Assistant

At Sonatafy, we’ve created an advanced healthcare assistant using a cutting-edge language model based on the Llama large language model (LLM). Our model excels in natural language processing and provides medical insights similar to a doctor’s. It understands medical terminology, clinical concepts, and the relationships between symptoms, histories, and diagnoses. It can analyze complex patient descriptions, connect relevant details, and offer potential diagnoses, diagnostic tests, and treatment recommendations in a human-like conversational style. Our healthcare assistant aims to support healthcare professionals and patients by enhancing medical decision-making and understanding tailored to individual cases. Try It Out!

Abstractive Clinical Document Summarization Tool

Abstractive text summarization generates concise summaries by understanding and rephrasing the essence of the input text. In healthcare, it condenses complex medical records into clear summaries, aiding efficient information sharing and decision-making. At Sonatafy, we’ve developed an advanced model using the Llama large language model (LLM) to create coherent clinical summaries, ensuring critical details are captured in a human-readable format. Our model comprehends complex clinical texts, identifies important information, and generates coherent summaries that capture the essence of the input while maintaining crucial medical details, ensuring the summaries are coherent and human-readable narratives tailored to the clinical domain. Try It Out!

Electronic Health Record (EHR) Analyzer

Our AI demo leverages cutting-edge advancements in Natural Language Processing (NLP), Large Language Models (LLMs), and Generative AI to extract and present information from Electronic Health Records (EHRs) in PDF format. When you use this demo, you’re essentially uploading an EHR in PDF format, and the system quickly processes it, delivering results in fractions of a second.

Key features include a user-friendly PDF upload interface that supports various EHR formats and structures, ensuring compatibility with a wide range of healthcare providers. The system utilizes state-of-the-art NLP techniques to parse and understand medical terminologies and structures within the EHR. Advanced LLMs are employed to interpret the context and extract relevant information, such as patient demographics, medical history, diagnoses, medications, lab results, and treatment plans.

Generative AI models are applied to identify and extract key information from unstructured data in the EHR, ensuring high accuracy and relevancy by leveraging the latest generative algorithms to understand the nuances of medical language. Optimized for speed, the system processes and extracts information in fractions of a second, achieving near-instantaneous results and enhancing the efficiency of clinical workflows.

The extracted information is presented in a structured, easy-to-read format, including visual aids such as charts and graphs to represent lab results and trends over time. This allows for quick access to critical patient information, improving decision-making processes.

It’s like having a helpful assistant that can instantly pick out the important details from a complex EHR! Try it out!

How Does The Tool Actually Work?

Our Technical Breakdown ✨

Information extraction plays a crucial role in the clinical domain by enabling the automated extraction of relevant medical information from unstructured text sources such as electronic health records (EHRs), clinical notes, research articles, and drug labels. In this context, information extraction techniques facilitate the identification and extraction of key entities, relationships, and events pertaining to patient diagnoses, treatments, medications, adverse reactions, and other clinically relevant information. By automating the extraction process, healthcare providers can streamline clinical workflows, enhance decision-making processes, improve patient care outcomes, and facilitate medical research and analysis on a large scale.

These demos are trained to tackle the problem of information extraction in the medical field as a sequence labeling one, which is a fundamental task in natural language processing (NLP) that involves assigning labels to individual tokens in a sequence of text. To do this task, usually named entity recognition (NER), the BIO (Begin, Inside, Outside) schema has been used to annotate entities such as drugs and adverse effects within clinical text. Under this schema, each token in the input text is labeled with a tag indicating whether it represents the beginning (B), inside (I), or outside (O) of a named entity. This structured labeling scheme allows for the accurate identification and delineation of multi-token entities, which is essential for downstream tasks such as information retrieval, question answering, and clinical decision support systems.

Fine-tuning with the pre-trained language models called BERT (Bidirectional Encoder Representations from Transformers) for named entity recognition (NER) was carried out for these demos. It’s a powerful approach to address the task of identifying drugs mentions and their adverse effects as well as diseases in clinical text. By leveraging large-scale pre-trained language representations, fine-tuning enables the model to learn domain-specific patterns and semantic representations from annotated clinical data. This fine-tuning process involves adjusting the parameters of the pre-trained model on a task-specific dataset, thereby tailoring the model to the nuances of the clinical domain and improving its performance on NER tasks. The ability to accurately identify entities such as drugs and their adverse effects as well as diseases from vast amounts of clinical text is paramount for various applications in healthcare, including pharmacovigilance, drug safety monitoring, adverse event detection, and personalized medicine. Our model offers a scalable and efficient solution to this challenge, enabling healthcare organizations to analyze large volumes of clinical text data and extract actionable insights to support clinical decision-making and improve patient outcomes.

This demonstration showcases a streamlined process wherein a given input text, sourced from the clinical domain such as an Electronic Health Record (EHR), undergoes rapid analysis to identify instances of drug mentions and their associated adverse effects as well as diseases within a matter of seconds. Leveraging advanced natural language processing (NLP) techniques and state-of-the-art models trained specifically for the clinical domain, the system swiftly parses through the input text, recognizing and categorizing drug entities, adverse effects or diseases. By harnessing the power of machine learning algorithms and pre-trained language models fine-tuned for named entity recognition (NER) tasks, these demos exemplify the capability to automate and expedite the extraction of vital medical information from unstructured clinical text. This accelerated process not only enhances the efficiency of clinical data analysis and decision-making but also empowers healthcare professionals with timely insights into medication-related aspects of patient care, ultimately contributing to improved patient outcomes and safety.

Information Extraction

as a Sequence Labeling Task in NLP

Named Entity Recognition (NER)

for Clinical Text

BIO Schema for NER

Begin | Marks the beginning of a named entity.
Inside | Continues the named entity recognition.
Outside | No named entity present.

Input Text Sequence

“Patient takes Aspirin”
Labels: [O, O, B-Drug]

Applications of NER in Healthcare

Information Retrieval | Question-Answer Resource | Clinical Decision Support Systems

How Does The Tool Actually Work?

LLMOps Pipeline ✨

The Large Language Model pipeline involves three steps as follows:

Data preprocessing
To process data from natural language to the BIO scheme we used a custom script in Python. This process takes each entity in the training datasets together with the character where they start and search in order of appearance in the text to assign the corresponding label in each case (Drug, Adverse Effect or Disease). Additionally, we implemented a simple sentence tokenization with the period (“.”) token.

Fine-tuning
The fine-tuning process for both tools was carried out in two steps, as follows: we fine-tuned one of the most powerful Large Language Models (BERT). A virtual machine with a GPU Tesla T4 with 27.3 gigabytes of available RAM was used during the training process.

Post-processing
As a post-processing we treated the subword tokenization inherent to the model used. Additionally, when the models extract two or more entities separately, but they are contiguous, we group them into a unique entity.

Healthcare & The

Benefits Of
Artificial Intelligence ✨

Try Our AI Detection Tool

Predictive Analytics

AI can analyze large amounts of data to predict disease outbreaks, identify at-risk patients, and suggest preventive measures. This proactive approach can lead to improved patient outcomes and reduced hospital readmissions.

Drug Development

AI can analyze large quantities of data in a process less prone to error and with no burnout risk.

Personalized Medicine

AI enables personalized treatment plans by analyzing genetic, lifestyle, and environmental data, improving treatment effectiveness and patient satisfaction.

Cost Reduction

AI can automate certain tasks and processes, helping streamline operations and improve efficiency.

Medical Research

AI can accelerate medical research by identifying potential antidotes and treatment options, enabling rapid response to complex global health challenges.

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Time Efficiency

AI can improve the efficiency of medical tasks, such as imaging scans, thereby reducing the waiting time.

Medical Image Analysis

AI can reveal images of the internal aspects of a body through a noninvasive process of imaging, which helps in diagnosing and treating disease.

Fraud Detection

AI can analyze financial and claims data, helping healthcare organizations maintain regulatory compliance and allocate resources more efficiently to benefit patient care.

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Meet Our Developers

Review real engineer CVs of current and past Sonatafy Technology nearshore developers. We have a wide range of different positions and skills thanks to our talented engineers. Learn More.