ArticlePLoS computational biology2020
Learning clinical networks from medical records based on information estimates in mixed-type data.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Clonal memory of cell division in humans diverges between healthy haematopoiesis and acute myeloid leukaemia.Nature communications · 2026Article
- A New Mutual Information Estimator for Continuous Censored Variables.Entropy (Basel, Switzerland) · 2026Article
- CausalCCC: a web server to explore intracellular causal pathways enabling cell-cell communication.Nucleic acids research · 2025Article
- Preserving information while respecting privacy through an information theoretic framework for synthetic health data generation.NPJ digital medicine · 2025Article
- CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data.eLife · 2025Article
- Topological Structures in the Space of Treatment-Naïve Patients with Chronic Lymphocytic Leukemia.Cancers · 2024Article
- Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients.iScience · 2024Article
- A Conditional Mutual Information Estimator for Mixed Data and an Associated Conditional Independence Test.Entropy (Basel, Switzerland) · 2022Article
- Interactive exploration of a global clinical network from a large breast cancer cohort.NPJ digital medicine · 2022Article
- Inferring Gene Networks in Bone Marrow Hematopoietic Stem Cell-Supporting Stromal Niche Populations.iScience · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The precise diagnostics of complex diseases require to integrate a large amount of information from heterogeneous clinical and biomedical data, whose direct and indirect interdependences are notoriously difficult to assess. To this end, we propose an efficient computational approach to simultaneously compute and assess the significance of multivariate information between any combination of mixed-type (continuous/categorical) variables. The method is then used to uncover direct, indirect and possibly causal relationships between mixed-type data from medical records, by extending a recent machine learning method to reconstruct graphical models beyond simple categorical datasets. The method is shown to outperform existing tools on benchmark mixed-type datasets, before being applied to analyze the medical records of eldery patients with cognitive disorders from La Pitié-Salpêtrière Hospital, Paris. The resulting clinical network visually captures the global interdependences in these medical records and some facets of clinical diagnosis practice, without specific hypothesis nor prior knowledge on any clinically relevant information. In particular, it provides some physiological insights linking the consequence of cerebrovascular accidents to the atrophy of important brain structures associated to cognitive impairment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.