Numerical Analysis
Review structured records, prediction targets, accuracy measures, and the difference between a language baseline and a numerical model.
Structured data · prediction · precision
Numerical analysis starter
Start with the data, then choose the decision.
Use the resource spaces below to understand the numerical signal, the patient-risk context, and the operational solution it can support.
Patient Research Space
Explore readmission-risk and sepsis-onset use cases as decision-support research areas. Patient data requires clinical review, governance, and validation.
Risk signals · clinical review · interventionSolution Space
Translate numerical signals into practical actions such as follow-up prioritization, resource planning, inventory decisions, and financial risk review.
Signal → decision → measurable outcome
Reference medical benchmarks
Accuracy graph, shown directly
This graph shows the currently published internal comparison for healthcare and other structured-data use cases. It compares ChatGPT (GPT-4o) with tuned XGBoost / Random Forest baselines. It is not a clinical validation study, and Sriya.AI's per-row LNM results are not yet published.
ChatGPT (GPT-4o)
XGBoost / Random Forest
Source: internal case-study benchmarking cited in Sriya.AI materials. The Sriya.AI LNM column was cropped in the source document this page was generated from — company materials state a 20–40% accuracy lift with 100% precision over standard ML on comparable tasks; plug in your verified per-row LNM figures here once available.