Data

1. Patient ID

1.1. Patient attributes

1.1.1. Smoking

1.1.2. Height

1.1.3. Weight

1.1.4. Pregnancy

1.1.5. Medical History

1.1.5.1. Recent Surgery

1.1.5.2. Problem History

1.1.5.3. Medications

1.2. Presenting Information

1.3. Vital measurements

1.3.1. Blood pressure

1.3.2. Respiration Rate

1.3.3. Heart Rate

1.3.4. Temperature

2. Hospital

2.1. Model of Care

2.2. Hospital ID

2.3. Admission status

3. Model Evaluation

3.1. Multiclass logistic regression

3.2. Decision Trees

3.3. Neural Networks

3.4. Determine evaluation metric

3.5. Compare models

3.6. Select model

4. Feature Engineering

4.1. Clustering

4.2. Natural Language Processing

5. Data pre-processing

5.1. Validate data ranges

5.2. Validate data types

5.3. Group variables or values

5.4. Discard unused variables

5.5. Deal with missing values

6. Data visualisation

6.1. Visualise distributions

6.2. Visualise outliers

6.3. Visualise clusters

6.4. Visualise model evaluation

7. Product Development

7.1. Develop web application

7.2. Store data

7.3. Deploy model