Drug Treatment Insights: Deep Learning & Analytics

This repository contains the solutions to the problems related to drug treatment analysis. The analysis is based on a dataset containing electronic health records of patients diagnosed with a specific disease. The dataset provides detailed information about each patient's medical history, including diagnoses, symptoms, prescribed drug treatments, and medical tests.

Problem 1: Predicting Eligibility for "Target Drug"

The objective of this problem is to develop a predictive model to determine whether a patient will be eligible for "Target Drug" in the next 30 days. The model aims to assist physicians in making informed decisions about treatment options. To solve this problem, the following steps were performed:

• Data Preprocessing: The dataset was prepared by cleaning and transforming the data into a suitable format for model training.

• Feature Engineering: Relevant features were extracted from the dataset to capture the patient's medical history and treatment patterns.

• Model Development: Various machine learning algorithms were employed to build a predictive model. The model was trained on historical data to predict eligibility for "Target Drug."

• Model Evaluation and Prediction: The model's performance was assessed using appropriate evaluation metrics to determine its accuracy and effectiveness and predict the output for final submission.

Problem 2: Analyzing Drop-off for "Target Drug"

In this problem, the focus is on studying the drop-off rate for "Target Drug" and identifying the events that lead to patients stopping the treatment. The following steps were undertaken to analyze drop-off rates:

• Calculation of Drop-off Rate: The drop-off rate was computed by tracking the number of patients discontinuing "Target Drug" each month.
• Analysis of Events: Events and factors contributing to treatment discontinuation were analyzed to understand why patients stop taking "Target Drug." Insights were generated by exploring the dataset and identifying patterns or correlations.

Problem 3: Analyzing Prescription Patterns for "Target Drug"

The objective of this problem is to analyze the patterns of administering "Target Drug" to patients. The analysis involves identifying the dominant patterns of drug prescription and visualizing them over time. The following steps were taken:

• Data Preparation: The dataset was processed to extract relevant information related to the administration of "Target Drug."
• Unsupervised Learning: Clustering or other unsupervised techniques were applied to identify distinct patterns of drug administration.
• Visualization: The identified patterns were visualized on a time axis (X-axis represents months, and Y-axis represents the number of prescriptions) to gain insights into the prescription patterns.

Requirements The solution notebooks require the following dependencies:

Python 3.x
Jupyter Notebook
pandas
scikit-learn
matplotlib
seaborn

Please ensure that these dependencies are installed before running the notebooks.

Instructions for Use

• Clone the repository to your local machine or download the ZIP archive.
• Ensure that all the dependencies mentioned above are installed.
• Open the desired notebook using Jupyter Notebook.
• Follow the instructions in the notebook to execute the code and observe the results.
• Repeat steps 3-4 for the other problem notebooks as well.
• Feel free to modify the code or experiment with different approaches to enhance the analysis further.

Conclusion

This analysis provides insights into drug treatment analysis, explicitly focusing on predicting eligibility, analyzing drop-off rates, and understanding prescription patterns for "Target Drug." The solutions provided in the notebooks aim to assist healthcare professionals in making informed decisions and improving patient outcomes.

License

This project is licensed under the MIT License. Thank you for viewing this repo! Feel free to reach out with any questions or feedback.

✨ --- Designed & made with Love by Shib Kumar Saraf ✨