Add project: Predicting Patient Admission to Intensive Care Unit (ICU) using Machine Learning

[Aditi22Bansal]

Is your feature request related to a problem? Please describe. A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

Hospitals often face challenges in effectively managing their resources, particularly ICU beds, which are limited and crucial for critical patient care. The problem is exacerbated during peak times or unexpected health crises, such as pandemics, where the demand for ICU beds can suddenly spike. Currently, there is a lack of predictive tools to assist healthcare providers in anticipating ICU admissions, which can lead to inefficient resource allocation, delayed patient care, and increased mortality rates.

Describe the solution you'd like A clear and concise description of what you want to happen.

The proposed solution is to develop a machine learning model that predicts whether a patient will require ICU admission based on their health indicators and hospital admission data. This model will help healthcare providers:

Identify high-risk patients early. Optimize ICU resource allocation. Enhance patient care by preparing in advance for critical cases. The solution involves:

Data preprocessing to clean and prepare hospital admission data. Exploratory Data Analysis (EDA) to understand data distribution and relationships. Feature engineering to select and transform relevant features. Model building using machine learning algorithms like Random Forest and Neural Networks. Model evaluation to ensure accuracy and reliability. Visualization of results to provide intuitive insights to healthcare providers.

Describe alternatives you've considered A clear and concise description of any alternative solutions or features you've considered.

Manual Risk Scoring Systems: Traditional risk scoring systems used by clinicians to assess patient conditions. However, these are often subjective and less accurate than data-driven approaches. Simple Statistical Models: Basic statistical models could be used, but they lack the predictive power and complexity handling capability of advanced machine learning models. Real-time Monitoring Systems: Systems that constantly monitor patient vitals and alert staff of critical changes. While useful, these do not predict future ICU needs but rather react to current conditions.

Additional context Add any other context or screenshots about the feature request here.

Historical patient admission records, including demographics, comorbidities, vital signs, lab results, and outcomes related to ICU admissions.

What problem is this feature trying to solve?

The feature addresses the problem of insufficient and reactive ICU resource management by providing a predictive tool to anticipate ICU admissions. This proactive approach can improve patient outcomes, reduce mortality rates, and optimize the use of critical care resources.

How do we know when the feature is complete?

The feature is considered complete when:

A robust and accurate machine learning model is developed and validated on historical data. The model achieves a predefined accuracy, precision, and recall threshold on test data. The solution is integrated into the hospital’s EHR system, providing real-time predictions for patient admissions. Visualizations and reports are generated to support decision-making by healthcare providers. The system undergoes user acceptance testing (UAT) and receives positive feedback from healthcare providers.

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[Aditi22Bansal]

@SrijanShovit Kindly assign me this issue

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