FoundationDx

FoundationDx builds and automates innovative machine learning solutions in healthcare and underserved organizations that need to efficiently and effectively drive patient satisfaction, healthcare quality and process performance goals in complex data environments.

We’re a small, self-funded company that’s led by its founders. Most importantly, we make services that we love, and it shows. Our subscribers are more than customers… they’re fans who share our passion for the opportunities that AI presents in their business and community. We’re growing at a quick but manageable pace, not because of big rounds of funding, but because we provide valuable early insights into process risk and performance improvement factors at modest cost.

Special programs are provided for 501(c)(3) organizations

Unsupervised learning for Anomaly Detection

FoundationDx is offering Quick Turnaround machine learning studies to evaluate the effectiveness of using automated anomaly detection for improving the efficiency and effectiveness of your data driven quality and outcome processes.

We use Large Langauge Models (LLMs) to help craft, code and refine our Machine Learning (ML) solutions.

FoundationDx builds and automates innovative machine learning solutions in healthcare, security, and underserved organizations that need to efficiently and effectively drive quality data review processes in complex data environments.

We use a HybridAI approach where domain-specific rules and unsupervised learning provide valuable early insights into process risk and performance improvement factors at modest cost.

Special programs are provided for 501(c)(3) organizations

Feature	LLMs	Machine Learning	Hybrid AI (Rules + Unsupervised Learning)
Input Format	Requires text (needs conversion or prompting)	Works directly with structured/tabular data	Works with structured data and rule-derived features; no conversion needed
Performance on Small Datasets	Often worse unless fine-tuned on specific domain data	Generally better out of the box	Enhanced by injecting domain rules as weak signals or priors; handles small data better than ML alone in low-signal environments
Explainability	Low (black box)	Higher (especially with linear/logistic models or decision trees)	High: rule triggers are interpretable; anomaly scores can be contextualized by rules
Training on Small Data	Needs heavy prompt engineering or fine-tuning	Well-suited for training on thousands or millions of rows	Rules supplement missing data or label sparsity; unsupervised models bootstrap learning from structure
Inference Speed	Slower (esp. large models)	Faster and cheaper	Fast: rule filters can pre-select relevant data; lightweight models score remaining cases efficiently
Flexibility	Can be used for many modalities (text/image/code/etc.) and unstructured data	Tuned to structured formats only	Highly adaptable: uses structured data but incorporates domain-specific logic and anomaly detection for broader insights

FoundationDx

Philadelphia, PA.

Phone: (267) 358-0984

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Unsupervised learning for Anomaly Detection

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