# Balancing Predictive Performance and Explainability for Integrating Clinical, Biological, and Radiological Data in the Basque Country COVID-19 Cohort

Lander Rodriguez-Idiazabal, Marta Avalos, Dylan Russon, Irantzu Barrio, Jose M. Quintana-Lopez

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Lander Rodriguez-Idiazabal, Marta Avalos, Dylan Russon, Irantzu Barrio, Jose M. Quintana-Lopez. Balancing Predictive Performance and Explainability for Integrating Clinical, Biological, and Radiological Data in the Basque Country COVID-19 Cohort. ISI 2025 - 65th ISI World Statistics Congress, International Statistical Institute, Oct 2025, Hague, Netherlands. hal-05385401

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Distributed under a Creative Commons Attribution 4.0 International License
# Balancing Predictive Performance and Explainability for Integrating Clinical, Biological, and Radiological Data in the Basque Country COVID-19 Cohort

Lander RODRIGUEZ-IDIAZABAL, Irantzu BARRIO, Jose Maria QUINTANA-LOPEZ

Univ Pais Vasco (SP)

Marta AVALOS-FERNANDEZ, Dylan RUSSON

Univ of Bordeaux (FR)

<figure><img src="image_2.png" /></figure>

## 65th ISI World Statistics Congress

7 Oct 2025, The Hague, the Netherlands
* Modern **healthcare** routinely generates massive, heterogeneous data: EHRs, Biological data, X-ray, Clinical notes, Treatments,...

Need for:

* **BALANCE** → Predictive power VS Explanaibility

- **Accurate** → To usefully guide decisions

- **Explainable** → Clinicians need transparent models to gain trust

* **INTEGRATION** of multiple data sources and multimodal data → Efficient
# The Basque Country COVID-19 Cohort

Adult patients diagnosed with COVID-19 and admitted to Galdakao-Usansolo Hospital's ED between March 2020 and January 2022 (N = 5,504)

## Tabular data:

- Sociodemographic (3 variables): age, sex and vaccination status

- Comorbidities (13 variables): Charlson index, and presence of comorbidities (heart failure, dementia, diabetes...)

- Laboratory (22 variables): Partial CO₂ and O₂ pressure, glucose...

- Image data: **RAW & UNLABELED** Frontal X-ray image of lungs

- **Outcome**: mortality status

<figure><img src="image_3.png" /></figure>

[Quintana-Lopez, Rodríguez-Idiazabal, 2024]
No longer in the urgency of developing predictive models for COVID-19 mortality
Rich data, valuable opportunity to develop/test methods and integration techniques

- **Challenges:**

* **Missing** chest X-ray → 965 / 5,504

* **Unlabeled** chest X-ray images → No reports, No class labels, No expert feedback

* ~40 mostly continuous tabular → Potentially **non-linear** relationships

* **Imbalanced** outcome classes → <10% mortality rate

* **Trade off** → Predictive performance vs. Explainability

- **Objective:** To investigate the **predictive performance** of COVID-19 death models

* **Single and multimodal** approaches using chest X-ray images and EHR data

* Models differ in their **explainability capacities**
# METHODS: Model architectures (1st attempt) - clustering

**Categorical (Tabular) Variable Representing X-ray Information:**

* Gaussian Mixture Variational Autoencoders with a Convolutional Neural Network architecture to classify frontal chest X-ray images into **clusters**

* Patients without X-ray images were assigned to a **"missing value" category**

**Aim:**

* To **facilitate data integration** across modalities

* To leverage the **full cohort** of >5,000 patients

**Naïve approach:** Clustering chest X-ray images of COVID-19 patients **without prior clinical knowledge** is neither predictive nor explainable
METHODS: Model architectures – single modality

* **EHR-only** (Threshold): **Cost-sensitive Lasso logistic regression** with **0-degree spline** to categorize variables for clinical interpretation

* **Image-only:** **Adapted CheXNet architecture** replacing its final classification layer with a task-specific set of fully connected (dense) layers

<figure><img src="image_4.png" /></figure>
# METHODS: Model architectures – single modality

* **EHR-only (Threshold): Cost-sensitive Lasso logistic regression** with **0-degree spline** to categorize variables for clinical interpretation

$$ \max_{\beta} \left\{ \sum_{i=1}^{n} \omega_i \ln \frac{e^{y_i \sum_{j=1}^{p} \chi_{ij} \beta_j}}{1 + e^{\sum_{j=1}^{p} \chi_{ij} \beta_j}} - \lambda \|\beta\|_1 \right\} $$

With $X = \{x_{ij}\}, i = 1, \dots, n; j = 1, \dots, p$ and $q_{kj}$ the k-th percentile of $X_j, k = 1, \dots, k_j$

$$ \chi_{ik}^j = \begin{cases} 1 & \text{if } x_{ij} > q_{kj} \\ 0 & \text{otherwise.} \end{cases} $$

$$ \beta^j \in \mathbb{R}^{kj} \quad \text{and} \quad \|\beta\|_1 = \sum_{j=1}^{p} \|\beta^j\|_1 = \sum_{j=1}^{p} \sum_{k=1}^{k_j} |\beta_k^j| $$

$$ \omega_i = \begin{cases} \omega & \text{if } y_i = 1 \\ 1 & \text{otherwise.} \end{cases} $$

<figure><img src="image_5.png" /></figure>

[Avalos, 2021]
# METHODS: Model architectures – single modality

* **Image-only: Adapted CheXNet architecture** replacing its final classification layer with a task-specific set of fully connected (dense) layers

**CheXNet Algorithm:**

* Predicts the **probability of 14 disease categories** from chest X-rays

* Detects pneumonia at a level exceeding practicing radiologists

* 121-layer Convolutional Neural Network (CNN)

* Trained on >100,000 **frontal-view chest X-ray images** across 14 disease categories (ChestX-ray14 dataset)

* **Transfert learning:** pre-trained CheXNet adapted to predict

* The probability of 14 disease categories or

* The death
  in our COVID-19 data

<figure><img src="image_6.png" /><figcaption>Input
Chest X-Ray Image</figcaption></figure>

<caption>CheXNet
121-layer CNN</caption>

<figure><img src="image_7.png" /><figcaption>Output
Pneumonia Positive (85%)</figcaption></figure>

[Rajpurkar, 2017]
# METHODS: Model architectures – multimodal

* **Disease concatenation:** Cost-sensitive Lasso with 0-splines applied to EHR data + 14 disease probability outputs from (unmodified) CheXNet

* **Late fusion:** Predicted probabilities from single modality models are inputs to:
1. A set of fully connected networks (NN) or
2. A logistic regression (LR)

<figure><img src="image_8.png" /></figure>
* **Train** (60%), **validation** (20%), and **test** (20%) **stratified** split of the data

* **Predictive performance** assessed on the **test set**:

* Area Under the Receiver Operating Characteristic Curve (AUROC)

* Area Under the Precision-Recall Curve (AUPRC)

* **95% CIs:** bootstrap resampling with 1000 replicates
RESULTS: Descriptive statistics

<table><thead><tr><th>Variable</th><th>Total sample</th><th>Non-deceased</th><th>Deceased</th><th>p-value</th></tr></thead><tbody><tr><td>N, (%)</td><td>965 (100%)</td><td>901 (93.37%)</td><td>64 (6.33%)</td><td></td></tr><tr><td><em>Sociodemographic variables</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Age</td><td>63 [54-72]</td><td>62 [53-71]</td><td>78 [69-85]</td><td>&lt;0.05</td></tr><tr><td>Charlson Index</td><td>1 [0-2]</td><td>1 [0-2]</td><td>3 [1-5]</td><td>&lt;0.05</td></tr><tr><td>Sex at birth</td><td></td><td></td><td></td><td>0.108</td></tr><tr><td>Female</td><td>322 (33.4%)</td><td>307 (34.1%)</td><td>15 (23.4%)</td><td></td></tr><tr><td>Male</td><td>643 (66.6%)</td><td>594 (65.9%)</td><td>49 (76.6%)</td><td></td></tr><tr><td><em>Comorbidities</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Heart failure</td><td>91 (9.4%)</td><td>69 (7.7%)</td><td>22 (34.4%)</td><td>&lt;0.05</td></tr><tr><td>Cerebrovascular disease</td><td>102 (10.6%)</td><td>82 (9.1%)</td><td>20 (31.2%)</td><td>&lt;0.05</td></tr><tr><td>Arterial hypertension</td><td>465 (48.2%)</td><td>422 (46.8%)</td><td>43 (67.2%)</td><td>&lt;0.05</td></tr><tr><td>Dementia</td><td>17 (1.8%)</td><td>9 (1%)</td><td>8 (12.5%)</td><td>&lt;0.05</td></tr><tr><td>Diabetes</td><td></td><td></td><td></td><td>&lt;0.05</td></tr><tr><td>No</td><td>764 (79.2%)</td><td>724 (80.4%)</td><td>40 (62.5%)</td><td></td></tr><tr><td>Without chronic complications</td><td>163 (16.9%)</td><td>144 (16%)</td><td>19 (29.7%)</td><td></td></tr><tr><td>With chronic complications</td><td>38 (3.9%)</td><td>33 (3.7%)</td><td>5 (7.8%)</td><td></td></tr><tr><td>Kidney disease</td><td>128 (13.3%)</td><td>108 (12%)</td><td>20 (31.2%)</td><td>&lt;0.05</td></tr><tr><td>Cancer</td><td>54 (5.6%)</td><td>48 (5.3%)</td><td>6 (9.4%)</td><td>0.28</td></tr></tbody></table>
RESULTS: EHR-only model

<figure><img src="image_9.png" /></figure>
# RESULTS: Models performance

## Late Fusion Approaches:

- Showed a **higher AUPRC** point estimate compared to single modality models

- However, variability was high (**wide 95% CIs**)

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>
* **Integrating multimodal** data improves prediction - but also increases complexity

* **New open-source deep learning methods for medical imaging** offer exciting opportunities

* **Simpler models (e.g., 0-degree splines)** are well understood and mimic **clinical reasoning**

* **Methods developed here can be extended** beyond COVID-19 to other diseases

* **Balancing accuracy and transparency** remains the central challenge
[Quintana-Lopez, Rodríguez-Idiazabal, 2024]

Quintana-Lopez, Rodríguez-Idiazabal, Portuondo, García, Legarreta, Gascón, Larrea, Barrio & COVID-Health Basque Country Research Group (2024). Relevance of comorbidities for main outcomes during different periods of the COVID-19 pandemic. *Influenza and other respiratory viruses* https://doi.org/10.1111/irv.13240

[Avalos, 2021]

Avalos, Touchais, Henríquez-Henríquez (2021). Optimising Criteria for Manual Smear Review Following Automated Blood Count Analysis: A Machine Learning Approach. *In: Advances in Intelligent Systems and Computing, vol 1372*. Springer https://doi.org/10.1007/978-3-030-73603-3_35

[Rajpurkar, 2017]

Rajpurkar, Irvin, Zhu, Yang, Mehta, Duan, Ding, Bagul, Langlotz, Shpanskaya, Lungren, & Ng (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning [Preprint]. arXiv https://doi.org/10.48550/arXiv.1711.05225