Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysis Article Swipe
Helen Coupland
,
Neil Scheidwasser
,
Alexandros Katsiferis
,
Megan Davies
,
Seth Flaxman
,
Naja Hulvej Rod
,
Swapnil Mishra
,
Samir Bhatt
,
H. Juliette T. Unwin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s12889-025-22705-4
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s12889-025-22705-4
Background Understanding the complex interplay between life course exposures, such as adverse childhood experiences and environmental factors, and disease risk is essential for developing effective public health interventions. Traditional epidemiological methods, such as regression models and risk scoring, are limited in their ability to capture the non-linear and temporally dynamic nature of these relationships. Deep learning (DL) and explainable artificial intelligence (XAI) are increasingly applied within healthcare settings to identify influential risk factors and enable personalised interventions. However, significant gaps remain in understanding their utility and limitations, especially for sparse longitudinal life course data and how the influential patterns identified using explainability are linked to underlying causal mechanisms. Methods We conducted a controlled simulation study to assess the performance of various state-of-the-art DL architectures including CNNs and (attention-based) RNNs against XGBoost and logistic regression. Input data was simulated to reflect a generic and generalisable scenario with different rules used to generate multiple realistic outcomes based upon epidemiological concepts. Multiple metrics were used to assess model performance in the presence of class imbalance and SHAP values were calculated. Results We find that DL methods can accurately detect dynamic relationships that baseline linear models and tree-based methods cannot. However, there is no one model that consistently outperforms the others across all scenarios. We further identify the superior performance of DL models in handling sparse feature availability over time compared to traditional machine learning approaches. Additionally, we examine the interpretability provided by SHAP values, demonstrating that these explanations often misalign with causal relationships, despite excellent predictive and calibrative performance. Conclusions These insights provide a foundation for future research applying DL and XAI to life course data, highlighting the challenges associated with sparse healthcare data, and the critical need for advancing interpretability frameworks in personalised public health.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12889-025-22705-4
- https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-025-22705-4
- OA Status
- gold
- Cited By
- 6
- References
- 64
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409733048
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4409733048Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12889-025-22705-4Digital Object Identifier
- Title
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Exploring the potential and limitations of deep learning and explainable AI for longitudinal life course analysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-24Full publication date if available
- Authors
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Helen Coupland, Neil Scheidwasser, Alexandros Katsiferis, Megan Davies, Seth Flaxman, Naja Hulvej Rod, Swapnil Mishra, Samir Bhatt, H. Juliette T. UnwinList of authors in order
- Landing page
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https://doi.org/10.1186/s12889-025-22705-4Publisher landing page
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https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-025-22705-4Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://bmcpublichealth.biomedcentral.com/counter/pdf/10.1186/s12889-025-22705-4Direct OA link when available
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Biostatistics, Medicine, Course (navigation), Life course approach, Public health, Epidemiology, Gerontology, Medical education, Pathology, Developmental psychology, Psychology, Physics, AstronomyTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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64Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.simulated | 138 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.accurately | 185 |
| abstract_inverted_index.artificial | 60 |
| abstract_inverted_index.associated | 277 |
| abstract_inverted_index.challenges | 276 |
| abstract_inverted_index.controlled | 113 |
| abstract_inverted_index.developing | 24 |
| abstract_inverted_index.especially | 88 |
| abstract_inverted_index.exposures, | 9 |
| abstract_inverted_index.foundation | 262 |
| abstract_inverted_index.frameworks | 289 |
| abstract_inverted_index.healthcare | 67, 280 |
| abstract_inverted_index.identified | 100 |
| abstract_inverted_index.non-linear | 47 |
| abstract_inverted_index.predictive | 253 |
| abstract_inverted_index.regression | 34 |
| abstract_inverted_index.scenarios. | 210 |
| abstract_inverted_index.simulation | 114 |
| abstract_inverted_index.temporally | 49 |
| abstract_inverted_index.tree-based | 194 |
| abstract_inverted_index.underlying | 106 |
| abstract_inverted_index.Conclusions | 257 |
| abstract_inverted_index.Traditional | 29 |
| abstract_inverted_index.approaches. | 232 |
| abstract_inverted_index.calculated. | 177 |
| abstract_inverted_index.calibrative | 255 |
| abstract_inverted_index.experiences | 14 |
| abstract_inverted_index.explainable | 59 |
| abstract_inverted_index.influential | 71, 98 |
| abstract_inverted_index.mechanisms. | 108 |
| abstract_inverted_index.outperforms | 205 |
| abstract_inverted_index.performance | 119, 166, 216 |
| abstract_inverted_index.regression. | 134 |
| abstract_inverted_index.significant | 79 |
| abstract_inverted_index.traditional | 229 |
| abstract_inverted_index.availability | 224 |
| abstract_inverted_index.consistently | 204 |
| abstract_inverted_index.explanations | 245 |
| abstract_inverted_index.highlighting | 274 |
| abstract_inverted_index.increasingly | 64 |
| abstract_inverted_index.intelligence | 61 |
| abstract_inverted_index.limitations, | 87 |
| abstract_inverted_index.longitudinal | 91 |
| abstract_inverted_index.performance. | 256 |
| abstract_inverted_index.personalised | 76, 291 |
| abstract_inverted_index.Additionally, | 233 |
| abstract_inverted_index.Understanding | 2 |
| abstract_inverted_index.architectures | 124 |
| abstract_inverted_index.demonstrating | 242 |
| abstract_inverted_index.environmental | 16 |
| abstract_inverted_index.generalisable | 144 |
| abstract_inverted_index.relationships | 188 |
| abstract_inverted_index.understanding | 83 |
| abstract_inverted_index.explainability | 102 |
| abstract_inverted_index.interventions. | 28, 77 |
| abstract_inverted_index.relationships, | 250 |
| abstract_inverted_index.relationships. | 54 |
| abstract_inverted_index.epidemiological | 30, 157 |
| abstract_inverted_index.interpretability | 237, 288 |
| abstract_inverted_index.state-of-the-art | 122 |
| abstract_inverted_index.(attention-based) | 128 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.99422719 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |