Ivee Software
iveesoftware.com
United States
Veterinary Technology
Datakimia collaborated with IVEE, an innovative veterinary startup, to create a national registry for canine cancer. The primary objective was to centralize data from multiple sources, including medical records, clinic databases, and veterinary notes, into a comprehensive system.
To achieve this, advanced algorithms and machine learning techniques were implemented to automate the identification of dogs diagnosed with cancer, transforming unstructured medical data into structured, actionable insights. This approach not only streamlined data processing but also helped uncover hidden patterns and correlations that were previously difficult to detect.
With this robust, automated system in place, IVEE was able to provide more accurate and reliable data to its clients, enhancing their ability to make informed decisions in veterinary care. The solution also improved overall efficiency in managing the data, allowing IVEE to deliver faster, data-driven insights and contribute to better treatment outcomes for dogs with cancer.
Before Implementation
Managing large volumes of unstructured data from various sources posed a challenge for analysis and identification of patterns.
Data duplication from two APIs caused redundancy, slowing down processing.
Generating and managing reports for the newsletter and PDF extraction was largely manual, resulting in inefficiencies.
Key Improvements
By creating an automated procedure, IVEE was able to identify dogs with cancer based on medical records and veterinarians' notes.
Centralizing data streamlined processing and improved reliability.
The use of machine learning algorithms to detect patterns in the data provided IVEE with deeper insights, enabling better decision-making.
Project Overview
1.
Data Analysis
The team started by understanding IVEE’s existing data structure and processes. This involved analyzing the current data pipeline, identifying areas of improvement, and creating a roadmap for the project.
2.
Architecture design
Datakimia designed the architecture for data integration and machine learning, focusing on creating an automated registry of canine cancer. This included data extraction, transformation, and visualization processes.
3.
Machine Learning
The team implemented machine learning models for identifying patterns in the data. Additionally, automation tools for reporting and newsletters were integrated to reduce manual tasks and enhance operational efficiency.
4.
Optimization
The solution was rigorously tested for data accuracy, consistency, and performance. Adjustments were made to optimize the process, including refining the extraction from PDFs and ensuring the integration of multiple sources.
Vertex AI
Power BI
Cloud Functions
Colab
Technologies used
Chelsea Rhoads
CEO - IVEE Software
