NHS AI Lab Skunkworks project: Long Stayer Risk Stratification

Overview

Banner

NHS AI Lab Skunkworks project: Long Stayer Risk Stratification

A pilot project for the NHS AI Lab Skunkworks team, Long Stayer Risk Stratification uses historical data from Gloucestershire Hospitals NHS Foundation Trust to predict how long a patient will stay in hospital upon admission.

As the successful candidate from a Dragons’ Den-style project pitch, Long Stayer Risk Stratification was first picked as a pilot project for the AI (Artificial Intelligence) Skunkworks team in April 2021.

Background

Hospital long stayers, those with a length of stay (LoS) of 21 days or longer, have significantly worse medical and social outcomes than other patients. Long-stayers are often medically optimised (fit for discharge) many days before their actual discharge. Moreover, there are a complex mixture of medical, cultural and socioeconomic factors which contribute to the causes of unnecessary long stays.

This repository contains a proof-of-concept demonstrator, developed as part of a research project - a collaboration between Polygeist, Gloucestershire Hospitals NHS Foundation Trust, NHSX, and the Home Office’s Accelerated Capability Environment (ACE). The project aimed to achieve two core objectives:
firstly, to determine if an experimental artificial intelligence (AI) approach to predicting hospital long-stayers was possible; secondly, if so, to produce a proof-of-concept (PoC) risk stratification tool.

Stratification Tool

Banner

The tool displays the LTSS for a patient record, between Level 1 and 5; with 5 being the most severe risk of the patient becoming a long stayer. The tool allows exploration of various factors, and enables the user to edit those entries to produce refined or hypothetical estimates of the patient's risk.

The tool has shown good risk stratification for real data, with Level 1 consisted of 99% short stayers, and minor cases, with less than 1% of long-stayers being classified as very low risk. Moreover, 66% of all long-stayers were classified as Risk Category 4 and 5, with proportions steadily increasing through the categories. Risk Category 5 also stratified those patients with long and serious hospital stays under the long-stay threshold (serious and lengthy stays).

Documentation:

Docs Description
REST API API Endpoint descriptions and usage examples
LTSS Flask App API Package documentation for the ltss Python package and incorporated submodules
Deployment Instructions Build and run instruction for development or production deployments
WebUI Overview Description of UI components and application structure
Configuration Files Overview of provided configuration files
Production Build Configuration Files Overview of the configuration files provided for production build Docker containers
Training Description of the training process for the models used in the LTSS API

NHS AI Lab Skunkworks

The project is supported by the NHS AI Lab Skunkworks, which exists within the NHS AI Lab to support the health and care community to rapidly progress ideas from the conceptual stage to a proof of concept.

Find out more about the NHS AI Lab Skunkworks. Join our Virtual Hub to hear more about future crowdsourcing event opportunities. Get in touch with the Skunkworks team at [email protected].

Owner
NHSX
NHSX
Codes for paper "Towards Diverse Paragraph Captioning for Untrimmed Videos". CVPR 2021

Towards Diverse Paragraph Captioning for Untrimmed Videos This repository contains PyTorch implementation of our paper Towards Diverse Paragraph Capti

Yuqing Song 61 Oct 11, 2022
Studying Python release adoptions by looking at PyPI downloads

Analysis of version adoptions on PyPI We get PyPI download statistics via Google's BigQuery using the pypinfo tool. Usage First you need to get an acc

Julien Palard 9 Nov 04, 2022
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering Authors: Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou and

Salesforce 72 Dec 05, 2022
[CVPR 2022 Oral] Versatile Multi-Modal Pre-Training for Human-Centric Perception

Versatile Multi-Modal Pre-Training for Human-Centric Perception Fangzhou Hong1  Liang Pan1  Zhongang Cai1,2,3  Ziwei Liu1* 1S-Lab, Nanyang Technologic

Fangzhou Hong 96 Jan 03, 2023
Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

Explicable Reward Design for Reinforcement Learning Agents [NeurIPS'21]

3 May 12, 2022
Official Pytorch implementation of RePOSE (ICCV2021)

RePOSE: Iterative Rendering and Refinement for 6D Object Detection (ICCV2021) [Link] Abstract We present RePOSE, a fast iterative refinement method fo

Shun Iwase 68 Nov 15, 2022
SuMa++: Efficient LiDAR-based Semantic SLAM (Chen et al IROS 2019)

SuMa++: Efficient LiDAR-based Semantic SLAM This repository contains the implementation of SuMa++, which generates semantic maps only using three-dime

Photogrammetry & Robotics Bonn 701 Dec 30, 2022
Implementation of Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021)

PSWE: Pooling by Sliced-Wasserstein Embedding (NeurIPS 2021) PSWE is a permutation-invariant feature aggregation/pooling method based on sliced-Wasser

Navid Naderializadeh 3 May 06, 2022
Enhancing Knowledge Tracing via Adversarial Training

Enhancing Knowledge Tracing via Adversarial Training This repository contains source code for the paper "Enhancing Knowledge Tracing via Adversarial T

Xiaopeng Guo 14 Oct 24, 2022
Asynchronous Advantage Actor-Critic in PyTorch

Asynchronous Advantage Actor-Critic in PyTorch This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learn

Reiji Hatsugai 38 Dec 12, 2022
Fight Recognition from Still Images in the Wild @ WACVW2022, Real-world Surveillance Workshop

Fight Detection from Still Images in the Wild Detecting fights from still images is an important task required to limit the distribution of social med

Şeymanur Aktı 10 Nov 09, 2022
Some methods for comparing network representations in deep learning and neuroscience.

Generalized Shape Metrics on Neural Representations In neuroscience and in deep learning, quantifying the (dis)similarity of neural representations ac

Alex Williams 45 Dec 27, 2022
A Java implementation of the experiments for the paper "k-Center Clustering with Outliers in Sliding Windows"

OutliersSlidingWindows A Java implementation of the experiments for the paper "k-Center Clustering with Outliers in Sliding Windows" Dataset generatio

PaoloPellizzoni 0 Jan 05, 2022
This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging" that has been accepted to NeurIPS 2021.

Dugh-NeurIPS-2021 This repo contains the code for the paper "Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroi

Ali Hashemi 5 Jul 12, 2022
magiCARP: Contrastive Authoring+Reviewing Pretraining

magiCARP: Contrastive Authoring+Reviewing Pretraining Welcome to the magiCARP API, the test bed used by EleutherAI for performing text/text bi-encoder

EleutherAI 43 Dec 29, 2022
Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

104 Dec 15, 2022
Remote sensing change detection using PaddlePaddle

Change Detection Laboratory Developing and benchmarking deep learning-based remo

Lin Manhui 15 Sep 23, 2022
Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)

Diffusion Probabilistic Models for 3D Point Cloud Generation [Paper] [Code] The official code repository for our CVPR 2021 paper "Diffusion Probabilis

Shitong Luo 323 Jan 05, 2023
The personal repository of the work: *DanceNet3D: Music Based Dance Generation with Parametric Motion Transformer*.

DanceNet3D The personal repository of the work: DanceNet3D: Music Based Dance Generation with Parametric Motion Transformer. Dataset and Results Pleas

南嘉Nanga 36 Dec 21, 2022
Offline Reinforcement Learning with Implicit Q-Learning

Offline Reinforcement Learning with Implicit Q-Learning This repository contains the official implementation of Offline Reinforcement Learning with Im

Ilya Kostrikov 125 Dec 31, 2022