**Predicting Hospital Readmission using Recurrent Neural Net

Predicting Hospital Readmission using Recurrent Neural Networks

Here's a simplified Python snippet utilizing the Keras library to demonstrate how Recurrent Neural Networks (RNNs) can predict hospital readmission:

from keras.models import Sequential
from keras.layers import LSTM, Dense

model = Sequential()
model.add(LSTM(50, input_shape=(10, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=100, verbose=0)

In this code, we're training an RNN to forecast hospital readmission by analyzing a patient's 10-day historical healthcare data. The LSTM (Long Short-Term Memory) layer allows the model to capture temporal dependencies, leveraging time-series patterns to make accurate predictions. By compiling the model using the Adam optimizer and minimizing the Mean Squared Error, we're ensuring the model produces accurate and reliable predictions.

This snippet serves as a prime example of how AI and machine learning can significantly enhance patient care by identifying high-risk patients and enabling targeted interventions to prevent avoidable readmissions.

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