BC3407-Group-5-Project - BC3407 Group Project With Python

Overview

BC3407-Group-5-Project

As the world struggles to contain the ever-changing variants of COVID-19, healthcare industry is facing tremendous stress from issues arising from different aspects. One significant issue is on resource allocation and utilization. Building additional hospital facility may not be a viable solution in a land scarce country like Singapore. One long standing problem among resource utilization is due to missed appointments. When patients do not show up following their appointment time, the missed appointment results in a waste of resources that have been scheduled and planned. There has not been good solution in reducing the no-show rates. In some, the rates are not even been tracked or computed.

You are to create a Python-based program that can be useful for healthcare industry in tackling this issue. This program is a prototype or proof-of-concept to path the implementation for subsequent development solution to be integrated to the existing system. You are given a sample dataset to start with. It comes with past records of patients who show-up or did not show-up for the appointment in a clinic. You can use it as a start, to develop a suitable solution to provide insights to healthcare professionals. The solution can be a dashboard, web-based or command prompt program. The objective is to apply what you have learnt in this course into this problem domain.

The following table displays the first 5 records from the attached dataset “appointmentData.csv”. It includes the age and gender information of patients. Followed by the time they registered for the appointment and the appointment time details. The day name of appointment is reflected. The other details like their health condition, and other relevant features are also captured. The last column “Show Up” denotes if the patient shows up for the medical appointment.

image

Below are some questions you can try to answer or features you can try to include, you can address one or more of the following or suggest other relevant questions:

  1.     How is the overall picture of no shows over the period?
    
  2.     No-show made up of how many percent of the given data?
    
  3.     What kind of people or age group likely to no-show? Or it’s random?
    
  4.     Does sending reminder helps with reducing no-show?
    
  5.     Provide appropriate dashboard or visualization features to summarize the given data set for viewing.
    
  6.     Suggest or implement suitable features that can help with reducing no-show or highlight patients more likely to no-show with additional reminders or etc?
    

Alternatively, you can look for additional dataset that can help with understanding or tackling this issue or for better resource planning in healthcare. For example, the healthcare resources data, infection rates, chronic disease rates etc. You can make assumptions on information not stated in this requirements, or target a certain specialist clinic.

All work must be done in the Python programming language.

Deliverables: Prepare a zip file (in .zip format only) containing the relevant deliverables below:

  • Report: One word or pdf file containing the proof-of-concept prototype with the following content: -- Work/responsibility distribution. Which team members in charge of which part of the program. -- Objective of the project and how it addresses the issue. -- Features/Functionalities designed for the prototype. -- User manual with print screens from the prototype to illustrate how to use this project's program; consider different user roles. -- Include the links to recording, i,e. the hyperlinks to every group member’s individual recorded videos. Do not submit video files, submit only hyperlink.
  • One folder containing additional dataset (if any), all working files.

One group submit one copy of the above. Submission box will be opened in Week 11. All works submitted will go through plagiarism checker. Submitting work done by others will result in failing the module directly.

No presentation nor lesson on week 13, you are to prepare the recording beforehand and submit before due date. Recording replaces class presentation. The duration of each group member’s individual recording should be about 2 to 3 minutes maximum. You are advised to adhere to the time limit strictly or penalty will be imposed. In the individual video, you are required to reflect on your contribution to the group project, what you have done for the project, what have you learnt from the project. Each group member will record his/her own video and ensure that the hyperlink to the video is included on the first page of the report submitted by the group. You can use Zoom, Teams or YouTube for recording. If you use other software to record into mp4 file, upload online to Youtube, OneDrive or other cloud storage (Do not submit video files). In the case of Youtube, indicate as restricted, and submit only the link information. Include the link information (one link for each team member) in your report.

You are required to submit a peer evaluation form online individually at the end of the semester. Individual peer evaluation submission is compulsory for all team members. An online peer evaluation system will be opened nearer to the submission date.

Good project outcome is the end product of good teamwork. We hope to see all team members contribute equally. The peer evaluation will be considered in evaluating the project grade should the contribution be significantly unequal. Submission will be kept confidential.

Due Date: Week 13 Wednesday (13 Apr 2022, 7 p.m.)

Reference: Zoom Recording: Registering your NTU Zoom account (If you have not done so. Quick Start Guide for NTU Zoom Account.pdf) Zoom Login & Create Meeting (See Quick Start Guide for Online Meetings with Zoom.pdf) Video Guide on Recording Teams Recording: Zoom Login & Create Meeting (See Quick Start Guide for Online Meetings with Zoom.pdf) Online Guide on Recording: https://support.microsoft.com/en-us/office/record-a-meeting-in-teams-34dfbe7f-b07d-4a27-b4c6-de62f1348c24

Prompt Tuning with Rules

PTR Code and datasets for our paper "PTR: Prompt Tuning with Rules for Text Classification" If you use the code, please cite the following paper: @art

THUNLP 118 Dec 30, 2022
Official PyTorch Code of GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection (CVPR 2021)

GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Mo

Abhinav Kumar 76 Jan 02, 2023
"Neural Turing Machine" in Tensorflow

Neural Turing Machine in Tensorflow Tensorflow implementation of Neural Turing Machine. This implementation uses an LSTM controller. NTM models with m

Taehoon Kim 1k Dec 06, 2022
Reinforcement learning models in ViZDoom environment

DoomNet DoomNet is a ViZDoom agent trained by reinforcement learning. The agent is a neural network that outputs a probability of actions given only p

Andrey Kolishchak 126 Dec 09, 2022
TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently.

Adversarial Chess TensorFlow implementation of Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently. Requirements To run

Muthu Chidambaram 30 Sep 07, 2021
Imagededup - 😎 Finding duplicate images made easy

imagededup is a python package that simplifies the task of finding exact and near duplicates in an image collection.

idealo 4.3k Jan 07, 2023
Denoising Diffusion Probabilistic Models

Denoising Diffusion Probabilistic Models This repo contains code for DDPM training. Based on Denoising Diffusion Probabilistic Models, Improved Denois

Alexander Markov 7 Dec 15, 2022
EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks

EncT5 (Unofficial) Pytorch Implementation of EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks About Finetune T5 model for classification & r

Jangwon Park 34 Jan 01, 2023
Auto-updating data to assist in investment to NEPSE

Symbol Ratios Summary Sector LTP Undervalued Bonus % MEGA Strong Commercial Banks 368 5 10 JBBL Strong Development Banks 568 5 10 SIFC Strong Finance

Amit Chaudhary 16 Nov 01, 2022
Pytorch implementation of Decoupled Spatial-Temporal Transformer for Video Inpainting

Decoupled Spatial-Temporal Transformer for Video Inpainting By Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, J

51 Dec 13, 2022
Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples"

KSTER Code for our EMNLP 2021 paper "Learning Kernel-Smoothed Machine Translation with Retrieved Examples" [paper]. Usage Download the processed datas

jiangqn 23 Nov 24, 2022
Official implementation for "Image Quality Assessment using Contrastive Learning"

Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi

Pavan Chennagiri 67 Dec 30, 2022
Repository for "Space-Time Correspondence as a Contrastive Random Walk" (NeurIPS 2020)

Space-Time Correspondence as a Contrastive Random Walk This is the repository for Space-Time Correspondence as a Contrastive Random Walk, published at

A. Jabri 239 Dec 27, 2022
Easy genetic ancestry predictions in Python

ezancestry Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom

Kevin Arvai 38 Jan 02, 2023
Cross-Modal Contrastive Learning for Text-to-Image Generation

Cross-Modal Contrastive Learning for Text-to-Image Generation This repository hosts the open source JAX implementation of XMC-GAN. Setup instructions

Google Research 94 Nov 12, 2022
Instant-nerf-pytorch - NeRF trained SUPER FAST in pytorch

instant-nerf-pytorch This is WORK IN PROGRESS, please feel free to contribute vi

94 Nov 22, 2022
[CVPR 2016] Unsupervised Feature Learning by Image Inpainting using GANs

Context Encoders: Feature Learning by Inpainting CVPR 2016 [Project Website] [Imagenet Results] Sample results on held-out images: This is the trainin

Deepak Pathak 829 Dec 31, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging object detection dataset

CPPE - 5 CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization

Rishit Dagli 53 Dec 17, 2022
Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021.

NL-CSNet-Pytorch Pytorch code for paper "Image Compressed Sensing Using Non-local Neural Network" TMM 2021. Note: this repo only shows the strategy of

WenxueCui 7 Nov 07, 2022