Academic

Modeling Patient Care Trajectories with Transformer Hawkes Processes

arXiv:2604.05844v1 Announce Type: new Abstract: Patient healthcare utilization consists of irregularly time-stamped events, such as outpatient visits, inpatient admissions, and emergency encounters, forming individualized care trajectories. Modeling these trajectories is crucial for understanding utilization patterns and predicting future care needs, but is challenging due to temporal irregularity and severe class imbalance. In this work, we build on the Transformer Hawkes Process framework to model patient trajectories in continuous time. By combining Transformer-based history encoding with Hawkes process dynamics, the model captures event dependencies and jointly predicts event type and time-to-event. To address extreme imbalance, we introduce an imbalance-aware training strategy using inverse square-root class weighting. This improves sensitivity to rare but clinically important events without altering the data distribution. Experiments on real-world data demonstrate improved perfo

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Saumya Pandey, Varun Chandola · · 1 min read · 6 views
#cs.LG #q-bio.QM

arXiv:2604.05844v1 Announce Type: new Abstract: Patient healthcare utilization consists of irregularly time-stamped events, such as outpatient visits, inpatient admissions, and emergency encounters, forming individualized care trajectories. Modeling these trajectories is crucial for understanding utilization patterns and predicting future care needs, but is challenging due to temporal irregularity and severe class imbalance. In this work, we build on the Transformer Hawkes Process framework to model patient trajectories in continuous time. By combining Transformer-based history encoding with Hawkes process dynamics, the model captures event dependencies and jointly predicts event type and time-to-event. To address extreme imbalance, we introduce an imbalance-aware training strategy using inverse square-root class weighting. This improves sensitivity to rare but clinically important events without altering the data distribution. Experiments on real-world data demonstrate improved performance and provide clinically meaningful insights for identifying high-risk patient populations.

Executive Summary

The article presents a novel framework for modeling patient care trajectories using a Transformer Hawkes Process (THP) model, which integrates Transformer-based history encoding with Hawkes process dynamics to capture event dependencies in continuous time. The model addresses two critical challenges in healthcare data: temporal irregularity and extreme class imbalance. By employing an imbalance-aware training strategy with inverse square-root class weighting, the approach enhances sensitivity to rare but clinically significant events without altering the underlying data distribution. Empirical evaluation on real-world datasets demonstrates superior performance and provides actionable insights for identifying high-risk patient populations, thereby advancing predictive analytics in healthcare.

Key Points

  • ▸ Proposes a Transformer Hawkes Process (THP) model to handle irregularly time-stamped patient care events, such as outpatient visits, inpatient admissions, and emergency encounters.
  • ▸ Introduces an imbalance-aware training strategy using inverse square-root class weighting to address severe class imbalance in healthcare data.
  • ▸ Demonstrates improved predictive performance and clinical interpretability, enabling the identification of high-risk patient populations.
  • ▸ Validates the model on real-world datasets, showcasing its practical utility in healthcare analytics.

Merits

Innovative Integration of THP Framework

The fusion of Transformer-based history encoding with Hawkes process dynamics is a novel and theoretically robust approach to modeling irregularly timed events in healthcare, offering superior performance over traditional methods.

Addressing Class Imbalance

The introduction of inverse square-root class weighting provides an effective solution to extreme class imbalance, a pervasive issue in healthcare datasets, without resorting to data resampling techniques that may distort intrinsic event distributions.

Clinical Relevance and Applicability

The model's ability to identify high-risk patient populations and provide clinically meaningful insights underscores its practical utility in real-world healthcare settings, aligning with the growing demand for precision medicine.

Empirical Validation

The experiments conducted on real-world datasets validate the model's effectiveness, demonstrating tangible improvements in predictive performance and interpretability.

Demerits

Generalizability Concerns

The study's reliance on specific datasets may limit the generalizability of the findings, as patient care trajectories can vary significantly across different healthcare systems, demographics, and geographic regions.

Computational Complexity

The Transformer Hawkes Process model, particularly with the added complexity of imbalance-aware training, may impose substantial computational demands, potentially limiting its scalability in resource-constrained environments.

Data Quality Dependence

The model's performance is highly dependent on the quality and completeness of the input data. Missing or noisy data could significantly undermine the accuracy and reliability of the predictions.

Ethical and Privacy Considerations

The use of patient data for predictive modeling raises ethical and privacy concerns, particularly regarding data anonymization, informed consent, and compliance with regulations such as HIPAA or GDPR.

Expert Commentary

This paper represents a significant advancement in the modeling of patient care trajectories, addressing longstanding challenges in healthcare analytics through the innovative integration of Transformer architectures with Hawkes processes. The authors' approach to mitigating class imbalance is particularly noteworthy, as it avoids the pitfalls of data resampling while improving sensitivity to rare events—a critical feature for identifying high-risk patients in clinical practice. The empirical validation on real-world datasets underscores the model's practical utility, though questions remain about its generalizability across diverse healthcare systems and populations. From a methodological standpoint, the THP framework offers a promising avenue for future research, particularly in exploring its applicability to other domains with irregular event data, such as fraud detection or social network analysis. However, the computational demands of the model and its dependence on high-quality data pose practical challenges that will need to be addressed for widespread adoption. Overall, this work makes a valuable contribution to the intersection of machine learning and healthcare, with implications that extend beyond the immediate scope of patient care trajectories.

Recommendations

  • ✓ Further validation of the THP model across diverse healthcare datasets and populations is essential to assess its generalizability and robustness, particularly in low-resource or underrepresented settings.
  • ✓ Collaboration between data scientists, clinicians, and policymakers should be encouraged to facilitate the integration of the THP framework into clinical workflows, ensuring that its predictions are actionable and aligned with real-world healthcare practices.
  • ✓ Future research should explore hybrid models that combine the THP framework with other machine learning techniques, such as reinforcement learning or causal inference, to enhance the model's predictive power and interpretability.
  • ✓ Efforts should be made to develop lightweight versions of the THP model to reduce computational complexity, making it more accessible for deployment in resource-constrained environments without sacrificing performance.
  • ✓ Ethical and regulatory frameworks should be established to guide the responsible use of AI in predictive healthcare modeling, with a focus on ensuring transparency, fairness, and patient privacy.

Sources

Original: arXiv - cs.LG

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