Exploratory and Interpretable Approach to Estimating Latent Health Risk Factors Without Using Domain Knowledge
The identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses. In recent years, health analyses based on neural network models have been applied widely. However, such analysis processes are blackbox and the results la...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2025-04-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2024.9020081 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850069245620125696 |
|---|---|
| author | Ruichen Cong Shoji Nishimura Atsushi Ogihara Qun Jin |
| author_facet | Ruichen Cong Shoji Nishimura Atsushi Ogihara Qun Jin |
| author_sort | Ruichen Cong |
| collection | DOAJ |
| description | The identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses. In recent years, health analyses based on neural network models have been applied widely. However, such analysis processes are blackbox and the results lack explainability. Some approaches by constructing a domain model may tackle these issues. However, domain knowledge from an expert is required. In this study, we propose an exploratory and interpretable approach to estimating latent health risk factors without relying on domain knowledge, in which feature selection and causal discovery are used to construct a domain model for uncovering complex relationships in health and medical data. An evaluation experiment conducted on two datasets by comparing the proposed approach with four baselines demonstrated that the proposed approach outperformed the baselines in terms of model fitness. Furthermore, the number of model parameters in our method was smaller than that in the baselines, which reduced model complexity. Moreover, the analysis process of the proposed approach was visible and explainable, which improved the interpretability of the analysis processes. |
| format | Article |
| id | doaj-art-471db1d04ee647dca9a12c2c5a0e8b57 |
| institution | DOAJ |
| issn | 2096-0654 2097-406X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-471db1d04ee647dca9a12c2c5a0e8b572025-08-20T02:47:49ZengTsinghua University PressBig Data Mining and Analytics2096-06542097-406X2025-04-018244745710.26599/BDMA.2024.9020081Exploratory and Interpretable Approach to Estimating Latent Health Risk Factors Without Using Domain KnowledgeRuichen Cong0Shoji Nishimura1Atsushi Ogihara2Qun Jin3Graduate School of Human Sciences, Waseda University, Tokorozawa 359-1192, JapanFaculty of Human Sciences, Waseda University, Tokorozawa 359-1192, JapanFaculty of Human Sciences, Waseda University, Tokorozawa 359-1192, JapanFaculty of Human Sciences, Waseda University, Tokorozawa 359-1192, JapanThe identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses. In recent years, health analyses based on neural network models have been applied widely. However, such analysis processes are blackbox and the results lack explainability. Some approaches by constructing a domain model may tackle these issues. However, domain knowledge from an expert is required. In this study, we propose an exploratory and interpretable approach to estimating latent health risk factors without relying on domain knowledge, in which feature selection and causal discovery are used to construct a domain model for uncovering complex relationships in health and medical data. An evaluation experiment conducted on two datasets by comparing the proposed approach with four baselines demonstrated that the proposed approach outperformed the baselines in terms of model fitness. Furthermore, the number of model parameters in our method was smaller than that in the baselines, which reduced model complexity. Moreover, the analysis process of the proposed approach was visible and explainable, which improved the interpretability of the analysis processes.https://www.sciopen.com/article/10.26599/BDMA.2024.9020081health data analysislatent factor explorationinterpretable approachhealth risk estimation |
| spellingShingle | Ruichen Cong Shoji Nishimura Atsushi Ogihara Qun Jin Exploratory and Interpretable Approach to Estimating Latent Health Risk Factors Without Using Domain Knowledge Big Data Mining and Analytics health data analysis latent factor exploration interpretable approach health risk estimation |
| title | Exploratory and Interpretable Approach to Estimating Latent Health Risk Factors Without Using Domain Knowledge |
| title_full | Exploratory and Interpretable Approach to Estimating Latent Health Risk Factors Without Using Domain Knowledge |
| title_fullStr | Exploratory and Interpretable Approach to Estimating Latent Health Risk Factors Without Using Domain Knowledge |
| title_full_unstemmed | Exploratory and Interpretable Approach to Estimating Latent Health Risk Factors Without Using Domain Knowledge |
| title_short | Exploratory and Interpretable Approach to Estimating Latent Health Risk Factors Without Using Domain Knowledge |
| title_sort | exploratory and interpretable approach to estimating latent health risk factors without using domain knowledge |
| topic | health data analysis latent factor exploration interpretable approach health risk estimation |
| url | https://www.sciopen.com/article/10.26599/BDMA.2024.9020081 |
| work_keys_str_mv | AT ruichencong exploratoryandinterpretableapproachtoestimatinglatenthealthriskfactorswithoutusingdomainknowledge AT shojinishimura exploratoryandinterpretableapproachtoestimatinglatenthealthriskfactorswithoutusingdomainknowledge AT atsushiogihara exploratoryandinterpretableapproachtoestimatinglatenthealthriskfactorswithoutusingdomainknowledge AT qunjin exploratoryandinterpretableapproachtoestimatinglatenthealthriskfactorswithoutusingdomainknowledge |