Name: Jürgen R. Plasser / ThetaPhiPsi
Type: User
Bio: Master of Science Artificial Intelligence, Diplomingenieur Informatik, both @ Johannes Kepler University, Linz.
Twitter: jplasser
Location: Linz, Austria
Blog: https://www.plasser.net
Jürgen R. Plasser / ThetaPhiPsi's Projects
Source Code for 'AI for Healthcare with Keras and Tensorflow 2.0' by Anshik Bansal
A python package for removing duplicate text in clinical notes or other documents
Contrastive Language-Image Pretraining
CNEP (Contrastive Notes Events Pre-training), Contrastive Learning with Clinical Notes and Events Data Pre-training from MIMIC-III
LaTeX files for the Deep Learning book notation
Code for AMIA CRI 2016 paper "Learning Low-Dimensional Representations of Medical Concepts"
Implementation and demo of explainable coding of clinical notes with Hierarchical Label-wise Attention Networks (HLAN)
FlexIble Data-Driven pipeLinE – a preprocessing pipeline that transforms structured EHR data into feature vectors to be used with ML algorithms. https://doi.org/10.1093/jamia/ocaa139
Simple transformer implementation from scratch in pytorch.
🦔 Bayesian networks in Python
Hopfield Networks is All You Need
About me!
Personal Github Page
Implementing a ChatGPT-like LLM from scratch, step by step
log anomaly detection via BERT
Quick script to parse out medications from discharge summaries in MIMIC format. Not that this approach uses minimal NLP, and can be vatly improved.
MIMIC Code Repository: Code shared by the research community for the MIMIC-III database
This is the source code for the paper 'Analysis and Prediction of Unplanned Intensive Care Unit Readmission'
Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database.
MIMIC-Extract:A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
An open source implementation of CLIP.
Simple implementation of OpenAI CLIP model in PyTorch.
This repository contains the code for "Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference"
Practical Guide to Applied Conformal Prediction, published by Packt
Comprehensive Python Cheatsheet
Code included in the book, PyTorch Pocket Reference