A Step-by-Step Guide to Building ML Model for Pregnancy Complications Prediction
2 weeks ago
Pregnancy is a beautiful journey, but it can also come with serious risks.
Shockingly, over 80% of maternal deaths could be prevented with earlier intervention (CDC).
Yet, many doctors still miss more than 50% of these risks until it's too late.
Imagine a tool that predicts these dangers as early as the first prenatal visit.
That’s where machine learning (ML) comes in.
By using ML in healthcare, we can identify high-risk pregnancies months before delivery, giving doctors more time to act.
This blog will guide health-tech entrepreneurs on how to build ML solutions like Elythea, a platform designed to predict complications and save lives.
Let's explore how ML can transform maternal healthcare for good.
The Problem with Traditional Risk Assessments
Current pregnancy risk assessments are outdated, with doctors often waiting until labor to check for complications.
This leads to over 50% of high-risk cases being missed, putting mothers at unnecessary risk.
1. Financial Impact
Pregnancy complications come with a high financial cost—up to $15,000 per case for healthcare providers and $75,000 for payers.
In the U.S. alone, these costs exceed $55 billion annually.
2. Crisis in Developing Countries
In low-resource regions, doctors detect life-threatening conditions like hemorrhages in just 1-3% of cases.
Shockingly, 99% of maternal deaths occur in these areas.
3. The Opportunity
There’s a huge opportunity to change this.
By developing machine learning (ML) solutions, you can help identify risks early, improve care, and save lives. The need is urgent, and the impact could be life-changing.
How Elythea Uses Machine Learning for Pregnancy Risk Prediction
Meet Elythea, a groundbreaking platform changing the game in maternal healthcare.
Founded by Reetam Ganguli, who started med school at Brown University at just 17, Elythea aims to solve critical challenges in pregnancy care.
Reetam left medical school to focus on this important project.
He has also authored over 20 publications, including some in top journals like Nature.
Elythea uses machine learning (ML) to predict and prevent pregnancy complications.
Unlike traditional methods that often miss risks until it's too late, Elythea can spot high-risk patients as early as their first trimester.
This early detection can save lives.
Elythea's results speak for themselves. Clinical studies show that it:
- Outperforms standard clinical tools.
- Identifies 2-3 times more at-risk mothers than traditional methods.
- Detects risks over 10 times earlier in pregnancy.
With this early warning system, healthcare providers can manage high-risk pregnancies more effectively.
They can take preventative actions and ensure that mothers have access to the resources they need.
Elythea's Development Journey: From Idea to Impact
Elythea is not just a tech platform; it’s a solution to a major gap in maternal healthcare. Here’s a brief overview of its journey:
1. A Personal Passion
Founder Reetam Ganguli was inspired by his mother's complications after surgery.
This experience drove him to interview over 80 obstetric providers worldwide, highlighting the urgent need for better risk assessment tools.
2. Accessibility
With 99% of maternal deaths occurring in developing countries, Elythea was designed to be mobile and web-based.
It allows healthcare providers to manually input data, making it usable even in low-tech clinics.
3. Rigorous Testing
Extensive clinical trials in the U.S., Cameroon, and Nigeria showed Elythea can identify 2-3 times more at-risk mothers and detect risks over 10 times earlier than traditional methods.
4. Sustainable Model
Elythea uses a dual business model.
In the U.S., hospitals pay a per-patient fee, while in developing countries, governments cover costs based on local income levels, ensuring broad access.
5. Strategic Partnerships
Collaborating with experts like Melissa Bime, Elythea is expanding its reach, conducting large trials, and working to integrate into healthcare systems globally.
Building a Solution Like Elythea: A Potential Development Roadmap
If you’re thinking about building a similar solution, here’s a roadmap inspired by Elythea's approach:
1. Data Collection and Analysis
Start by gathering demographic and clinical data from expectant mothers, including age, previous pregnancies, smoking status, education, and medical history.
Utilize resources like Electronic Health Records (EHRs) or public medical databases to train your ML models for accurate predictions.
2. ML Model Training
Train your ML models to recognize patterns between risk factors and pregnancy complications.
Employ advanced techniques like deep learning algorithms, as Elythea does, to analyze complex relationships within the data.
3. Identifying Hidden Trends and Interactions
Uncover subtle relationships in your data that traditional methods might miss.
Consider how factors like age, BMI, and medical histories interact to reveal new insights into patient risk profiles.
4. Predicting Complications and Providing Risk Scores
Generate risk scores for complications such as postpartum hemorrhage and preeclampsia.
Present these scores through an easy-to-use platform, helping healthcare providers identify high-risk patients early.
5. Facilitating Early Intervention and Proactive Care
Empower healthcare providers with tools for early identification.
This allows for preventative measures and tailored care plans, ensuring timely interventions for at-risk patients.
Key Advantages to Embrace
- Early Detection: Create a system that identifies at-risk mothers 2-3 times more effectively than traditional methods.
- Improved Accuracy: Develop ML models that outperform existing clinical tools to build trust with healthcare providers.
- Accessibility: Design your solution as a mobile and web-based platform for use in low-resource settings.
Essential Technologies for Developing ML in Healthcare
Here's a table summarizing the tech stack for developing a machine learning solution in healthcare:
Keep in mind that this is a general tech stack. For more tailored insights, connect with tech experts by filling out this form.
Challenges & Considerations for Machine Learning (ML) in Prenatal Care
Integrating machine learning (ML) into healthcare comes with some challenges.
First, let’s talk about ethical implications. Bias and fairness are big concerns.
For example, if your model mainly uses data from urban hospitals, it might miss risks for rural patients.
That’s not fair, right?
Regulatory compliance is a must. We should prioritize data security with strong encryption.
Also, getting informed consent from patients about how their data will be used is essential.
Now, gaining trust can be tricky. Some doctors might be hesitant to adopt ML.
They may worry about accuracy or how it affects their workflow. Patients might have concerns about data privacy too. Clear communication is key here.
Finally, don’t forget about ongoing monitoring.
ML models need regular updates to stay accurate.
Regularly retrain your models with new data and keep evaluating their performance.
Why Partner with a Health Tech Company for AI and ML Solutions?
Navigating AI and machine learning (ML) in healthcare can be challenging, especially in Canada.
Partnering with a health tech company simplifies this process.
These companies understand local healthcare systems, regulations, and workflows, ensuring your solutions are effective and compliant.
They have specialized teams in data science and software development, allowing you to leverage their expertise.
Moreover, they prioritize data security and know privacy regulations like PIPEDA, ensuring patient information is protected.
Each healthcare organization has unique needs, and a health tech company can customize AI and ML solutions for you.
By collaborating with them, you can focus on improving patient outcomes while they handle the technical complexities, giving you peace of mind.
Talk with our AI & ML experts & discover your ML model development journey (with free demo).