Name: William van Doorn
Type: User
Company: Maastricht University Medical Center
Bio: Resident clinical chemistry; software/machine learning enthusiast
Twitter: DoornWilliam
Location: Maastricht, The Netherlands
William van Doorn's Projects
Code used for Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications
Automated gating for clinical diagnostics with flow cytometry
This repository contains the learning material for the Nuclear TALENT course Learning from Data: Bayesian Methods and Machine Learning, in York, UK, June 10-28, 2019.
Official Documentation for the Binance APIs and Streams
Automation happy birthday messages on WhatsApp
Mobile quiz application with questions related to the field of clinical chemistry and laboratory medicine.
Chat with your medical guideline!
ECG classification from short single lead segments (Computing in Cardiology Challenge 2017 entry)
A collection of scripts and examples created while answering questions from the greater Dash community
Web scraper for the EFLM biological variations database
Folder / directory structure options and naming conventions for software projects
Code for the continuous glucose prediction study published in PLOS One.
Evalueren van LLM op tentamens binnen de opleiding klinische chemie
A quiz (exam) application based on Shiny
Version control for machine learning
CV WILLIAM V DOORN
Python package providing functionality and plotting for chemistry method comparison
Convert MP4 file to WhatsApp size format
Natural Gradient Boosting for Probabilistic Prediction
Integrating NiceGUI with Plotly/Dash framework
Create web-based interfaces with Python. The nice way.
A panel design tool
A personal SMS and Calls bot build using ChatGPT, Whisper and Twilio API
Repository that contains my personal website (wptmdoorn.name)
Build a Jekyll blog in minutes, without touching the command line.
š Productivity Insight
Convert scientific PubMED articles to audio-books
Scatter plots.
Python PDF parser for scientific publications
Code for the sepsis versus machine learning study published in PLOS One.