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Objective

The challenge is to create a model that uses data from the first 24 hours of intensive care to predict patient survival. MIT's GOSSIS community initiative, with privacy certification from the Harvard Privacy Lab, has provided a dataset of more than 130,000 hospital Intensive Care Unit (ICU) visits from patients, spanning a one-year timeframe. This data is part of a growing global effort and consortium spanning Argentina, Australia, New Zealand, Sri Lanka, Brazil, and more than 200 hospitals in the United States.

Data Description

MIT's GOSSIS community initiative, with privacy certification from the Harvard Privacy Lab, has provided a dataset of more than 130,000 hospital Intensive Care Unit (ICU) visits from patients, spanning a one-year timeframe. This data is part of a growing global effort and consortium spanning Argentina, Australia, New Zealand, Sri Lanka, Brazil, and more than 200 hospitals in the United States.

The data includes:

Training data for 91,713 encounters.
Unlabeled test data for 39,308 encounters, which includes all the information in the training data except for the values for hospital_death.
WiDS Datathon 2020 Dictionary with supplemental information about the data, including the category (e.g., identifier, demographic, vitals), unit of measure, data type (e.g., numeric, binary), description, and examples.
Sample submission files

PyTorch

PyTorch is defined as an open source machine learning library for Python. It is initially developed by Facebook artificial-intelligence research group, and Uber’s Pyro software for probabilistic programming which is built on it.Originally, PyTorch was developed by Hugh Perkins as a Python wrapper for the LusJIT based on Torch framework. There are two PyTorch variants.

PyTorch redesigns and implements Torch in Python while sharing the same core C libraries for the backend code. PyTorch developers tuned this back-end code to run Python efficiently. They also kept the GPU based hardware acceleration as well as the extensibility features that made Lua-based Torch.

Features

The major features of PyTorch are mentioned below −

Easy Interface − PyTorch offers easy to use API; hence it is considered to be very simple to operate and runs on Python. The code execution in this framework is quite easy.

Python usage − This library is considered to be Pythonic which smoothly integrates with the Python data science stack. Thus, it can leverage all the services and functionalities offered by the Python environment.

Computational graphs − PyTorch provides an excellent platform which offers dynamic computational graphs. Thus a user can change them during runtime. This is highly useful when a developer has no idea of how much memory is required for creating a neural network model.

PyTorch is known for having three levels of abstraction as given below −

Tensor − Imperative n-dimensional array which runs on GPU.

Variable − Node in computational graph. This stores data and gradient.

Module − Neural network layer which will store state or learnable weights.

Advantages of PyTorch

The following are the advantages of PyTorch −

It is easy to debug and understand the code.
It includes many layers as Torch.
It includes lot of loss functions.
It can be considered as NumPy extension to GPUs.
It allows building networks whose structure is dependent on computation itself.