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Reasoning-Enhanced Rare-Event Prediction with Balanced Outcome Correction

Vitaly Bulgakov, Alexander Turchin

TL;DR

Rare-event prediction suffers from extreme class imbalance, where the positive class is scarce but has high costs. LPCORP addresses this with a two-stage approach: Stage 1 uses a reasoning-enabled, low-cost LLM to enrich textual predictions, and Stage 2 corrects the LLM output with a logistic regression model trained on TF-IDF features, calibrated to the prevalence. The method yields substantial gains in precision and recall and demonstrates meaningful cost reductions across real-world datasets (e.g., consumer finance, hospital readmission, and IHCA), including scenarios with over 50% cost savings when thresholds are optimized. The approach avoids resampling, preserves sample counts, and provides a practical framework for threshold tuning and deployment in diverse rare-event domains.

Abstract

Rare-event prediction is critical in domains such as healthcare, finance, reliability engineering, customer support, aviation safety, where positive outcomes are infrequent yet potentially catastrophic. Extreme class imbalance biases conventional models toward majority-class predictions, limiting recall, calibration, and operational usefulness. We propose LPCORP (Low-Prevalence CORrector for Prediction)*, a two-stage framework that combines reasoningenhanced prediction with confidence-based outcome correction. A reasoning model first produces enriched predictions from narrative inputs, after which a lightweight logistic-regression classifier evaluates and selectively corrects these outputs to mitigate prevalence-driven bias. We evaluate LPCORP on real-world datasets from medical and consumer service domains. The results show that this method transforms a highly imbalanced setting into a well-balanced one while preserving the original number of samples and without applying any resampling strategies. Test-set evaluation demonstrates substantially improved performance, particularly in precision, which is a known weakness in low-prevalence data. We further provide a costreduction analysis comparing the expenses associated with rare-event damage control without preventive measures to those incurred when low-cost, prediction-based preventive interventions are applied that showed more than 50% reduction in some cases. * Patent pending: U.S. Provisional 63/933,518, filed 8 December 2025.

Reasoning-Enhanced Rare-Event Prediction with Balanced Outcome Correction

TL;DR

Rare-event prediction suffers from extreme class imbalance, where the positive class is scarce but has high costs. LPCORP addresses this with a two-stage approach: Stage 1 uses a reasoning-enabled, low-cost LLM to enrich textual predictions, and Stage 2 corrects the LLM output with a logistic regression model trained on TF-IDF features, calibrated to the prevalence. The method yields substantial gains in precision and recall and demonstrates meaningful cost reductions across real-world datasets (e.g., consumer finance, hospital readmission, and IHCA), including scenarios with over 50% cost savings when thresholds are optimized. The approach avoids resampling, preserves sample counts, and provides a practical framework for threshold tuning and deployment in diverse rare-event domains.

Abstract

Rare-event prediction is critical in domains such as healthcare, finance, reliability engineering, customer support, aviation safety, where positive outcomes are infrequent yet potentially catastrophic. Extreme class imbalance biases conventional models toward majority-class predictions, limiting recall, calibration, and operational usefulness. We propose LPCORP (Low-Prevalence CORrector for Prediction)*, a two-stage framework that combines reasoningenhanced prediction with confidence-based outcome correction. A reasoning model first produces enriched predictions from narrative inputs, after which a lightweight logistic-regression classifier evaluates and selectively corrects these outputs to mitigate prevalence-driven bias. We evaluate LPCORP on real-world datasets from medical and consumer service domains. The results show that this method transforms a highly imbalanced setting into a well-balanced one while preserving the original number of samples and without applying any resampling strategies. Test-set evaluation demonstrates substantially improved performance, particularly in precision, which is a known weakness in low-prevalence data. We further provide a costreduction analysis comparing the expenses associated with rare-event damage control without preventive measures to those incurred when low-cost, prediction-based preventive interventions are applied that showed more than 50% reduction in some cases. * Patent pending: U.S. Provisional 63/933,518, filed 8 December 2025.
Paper Structure (21 sections, 24 equations, 12 figures, 1 table)

This paper contains 21 sections, 24 equations, 12 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related work
  3. Formalization of the Imbalance Challenge
  4. Two-stage method
  5. Stage 1: Reasoning and conclusion generation with a low-cost LLM
  6. Stage 2: Correction with conventional Machine Learning (ML) model
  7. Formalization and intuition of the method
  8. Examples
  9. Example 1. Consumer Finance Complaint (Monetary Relief Prediction)
  10. Example 2. Prediction of hospital readmission within 30 days
  11. Example 3. Timestamped IHCA
  12. Data Retrieval
  13. Notes selection for positive and negative cases
  14. Positive Admissions.
  15. Negative Admissions.
  16. ...and 6 more sections

Figures (12)

  • Figure 1: Fig 1. Heatmap to show how accuracy net improvement depends on TPR and TNR
  • Figure 2: Fig 2a. Consumer Finance Complaint. Probability threshold 0.7
  • Figure 3: Fig 2b. Consumer Finance Complaint. Probability threshold 0.5
  • Figure 4: Fig 3. Metric and Cost reduction vs Probability threshold for “Consumer Finance Complaint” example.
  • Figure 5: Fig 4a. Hospital readmission. Probability threshold 0.5
  • ...and 7 more figures