Project 2 – AI Healthcare Project: Predictive Analytics for Patient Readmission

Project Overview

The goal of this project is to develop an AI model that predicts patient readmissions within 30 days after discharge from a hospital. This can help healthcare providers take proactive measures to prevent readmissions, thus improving patient care and reducing costs.

Project Lifecycle Stages

1. Problem Definition and Requirements Gathering

• Objective: To reduce patient readmission rates by predicting which patients are at high risk of being readmitted within 30 days.
• Stakeholders: Hospital administration, healthcare providers, data scientists, IT department.
• Requirements:
  • Collect historical patient data, including demographics, medical history, treatment details, and readmission status.
  • Develop a predictive model that can be integrated into the hospital’s existing systems.
  • Ensure compliance with healthcare regulations and data privacy laws.

2. Data Collection and Preparation

• Data Sources:
  • Electronic Health Records (EHR) from the hospital.
  • Public healthcare datasets (e.g., MIMIC-III).
• Data Collection:
  • Collect data on patient demographics (age, gender, etc.), medical history (previous conditions, medications), treatment details (procedures, length of stay), and readmission status.
• Data Cleaning:
  • Handle missing values by imputation or removal.
  • Normalize numerical values (e.g., age, length of stay).
  • Encode categorical variables (e.g., gender, diagnosis codes).
• Data Splitting:
  • Split the dataset into training (70%), validation (15%), and test sets (15%).

3. Exploratory Data Analysis (EDA)

• Data Visualization:
  • Use histograms, box plots, and scatter plots to visualize distributions and relationships between variables.
• Feature Engineering:
  • Create new features such as average length of stay, number of previous admissions, etc.
  • Perform correlation analysis to select relevant features.

4. Model Development

• Algorithm Selection:
  • Consider algorithms like Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and Neural Networks.
• Model Training:
  • Train multiple models using the training dataset.
  • Use cross-validation to tune hyperparameters and select the best model.
• Model Evaluation:
  • Evaluate models using metrics like accuracy, precision, recall, F1 score, and AUC-ROC.

5. Model Deployment

• Integration:
  • Develop an API to integrate the model with the hospital’s EHR system.
• Scalability:
  • Ensure the model can handle real-time data and large volumes of patient records.
• Security:
  • Implement encryption and access controls to protect patient data.

6. Monitoring and Maintenance

• Performance Monitoring:
  • Continuously monitor model performance using live data.
  • Track metrics and compare with validation results to detect any drift.
• Retraining:
  • Periodically retrain the model with new data to maintain accuracy.
• Feedback Loop:
  • Collect feedback from healthcare providers to improve model predictions and usability.

patient_id	age	gender	admission_type	length_of_stay	diagnosis	num_of_previous_admissions	num_of_medications	comorbidities	readmitted
001	65	Male	Emergency	5	I10	2	5	3	1
002	45	Female	Elective	3	E11	0	2	1	0
003	80	Female	Emergency	7	I25	4	6	4	1
004	30	Male	Elective	2	J44	1	3	2	0
005	50	Female	Emergency	6	E10	3	8	3	1
006	70	Male	Emergency	4	I11	2	5	3	1
007	60	Female	Elective	3	E12	1	4	2	0
008	75	Male	Emergency	5	I20	3	6	3	1
009	35	Female	Elective	2	J40	0	2	1	0
010	55	Male	Emergency	6	E11	4

011	40	Female	Emergency	4	I15	2	3	2	0
012	65	Male	Elective	5	E13	1	4	3	1
013	50	Female	Emergency	6	I30	3	5	3	1
014	70	Male	Elective	4	J45	2	6	2	0
015	45	Female	Emergency	5	E14	1	4	2	1
016	55	Male	Emergency	6	I25	3	7	3	1
017	60	Female	Elective	3	J20	2	5	2	0
018	35	Male	Emergency	4	E10	1	4	1	0
019	80	Female	Elective	5	I10	3	6	4	1
020	50	Male	Emergency	3	J30	2