The Potentials and Challenges of Handling Missing Data in Multimodal Healthcare Data

Introduction

Multimodal data in healthcare integrates diverse sources, such as medical imaging, wearable sensor readings, genomic information, and electronic health records (EHRs), offering profound insights for accurate diagnoses and treatment. However, missing data remains a persistent hurdle in leveraging the full potential of multimodal datasets. Missing values, partial modalities, or absent modalities create biases, reduce model reliability, and complicate healthcare applications.

This post aims to outline key remarks and delve into the challenges of handling missing data in multimodal healthcare systems, shedding light on opportunities for innovation and addressing the complexities involved.

Key Remarks

1. Significance of Multimodal Data:
   Multimodal data amalgamates information from various sources, enabling comprehensive decision-making and improved healthcare outcomes. For instance, combining MRI and PET scans with EHR data enhances disease prediction and progression tracking.
2. Prevalence of Missing Data:
   Missingness arises from diverse factors, such as equipment failure, patient noncompliance (e.g., skipping surveys or procedures), or logistical issues in merging datasets. Missing values can be random (e.g., device errors), blockwise (e.g., complete modality absence), or systematic (e.g., prohibitive cost of PET scans).
3. Handling Approaches:
   Researchers employ several strategies to address missing data, including discarding incomplete records, imputing values with statistical or machine-learning methods, and utilizing fusion methods to leverage observed modalities. Intermediate fusion techniques dominate multimodal imputation research due to their balance between simplicity and performance.

Challenges of Handling Missing Data

1. Diverse Missing Mechanisms

Handling missing data necessitates identifying the mechanism driving the missingness:

• Missing Completely at Random (MCAR): Data missing independently of observed and unobserved variables.
• Missing at Random (MAR): Missingness influenced by observed variables.
• Missing Not at Random (MNAR): Missingness depends on unobserved data.

In healthcare, data are often MNAR—e.g., patients failing to report extreme glucose levels—making imputation challenging. Few studies incorporate prior clinical knowledge to address mechanism complexities, leaving room for advancements in model development.

2. Computational Complexity

Multimodal datasets often involve high-dimensional data across several modalities. Imputation methods relying on fusion strategies can be computationally expensive, particularly for large datasets. Many algorithms focus on handling two modalities, overlooking scenarios involving missing data across multiple modalities simultaneously.

3. Bias from Imputation Techniques

While imputation improves data completeness, it risks introducing bias that can alter predictive performance. For example, imputing values in chronic kidney disease (CKD) monitoring must maintain physiological validity to avoid misguiding treatment decisions. Ensuring both statistical accuracy and clinical relevance remains an unresolved challenge.

4. Explainability in Healthcare Models

Medical applications demand interpretable and transparent models. Missing data handling methods, especially those relying on deep learning, often operate as black boxes, complicating the verification of imputed values and their impact on healthcare decisions. Developing explainable AI for missing data remains a necessity for trustworthy applications.

5. Data Privacy and Ethics

The integration of multimodal healthcare data raises ethical concerns, including ensuring patient confidentiality, informed consent, and unbiased data use. Synchronizing heterogeneous data sources can be challenging due to differing formats, noise levels, and privacy restrictions. Algorithms must prioritize ethical standards while addressing these obstacles.

6. Metrics for Evaluating Imputation Quality

Imputation methods often prioritize statistical metrics, such as Mean Squared Error (MSE), which fail to capture the clinical plausibility of imputed values. Multimodal data necessitate robust evaluation criteria tailored to domain-specific needs—e.g., ensuring realistic ranges for imputed heart rate values.

7. Interdisciplinary Collaboration

Effective solutions for multimodal missing data demand collaboration between computer scientists and healthcare professionals. Misalignment in priorities and methodologies often hinders progress. Bridging this gap is critical for designing clinically relevant models that align with real-world needs.

Opportunities for Future Research

1. Fusion of Prior Knowledge: Incorporating clinical domain knowledge into imputation algorithms could enhance the reliability and plausibility of imputed values, addressing MNAR challenges.
2. Optimized Computational Approaches: Developing scalable algorithms for high-dimensional multimodal datasets to reduce computational overhead.
3. Explainable Models: Building interpretable machine learning frameworks for imputation, ensuring alignment with clinical decision-making requirements.
4. Ethical AI Development: Exploring methods to integrate privacy-preserving techniques and mitigate demographic biases in multimodal data integration.
5. Advanced Metrics: Creating domain-specific evaluation metrics that balance statistical performance and medical plausibility.
6. Interdisciplinary Focus: Promoting collaboration between healthcare experts and data scientists to align technological innovation with clinical priorities.

To be more detailed:

1. Incorporating Prior Domain Knowledge

Fusing clinical expertise into imputation models is essential for improving the plausibility of imputed values. For instance, in diabetic care, data about glucose level fluctuations can guide imputation to ensure physiological accuracy, avoiding unrealistic values. Research can focus on developing hybrid models that combine statistical techniques with domain-specific physiological rules to enhance the reliability of imputation strategies. This will address the issue of Missing Not at Random (MNAR) data effectively and pave the way for clinically meaningful solutions.

2. Scaling to Multi-Modality with Advanced Fusion

Most existing approaches address missing data in one or two modalities; however, real-world datasets often involve a mix of imaging, genomics, time-series, and clinical records. Future efforts can focus on scalable intermediate fusion strategies capable of handling multiple modalities simultaneously. Research can also explore creating modular fusion systems where missingness in one modality does not compromise the integration process. Such solutions may involve hierarchical imputation frameworks that adapt dynamically to the availability of modalities.

3. Enhancing Computational Efficiency

Handling multimodal missing data for high-dimensional datasets can be computationally intensive, especially with deep learning methods like GANs and AutoEncoders. Innovative approaches are needed to optimize computational complexity—such as employing lightweight transformer architectures, parallelized imputation techniques, or decentralized/federated learning to process large-scale multimodal data efficiently. Future work could explore algorithms tailored to operate under constrained computational environments, benefiting resource-limited healthcare systems.

4. Expanding Applications to Non-Mental Health Domains

Many studies focus on mental health datasets (e.g., Alzheimer’s or Parkinson’s disease), leaving other healthcare domains relatively underexplored. Future research could target applications in cardiovascular diseases, respiratory disorders, or pediatric care. Multimodal datasets involving wearable data, environmental factors, and genetic profiles for these areas can yield new insights while addressing disease-specific challenges in missing data imputation.

5. Emphasis on Model Explainability

Deep learning methods used for imputation are often black boxes, raising concerns about trustworthiness in clinical applications. Future research should prioritize explainable models that allow clinicians to understand the logic behind imputed values and how missing data impacts predictions. Explainable AI frameworks can integrate visualization tools for imputed data, ensuring healthcare providers can verify imputed values align with medical plausibility.

6. Improving Ethical Standards and Privacy

Healthcare data is sensitive, and integrating multimodal datasets raises significant privacy risks. Future efforts should focus on privacy-preserving techniques for multimodal data integration and imputation. For example, federated learning frameworks allow decentralized analysis without compromising patient confidentiality. Research can also explore methods to reduce biases introduced by integrating multimodal data while maintaining ethical data use standards.

7. Developing Advanced Metrics

Current evaluation metrics like Mean Squared Error (MSE) are insufficient for healthcare applications as they do not account for clinical plausibility. Future studies can focus on creating domain-specific metrics tailored to healthcare data. For example, metrics could ensure imputed heart rates remain within physiological ranges or verify correlations between modalities (e.g., imaging and lab results) after imputation. Incorporating expert feedback to validate these metrics will ensure they are meaningful in clinical contexts.

8. Interdisciplinary Collaboration

Interdisciplinary partnerships between healthcare practitioners and computer scientists must be strengthened to ensure practical and impactful solutions. Collaborative initiatives can focus on identifying real-world healthcare challenges (e.g., prioritizing clinically relevant data points in imputation) and aligning technological innovations with clinical needs. Future work should emphasize frameworks for co-designing multimodal solutions, bridging the gap between computational approaches and medical workflows.

9. Addressing Uncertainty in Missing Data

Future research can explore uncertainty quantification methods during imputation. These approaches can assign confidence scores to imputed values, allowing clinicians to identify areas requiring caution. For example, Monte Carlo Dropout techniques or probabilistic models can gauge the reliability of imputed values, dynamically weighting reliable modalities during data fusion.

Conclusion

Handling missing data in multimodal healthcare systems is a multifaceted challenge requiring innovative solutions, computational expertise, and interdisciplinary collaboration. Addressing these challenges can unlock the potential of multimodal data to revolutionize healthcare, enhancing accuracy, reliability, and patient outcomes.

Read more:

Multimodal Missing Data in Healthcare: A Comprehensive Review and Future Directions by Lien P. Le, Thu Nguyen, Michael A. Riegler, Pål Halvorsen, Binh T. Nguyen