[Feature Request]: Addition of Hospital Availability Prediction using ML

[murtaza-sadri-19]

Is there an existing issue for this?

• [X] I have searched the existing issues

Feature Description

1. Data Integration: Collect real-time data on patient arrivals, bed occupancy, staff availability, and service availability.
2. Historical Analysis: Analyze past trends and patterns in hospital utilization.
3. Machine Learning: Use predictive algorithms to forecast hospital capacity and wait times.
4. Visualization: Present predictions on a user-friendly dashboard.

Use Case

1. Patient Routing: Guide patients to hospitals with the highest probability of available beds and services, reducing wait times and improving patient outcomes.
2. Resource Allocation: Help hospitals optimize resource allocation by anticipating peak demand and ensuring adequate staffing and equipment.
3. Emergency Management: During emergencies or disasters, predict hospital capacity and inform emergency response teams accordingly.

Benefits

• Improved patient care by reducing wait times and optimizing resource allocation.
• Enhanced hospital efficiency through better demand forecasting and resource management.
• Improved emergency preparedness by providing accurate predictions of hospital capacity.
• Data-driven decision making for hospitals.

Priority

Low

Record

• [X] I agree to follow this project's Code of Conduct
• [X] I'm a GSSOC contributor
• [X] I want to work on this issue
• [X] I'm willing to provide further clarification or assistance if needed.

[murtaza-sadri-19]

Hi @mansiruhil13 , I hope that you would assign me this.

[mansiruhil13]

real time data? how? there is already a page for add ambulances and add drivers and manage. can you elaborate

[mansiruhil13]

[murtaza-sadri-19]

Hi @mansiruhil13

• Tackling Real Time Data : Over time, hospital management will update bed availability information as beds become occupied or vacant on the platform and relay that to the ML model in form of data. By analysing this real-time data along with the patient's location coordinates, we can predict the nearest available hospital.

• Model Training: Use the preprocessed historical data to train the model, teaching it to predict hospital availability based on the given features.

[mansiruhil13]

Hi @mansiruhil13

• Tackling Real Time Data : Over time, hospital management will update bed availability information as beds become occupied or vacant on the platform and relay that to the ML model in form of data. By analysing this real-time data along with the patient's location coordinates, we can predict the nearest available hospital.
• Model Training: Use the preprocessed historical data to train the model, teaching it to predict hospital availability based on the given features.

okay

[murtaza-sadri-19]

Hi @mansiruhil13

• Tackling Real Time Data : Over time, hospital management will update bed availability information as beds become occupied or vacant on the platform and relay that to the ML model in form of data. By analysing this real-time data along with the patient's location coordinates, we can predict the nearest available hospital.
• Model Training: Use the preprocessed historical data to train the model, teaching it to predict hospital availability based on the given features.

okay

So would you assign me this under GSSoC'24 Extd ?