Hi fellow Devs 👋,
Welcome to my GitHub profile!
I look forward to both learn from and contribute to the community, inspiring and supporting fellow developers on their journeys. Together, we'll thrive and upskill in this vibrant community. 😇
I am a \ an ...
- 💻 MSc Data Science student @Berliner Hochschule für Technik (BHT).
- 💡Full-Stack Engineer with 2+ years of experience in the Ed-Tech domain.
- 🔭 Aspiring Full Stack Machine Learning Engineer passionate about ML and Full Stack Development, seeking opportunities to collaborate.
- 💞️ Open to collaborative projects leveraging my expertise in Software Engineering, excited to contribute and make a meaningful impact.
Backend Development
With a follow up paper to extend the research,
This research addresses the significant problems caused by handwritten drug prescriptions, such as medication errors from misinterpretation and the lack of standardized data for research.
Proposed Solution:
The authors propose a system that allows doctors to prescribe medication orally. This system uses speech-to-text technology to capture the doctor's dictation and then generates a standardized, digital e-prescription.
Technical Approach:
The core of the system is a Natural Language Understanding (NLU) model that employs a slot-filling technique. This method extracts key information—like drug name, dosage, and frequency—directly from the transcribed speech. To overcome the scarcity of training data, the researchers used data augmentation, creating artificial prescriptions to train their model effectively.
The ultimate goal is to replace ambiguous handwritten notes with clear, error-free, and digitally recorded prescriptions, improving patient safety and data collection.
Feel free to reach out, I'll be happy to hear from you.
