Mitchell.Hirsche: Mitchell.Hirsche moved page Documentation for the "AI Prediction" Node in Pathways to AI Prediction Node in Pathways

2025-03-05T22:44:12Z

Mitchell.Hirsche moved page Documentation for the "AI Prediction" Node in Pathways to AI Prediction Node in Pathways

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Revision as of 22:44, 5 March 2025

Mitchell.Hirsche: AI Predictions

2025-03-04T23:23:20Z

AI Predictions

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The '''AI Prediction''' node in FojiSoft Pathways is used to generate predictions based on a preconfigured AI model. This node allows workflows to integrate machine learning-based predictions seamlessly, supporting decision-making and automation in complex processes.
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=== '''Node Properties''' ===

==== '''1. Name''' ====

* '''Description''': A unique identifier for the AI Prediction node.
* '''Usage''': Provide a clear and descriptive name that reflects the purpose of the prediction (e.g., "Claim Approval Prediction" or "Patient Risk Assessment").

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==== '''2. Configuration''' ====

* '''Description''': Defines the AI model and its associated settings to be used for making predictions.
* '''Components''':
*# '''Model Selection''':
*#* Choose from preconfigured AI models available in Forge AI.
*#* Models may include classification, regression, clustering, or custom-trained models.
*# '''Input Mapping''':
*#* Map workflow variables or data inputs to the features required by the AI model.
*# '''Prediction Type''':
*#* Specify the type of prediction the AI model will perform (e.g., binary classification, multi-class classification, regression).

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==== '''3. Output Variable''' ====

* '''Description''': The variable name where the prediction results will be stored.
* '''Usage''': Define a unique variable name to store the output, such as claim_approval_status or predicted_risk_score.
* '''Example''':
** If the model predicts whether a claim will be approved:
*** '''Output Variable''': claim_approval
*** '''Prediction Output''': Approved or Rejected

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=== '''How It Works''' ===

# '''Input Configuration''':
#* Define the input features required by the AI model.
#* Ensure inputs are properly mapped to relevant workflow data (e.g., patient details, insurance policy, historical records).
# '''Model Prediction''':
#* The selected model processes the inputs and generates a prediction.
#* Predictions can include probabilities (e.g., likelihood of approval) or classifications (e.g., "Approved" vs. "Rejected").
# '''Output Handling''':
#* Store the prediction results in the specified '''Output Variable'''.
#* Use the results in downstream nodes for further actions, such as notifications or data updates.

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=== '''Example Usage''' ===

==== '''Scenario: Insurance Claim Approval Prediction''' ====

# '''Name''': Claim Approval Prediction
# '''Model Configuration''':
#* Selected Model: "ClaimApprovalModel"
#* Input Features:
#** claim_amount
#** policy_type
#** patient_age
#* Prediction Type: Binary Classification (Approved or Rejected)
# '''Output Variable''': claim_status

==== '''Workflow Process:''' ====

# Input claim details into the workflow.

The AI model predicts the likelihood of approval:

{

"claim_status": "Approved",

"confidence": 0.92

}

# Output variable claim_status is used in the next steps of the workflow to notify staff or proceed with claim submission.

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=== '''Best Practices''' ===

# '''Select the Right Model''':
#* Use models that are pre-trained for your specific task, or train a custom model using Forge AI.
# '''Map Inputs Correctly''':
#* Ensure input variables align with the model’s requirements for optimal performance.
# '''Define Clear Output Variables''':
#* Use meaningful variable names to make workflows easy to debug and maintain.
# '''Test the Workflow''':
#* Run test predictions to validate the model's outputs before deploying the workflow.

[[Category:ForgeAI]]
[[Category:Pathways]]

AI Prediction Node in Pathways - Revision history

Mitchell.Hirsche: Mitchell.Hirsche moved page Documentation for the "AI Prediction" Node in Pathways to AI Prediction Node in Pathways

Mitchell.Hirsche: AI Predictions