Predicting Pain: cliexa’s AI-Driven Insights into Lower Back Pain Outcomes Using Predictive Features and Emotional Wellbeing Metrics

The urgency behind personalized risk detection.

Healthcare analytics isn’t about collecting data anymore. It’s about activating insights to predict and prevent decline.

That brings us to this case study. After embedding patient-reported emotional and physical data into an AI framework, cliexa helped clinicians predict lower back pain trajectories up to two weeks in advance, enabling personalized interventions that were previously impossible.

Challenges in pain management.

Lower back pain (LBP), a leading chronic condition, is notoriously difficult to predict due to fluctuating physical, emotional, and lifestyle factors. In a six-month pilot at the University of Colorado, Boulder, cliexa tackled three major obstacles:

• Predicting pain levels accurately over time: Accurately predicting pain levels over time is challenging due to the fluctuating nature of pain, which can be influenced by various factors including physical activity, stress levels, and treatment adherence.
• Incorporating emotional and physical health metrics: Integrating both emotional and physical health metrics into pain management is crucial because pain perception is multifaceted, involving psychological and physiological components that need to be addressed for effective treatment.
• Developing personalized treatment plans based on predictive analytics: Creating personalized treatment plans based on predictive analytics involves using advanced data analysis to tailor interventions to individual patient needs, enhancing treatment efficacy and patient outcomes.

Our approach: the cliexa solution.

The methodology was designed to capture a holistic view of the patient’s experience and use it to forecast future pain.

1. 01

   Study design

   The analysis was based on data from a prospective study involving 65 adults with diagnosed chronic lower back pain (LBP). The study utilized two sets of predictive features, bodily expressions and feeling ratings, to forecast LBP patients’ pain levels two weeks in advance.

2. 02

   Data collection platform

   Participants used cliexa, our specialized mobile health platform, to complete two baseline assessments before any treatment began.

3. 03

   Model validation

   The predictive model was validated through a structured approach, including hyperparameter tuning and cross-validation techniques, ensuring robust and reliable predictions.

4. 04

   Key predictive inputs

   Over the assessment period, patients used the platform to report on two primary sets of data:

   1. Emotional “Feeling” Ratings: Quantified ratings for 11 distinct emotional states, such as stress, fatigue, and positive affect.
   2. Emotional Body Maps: Visual representations where patients mapped the physical location of these feelings onto a digital body form, which the case study refers to as “Bodily Expressions.”
5. 05

   Predictive goal

   The objective was to use this unique combination of emotional and somatic data, analyzed by our machine learning models, to accurately forecast a patient’s LBP intensity two weeks in the future.

6. 06

   Clinical result

   This high level of predictive accuracy enabled clinicians to make more informed decisions about patient care, tailoring interventions based on anticipated pain levels.

Real-world results.

Enhanced predictive accuracy

Improved patient engagement