Proactive Financial Planning for Lung Cancer Patients through Machine Learning-based Cost Prediction
Developing a predictive model that can estimate the out-of-pocket medical expenses of patients diagnosed with Stage 3 & 4 Lung Cancer to help patients prepare and alleviate sudden financial stress. In this 8-week challenge, you willjoin a collaborative team of 50 AI engineers from all around the world.
Cancer treatment can be very expensive, and even with insurance coverage, out-of-pocket costs can quickly accumulate and become unmanageable. This can lead to financial strain, bankruptcy, and other negative outcomes for patients and their families. Research has shown that over 40% of patients diagnosed with cancer will declare personal bankruptcy within four years of their diagnosis. This is a significant problem that has a major impact on the lives of cancer patients and their families.
There are many factors that contribute to the high costs of cancer treatment. These include the cost of drugs and other treatments, the cost of hospital stays and other medical services, and the cost of travel and other related expenses. Additionally, patients may need to take time off work or reduce their work hours to undergo treatment, which can further impact their financial situation. The financial burden of cancer treatment can also have negative impacts on patient outcomes. Patients who experience financial stress may be more likely to delay or forego treatment, which can lead to poorer outcomes and higher healthcare costs in the long run. Patients who declare bankruptcy may also experience psychological distress, which can further impact their overall health and well-being.
Overall, the financial burden of cancer treatment is a significant problem that affects a large number of patients and their families. Finding ways to reduce this burden and help patients proactively plan for out-of-pocket costs is an important area of research that has the potential to improve patient outcomes and reduce the risk of negative financial outcomes for those undergoing cancer treatment. The development of a predictive model for cancer treatment costs has the potential to significantly reduce the financial burden of a cancer diagnosis on patients.
The project goals
The goal of this project is to develop a predictive model that can help cancer patients proactively plan for future out-of-pocket costs. The project specifically aims to model the annual out-of-pocket costs for Stage 3 & 4 Lung Cancer patients from the year of diagnosis until the terminal year (death), taking into consideration various relevant factors. The ultimate aim is to reduce the financial burden of cancer diagnosis and treatment by providing patients with a better understanding of their future medical expenses.
The main goals of this Omdena-Iryss Challenge are:
Data collection and cleaning.
Develop a predictive model that can accurately forecast the out-of-pocket medical costs of Stage 3 & 4 Lung Cancer patients from the year of diagnosis until the terminal year (death).
Consider multiple factors including age, comorbidities, primary insurance, and other medical history factors that may materially enhance the quality of the model.
Provide a report that summarises the findings of the project, including the objectives, methodologies, data sources, and analytical techniques used in the project.
Deliver a list of the raw data sets used in the analysis, along with any cleaned, transformed, or aggregated data that was generated during the project.
Describe the methodologies and techniques used in the analysis, including any assumptions or limitations of the data, and how the data was collected and processed.
Evaluate the model’s predictive power and accuracy, and ensure it meets the project’s success criteria.
Why join? The uniqueness of Omdena AI Innovation Challenges
A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.
And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.