Survival of a Patient - Intermediat/Advanced

Predicting the chances of Survival of a Patient after 1 year of treatment

Medium

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67 Submissions

A hospital in the province of Greenland has been trying to improve its care conditions by looking at historic survival of the patients. They tried looking at their data but could not identify the main factors leading to high survivals.


Objective

You are the best data scientist in Greenland and they've hired you to solve this problem. Now you are responsible for developing a model that will predict the chances of survival of a patient after 1 year of treatment (Survived_1_year).


Evaluation Criteria

Submissions are evaluated using the F1 Score. How do we do it? 

Once you generate and submit the target variable predictions on evaluation dataset, your submissions will be compared with the true values of the target variable. 

The True or Actual values of the target variable are hidden on the DPhi Practice platform so that we can evaluate your model's performance on evaluation data. Finally, an F1 score for your model will be generated and displayed

About the dataset

The dataset contains the patient records collected from a hospital in Greenland. The "Survived_1_year" column is a target variable which has binary entries (0 or 1).

  • Survived_1_year == 0, implies that the patient did not survive after 1 year of treatment
  • Survived_1_year == 1, implies that the patient survived after 1 year of treatment

To load the dataset in your jupyter notebook, use the below command:

import pandas as pd
pharma_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/pharma_data/Training_set_begs.csv')

Data Description:

  • ID_Patient_Care_Situation: Care situation of a patient during treatment
  • Diagnosed_Condition: The diagnosed condition of the patient
  • ID_Patient: Patient identifier number
  • Treatment_with_drugs: Class of drugs used during treatment
  • Survived_1_year: If the patient survived after one year (0 means did not survive; 1 means survived)
  • Patient_Age: Age of the patient
  • Patient_Body_Mass_Index: A calculated value based on the patient’s weight, height, etc.
  • Patient_Smoker: If the patient was a smoker or not
  • Patient_Rural_Urban: If the patient stayed in Rural or Urban part of the country
  • Previous_Condition: Condition of the patient before the start of the treatment ( This variable is splitted into 8 columns - A, B, C, D, E, F, Z and Number_of_prev_cond. A, B, C, D, E, F and Z are the previous conditions of the patient. Suppose for one patient, if the entry in column A is 1, it means that the previous condition of the patient was A. If the patient didn't have that condition, it is 0 and same for other conditions. If a patient has previous condition as A and C , columns A and C will have entries as 1 and 1 respectively while the other column B, D, E, F, Z will have entries 0, 0, 0, 0, 0 respectively. The column Number_of_prev_cond will have entry as 2 i.e. 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 = 2 in this case. )

Feel free to google 'Diagnose' and 'Body Mass Index' if you don't know about these terms.


To load the test dataset in your jupyter notebook, use the below command:


test_data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/pharma_data/Testing_set_advc.csv')

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File Format

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Predictions

This file should have a header row called 'prediction'.
Please see the instructions to save a prediction file under the “Data” tab.

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