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Exclusive, integrated solutions crafted by our experts just for your business. Unlock new possibilities and drive efficiency with our tailored AI Integration Process.

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To Fine-Tune the most powerful Large Language Models.

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Schedule your 45-minute AI Workshop session, led by Sonatafy Technology’s lead AI Engineer Dr. Antonio Tamayo. We will take a deep dive into the intricacies of our customized artificial intelligence development and discover practical ways to integrate it into your business operations.

We will discuss actionable use cases, provide real-time demonstrations and code, and together strategize your organization’s current operations and suggested AI solutions.

AI Custom Resource Directory

Healthcare & Life Science ✨

Developed By Sonatafy Technology
AI Technical Breakdown

AI Custom Resource Directory

Talent Acquisition & Candidate CV Analysis ✨

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AI Custom Resource Directory

Social Media Sentiment Analysis ✨

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AI Custom Resource Directory

Invoice
Analyzer ✨

Developed By Sonatafy Technology

Advanced Solutions ✨

To Common AI Challenges

Our team specializes in guiding organizations through their AI journey, addressing these challenges head-on. We offer expertise in simplifying complex AI concepts, sourcing top-tier talent, ensuring data readiness, crafting strategic roadmaps, and defining clear success metrics.

Partnering with us means transforming your AI ambitions into achievable, impactful realities. Let us help you navigate the intricacies of AI implementation with confidence and precision, ensuring your investment translates into real-world success.

Complexity and Understanding

AI Technology, while promising, encompasses intricate systems that require a deep understanding to leverage effectively.

We’ve Solved That.

Strategic Alignment

Developing a coherent strategy that aligns with business goals is essential. Without it, AI projects risk becoming unfocused and ineffective.

We Have The Experience.

Return on Investment

Demonstrating the tangible value of AI investments remains a challenge for many, requiring clear metrics and outcomes.

We Have The Data.

Expertise Availability

The demand for skilled AI professionals far exceeds the supply, making it difficult for companies to secure the talent necessary for their projects.

We Have The Talent.

Data Quality and Quantity

Successful AI initiatives rely on the availability of high-quality, comprehensive data sets. Without this, projects struggle to achieve their full potential.

We’ve Compiled That.

Scalability and Integration

As organizations grow, their AI initiatives must adapt without compromising performance or efficiency.

We Have The Solution.

Our Team’s Verified AI Certifications

AWS Fundamentals: Addressing Security Risk

AWS Academy Graduate – AWS Academy Cloud Foundations

AWS Fundamentals: Going Cloud-Native, Coursera

Sentiment Analysis with Deep Learning using BERT, Coursera

Google Cloud Platform Fundamentals: Core Infrastructure, Coursera

Google Cloud Platform Big Data and Machine Learning Fundamentals, Coursera

Deep Learning with TensorFlow, Cognitive Class

Deep Learning Fundamentals, Cognitive Class

Machine Learning with Python, Cognitive Class

Statistics 101, Cognitive Class

Machine Learning
Dimensionality Reduction, Cognitive Class

Data Analysis with Python, Cognitive Class

How Does The Tool Actually Work?

Healthcare 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 ✨

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.

Led By Leaders
Of AI Engineering

We are excited to share our newest AI service offering, which is specifically designed to help clients rapidly accelerate their AI initiatives, enabling them to stay ahead in the highly competitive tech landscape. Whether you’re starting from scratch or looking to enhance your existing AI capabilities, our expert team is here to guide you through every step of the process.

Our AI development team is led by Dr. Antonio Tamayo, who has a Ph.D. in Computer Science and is an AI and Data Scientist leading expert.

Powerfully Engineered.
Committed To Excellence ⚡︎

What sets Sonatafy Technology apart is our commitment to delivering measurable progress. Our approach is hands-on and results-oriented, focusing on creating AI solutions that drive real business outcomes. Whether it’s streamlining operations, enhancing customer experiences, or unlocking new growth opportunities, our goal is to empower your organization to achieve its full AI potential.

If you’re looking to kickstart your AI journey or take your current efforts to the next level, let’s connect. Discover how Sonatafy Technology can help you turn AI ambitions into tangible successes.

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