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Analyzed customer churn data using Python and machine learning to identify patterns linked to customer retention. Cleaned and explored the dataset, engineered features, and built a churn prediction model with an interactive analysis dashboard.
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RetainCLM is an AI-powered Customer Lifecycle Management platform that predicts churn before it happens and automates the retention actions that stop it. Instead of reacting to cancellations, your team acts on risk signals in real time, powered by machine learning, not guesswork.
flagging churn risk before people cancel is how retention should work, nice demo
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That's really unique & futuristic dashboard 🔥🔥🔥🔥🔥
Reading a document is easy. Understanding its meaning is the real challenge.
While building AI-powered medical document intelligence systems, I’ve been learning that extracting words is only one part of the problem.
Consider this sentence:
“Patient denies chest pain.”
A system might recognize the words correctly but still extract the wrong information if it ignores the word “denies.”
The same challenge appears in phrases like:
“No history of asthma”
“Mother had breast cancer”
“Rule out pneumonia”
“Aspirin discontinued”
Each sentence requires more than text recognition. It requires understanding context, negation, uncertainty, relationships, and whether information is current.
This is one of the challenges I’m exploring while building MDIS (Medical Document Intelligence System) at Cognate AI.
My focus is on developing systems that go beyond extracting information—toward structured outputs, validation, evidence traceability, and human review.
Because in healthcare AI, extracting the right words is not enough. The system must preserve what those words actually mean.
I’m continuing to learn, build, test, and improve this system one step at a time.
What do you think is the biggest challenge in making AI understand clinical language accurately?
#AIEngineering #HealthcareAI #MedicalAI #DocumentIntelligence #BuildingInPublic
negation like that is exactly where naive extraction falls apart, great example