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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

TensorFlow 2.0

TensorFlow is a general-purpose high-performance computing library open-sourced by Google in 2015. Since the beginning, its main focus was to provide high-performance APIs for building Neural Networks (NNs). However, with the advance of time and interest by the Machine Learning (ML) community, the lib has grown to a full ML ecosystem.

TensorFlow 2.0 represents a major milestone in the library’s development. Over the past few years, one of TensorFlow’s main weaknesses was its very complicated API.Defining deep neural networks required far more work than was reasonable. This led to the development of several high-level APIs that sat on-top of TensorFlow including TF Slim and Keras.Keras will be the official API of TensorFlow 2.0. Loading data, defining models, training, and evaluating are all now much easier to do, with cleaner Keras style code and faster development time.

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#Import TensorFlow into your program:
from __future__ import absolute_import, division, print_function, unicode_literals
# Install TensorFlow
import tensorflow as tf
from sklearn import preprocessing
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import SGD, Adam
from tensorflow.keras.callbacks import ModelCheckpoint
from keras.callbacks import Callback, EarlyStopping

Using TensorFlow backend.