Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions

Overview

Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions

Usage

  1. Clone the code to local.
https://github.com/tanlab/MIMIC-III-Clinical-Drug-Representations.git
cd MIMIC-III-Clinical-Drug-Representations
  1. Run MIMIC-Extract Pipeline as explained in https://github.com/MLforHealth/MIMIC_Extract.

  2. Copy the output file of MIMIC-Extract Pipeline named all_hourly_data.h5 to mimic-extract folder.

  3. Copy the ADMISSIONS.csv, PRESCRIPTIONS.csv, ICUSTAYS.csv files into mimic-iii folder.

  4. Run 01-MIMIC-III-Drugs-Names-To-Pubchem-ID.ipynb to convert MIMIC-III Drug names into Pubchem ID.

  5. Download drug information via FDA. https://www.fda.gov/drugs/drug-approvals-and-databases/national-drug-code-directory.

  6. Run 02-Create-Cohort.ipynb to select correct drugs for patients and create the final cohort.

  7. Run 03-Embeddings.ipynb to get embeddings of drugs.

  8. Run 04-Timeseries.ipynb to run timeseries baseline model to predict 4 different clinical tasks.

  9. Run 05-ECFP-1024-TimeSeries.ipynb to run ECFP multimodal baseline to predict 4 different clinical tasks.

  10. Run 6-Smiles-Transformer-TimeSeries.ipynb to run Transformers multimodal baseline to predict 4 different clinical tasks.

References

Download the MIMIC-III dataset via https://mimic.physionet.org/

MIMIC-Extract implementation: https://github.com/MLforHealth/MIMIC_Extract

Owner
Computational Biology and Machine Learning lab @ TOBB ETU
Computational Biology and Machine Learning lab @ TOBB ETU
Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations

NANSY: Unofficial Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations Notice Papers' D

Dongho Choi 최동호 104 Dec 23, 2022
Predict bus arrival time using VertexAI and Nvidia's Jetson Nano

bus_prediction predict bus arrival time using VertexAI and Nvidia's Jetson Nano imagenet the command for imagenet.py look like this python3 /path/to/i

10 Dec 22, 2022
A modular application for performing anomaly detection in networks

Deep-Learning-Models-for-Network-Annomaly-Detection The modular app consists for mainly three annomaly detection algorithms. The system supports model

Shivam Patel 1 Dec 09, 2021
Code for CPM-2 Pre-Train

CPM-2 Pre-Train Pre-train CPM-2 此分支为110亿非 MoE 模型的预训练代码,MoE 模型的预训练代码请切换到 moe 分支 CPM-2技术报告请参考link。 0 模型下载 请在智源资源下载页面进行申请,文件介绍如下: 文件名 描述 参数大小 100000.tar

Tsinghua AI 136 Dec 28, 2022
The sixth place winning solution (6/220) in 2021 Gaofen Challenge.

SwinTransformer + OBBDet The sixth place winning solution (6/220) in the track of Fine-grained Object Recognition in High-Resolution Optical Images, 2

ming71 46 Dec 02, 2022
An official implementation of "Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation" (ICCV 2021) in PyTorch.

Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic Segmentation This is an official implementation of the paper "Exploiting a Joint

CV Lab @ Yonsei University 35 Oct 26, 2022
Genpass - A Passwors Generator App With Python3

Genpass Welcom again into another python3 App this is simply an Passwors Generat

Mal4D 1 Jan 09, 2022
A module for solving and visualizing Schrödinger equation.

qmsolve This is an attempt at making a solid, easy to use solver, capable of solving and visualize the Schrödinger equation for multiple particles, an

506 Dec 28, 2022
Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code

Python wrapper class for OpenVINO Model Server. User can submit inference request to OVMS with just a few lines of code.

Yasunori Shimura 7 Jul 27, 2022
Action Segmentation Evaluation

Reference Action Segmentation Evaluation Code This repository contains the reference code for action segmentation evaluation. If you have a bug-fix/im

5 May 22, 2022
Table-Extractor 表格抽取

(t)able-(ex)tractor 本项目旨在实现pdf表格抽取。 Models 版面分析模块(Yolo) 表格结构抽取(ResNet + Transformer) 文字识别模块(CRNN + CTC Loss) Acknowledgements TableMaster attention-i

2 Jan 15, 2022
A self-supervised 3D representation learning framework named viewpoint bottleneck.

Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck Paper Created by Liyi Luo, Beiwen Tian, Hao Zhao and Guyue Zhou from Institute for AI In

63 Aug 11, 2022
Autoencoders pretraining using clustering

Autoencoders pretraining using clustering

IITiS PAN 2 Dec 16, 2021
Facial Image Inpainting with Semantic Control

Facial Image Inpainting with Semantic Control In this repo, we provide a model for the controllable facial image inpainting task. This model enables u

Ren Yurui 8 Nov 22, 2021
This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios"

TinyWeaklyIsolationForest This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised a

2 Mar 21, 2022
Machine Learning Time-Series Platform

cesium: Open-Source Platform for Time Series Inference Summary cesium is an open source library that allows users to: extract features from raw time s

632 Dec 26, 2022
DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)

DECA: Detailed Expression Capture and Animation (SIGGRAPH2021) input image, aligned reconstruction, animation with various poses & expressions This is

Yao Feng 1.5k Jan 02, 2023
Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

Deep Hedging Demo Pricing Derivatives using Machine Learning 1) Jupyter version: Run ./colab/deep_hedging_colab.ipynb on Colab. 2) Gui version: Run py

Yu Man Tam 102 Jan 06, 2023
Implementation of algorithms for continuous control (DDPG and NAF).

DEPRECATION This repository is deprecated and is no longer maintaned. Please see a more recent implementation of RL for continuous control at jax-sac.

Ilya Kostrikov 288 Dec 31, 2022
Code for the paper "Adapting Monolingual Models: Data can be Scarce when Language Similarity is High"

Wietse de Vries • Martijn Bartelds • Malvina Nissim • Martijn Wieling Adapting Monolingual Models: Data can be Scarce when Language Similarity is High

Wietse de Vries 5 Aug 02, 2021