The Pain Predictor is a machine learning clinical decision support tool that forecasts individual pain levels over the near term following medication administration.
It models both raw physiological pain and non-linear perceived pain based on patient-specific sensitivity. This version includes a dynamic drug simulator, personalisation, and clinician cognitive bias modelling.
The predictor merges statistical learning, patient-level personalisation, and cognitive bias detection into a unified approach.
The predictor predicts pain trajectories using personalised digital twins, modelling patient-specific baselines and response to common pain medications. It simulates perceived pain based on a non-linear transformation and includes a prescribing logic module that reflects clinician decision styles. Output is presented visually for clinical review and protocol optimisation.
Why Predicting Pain Matters
Pain is one of the most common yet subjective symptoms encountered in clinical practice. Its assessment often relies on patient self-reporting, which varies widely due to cognitive, cultural, psychological, and situational factors. Clinicians, in turn, make treatment decisions based on this inherently noisy input — sometimes undertreating pain, and at other times contributing to the overuse of opioids and other high-risk analgesics.
The Pain Predictor was developed to address three pressing problems in modern pain care:
- Lack of Objectivity in Pain Assessment
Most systems still rely on verbal pain scores or behavioural cues, which are poor proxies for physiological pain experience. The predictor introduces a digital twin model that simulates personalised pain trajectories using vital signs and medication profiles, enabling clinicians to anticipate rather than just react. - Medication Misuse and Variation in Prescribing
In the U.S. CDC reports that over 75,000 people died from opioid overdoses in 2022 attributable in part to poor pain management by physicians and lack of structured approaches to pain assessment. Studies also show large inter-clinician variability in pain medication prescribing, particularly in emergency departments and aged care. This predictor embeds cognitive bias simulation to detect and adjust for these prescribing tendencies, offering bias-aware drug selection tailored to patient physiology. - Lack of Simulation Tools for Treatment Optimisation
Before this predictor was developed, there were no tools allowing clinicians to simulate a patient’s likely pain response over time to different drug regimens. By modelling drug onset, duration, and rebound alongside patient sensitivity, this predictor allows for safer, more accurate and efficient medication planning.
This predictor’s application of machine learning to pain measurementtransforms pain prediction from a reactive, guesswork-driven task into a precision-guided, evidence-based clinical process. It empowers clinicians to treat pain more effectively, patients to receive more consistent care, and health systems to reduce risks and costs associated with both undertreatment and overprescribing.
How It Works
The Pain Predictor is composed of three linked modules:
1. Baseline and Pain Prediction Model
Pain is predicted using mathematical models of drug kinetics and pain perception. The system simulates raw pain reduction based on dosage timing and drug clearance, followed by non-linear mapping to perceived pain.
2. Personalisation and Digital Twins
Each patient is assigned a unique sensitivity parameter and baseline pain level. The system learns these parameters to simulate accurate individual pain trajectories.
3. Clinician Bias Modelling
Simulates prescribing behaviour based on typical cognitive biases (e.g. omission, over-reliance, aggressiveness). Bias-aware prescribing logic adapts medication selection according to patient profile. This is to capture the need for clinical review of pain medications over time and review prescribed pain medications as patient circumstances change. It is designed to avoid pain prescribing based on physician beliefs about pain awareness in individuals.
Visualisation and Outputs
A dashboard displays pain trajectories over time, side-by-side with medication cycles. Users can compare raw vs perceived pain, track medication response, and evaluate outcomes under different clinician strategies.
Results (from Synthetic Dataset Demonstration)
Simulations using 100 synthetic patients and 3 medication types show clear differentiation in pain reduction trajectories. Bias-aware prescribing achieves improved alignment between raw and perceived pain reduction over time.
Pain response and perception are well studied in the context of non-linear models. Cognitive bias modelling draws on clinical heuristics literature and behavioural decision theory.
For more information: Dr Mike Tremblay, mike_tremblay@skythunder.net www.skythunder.net