AWS prescriptive-guidance documentation change
Summary
Updated image paths and simplified section titles (e.g., 'Predicting patient re-admission likelihood' to 'Predicting patient re-admission').
Security assessment
Changes are limited to image path corrections and title shortening. No references to security controls, vulnerabilities, or data protection measures were added or modified in the content.
Diff
diff --git a/prescriptive-guidance/latest/rag-healthcare-use-cases/case-2.md b/prescriptive-guidance/latest/rag-healthcare-use-cases/case-2.md index 32e34ba8b..fceba25fe 100644 --- a//prescriptive-guidance/latest/rag-healthcare-use-cases/case-2.md +++ b//prescriptive-guidance/latest/rag-healthcare-use-cases/case-2.md @@ -32 +32 @@ The risk-scoring models quantify the inferences from the LLM into numerical scor - + @@ -47 +47 @@ Building this solution consists of the following steps: -## Step 1: Predicting patient outcomes by using a medical knowledge graph +## Step 1: Predicting patient outcomes @@ -66 +66 @@ The following image shows the sequential steps involved in fine-tuning an LLM in - + @@ -89 +89 @@ The diagram shows the following workflow: -## Step 2: Predicting patient behavior towards prescribed medications or treatments +## Step 2: Predicting patient behavior @@ -159 +159 @@ Fine-tuned LLMs can consume past prescription fulfillment data from the medical -## Step 3: Predicting patient re-admission likelihood +## Step 3: Predicting patient re-admission @@ -244 +244 @@ You can process these time-series events to predict the re-admission likelihood -## Step 4: Computing the hospital re-admission propensity score +## Step 4: Computing the propensity score @@ -271 +271 @@ The following diagram shows the workflow of a conversational AI agent that a cli - +