ArticleBehavioral sciences (Basel, Switzerland)2019
A Machine Learning Approach for Studying the Comorbidities of Complex Diagnoses.
Article in Behavioral sciences (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Detecting comorbidity patterns in rare disease patients with machine learning.Frontiers in epidemiology · 2026Article
- Compositional variation in eye-infiltrating immune cells distinguishes human uveitis subtypes.iScience · 2025Article
- Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal.Health information management : journal of the Health Information Management Association of Australia · 2024Article
- Identifying Novel Subtypes of Functional Gastrointestinal Disorder by Analyzing Nonlinear Structure in Integrative Biopsychosocial Questionnaire Data.Journal of clinical medicine · 2024Article
- Predicting Multimorbidity Using Saudi Health Indicators (Sharik) Nationwide Data: Statistical and Machine Learning Approach.Healthcare (Basel, Switzerland) · 2023Article
- Subtypes of the psychedelic experience have reproducible and predictable effects on depression and anxiety symptoms.Journal of affective disorders · 2023Article
- An unsupervised machine learning approach using passive movement data to understand depression and schizophrenia.Journal of affective disorders · 2022Article
- Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.Journal of personalized medicine · 2022Review
- Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis.Frontiers in bioengineering and biotechnology · 2022Article
- Review
- Nucleus Accumbens Chemogenetic Inhibition Suppresses Amphetamine-Induced Ultrasonic Vocalizations in Male and Female Rats.Brain sciences · 2021Article
- Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper.Journal of medical Internet research · 2020Article
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2 authors.
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Abstract
The study of diagnostic associations entails a large number of methodological problems regarding the application of machine learning algorithms, collinearity and wide variability being some of the most prominent ones. To overcome these, we propose and tested the usage of uniform manifold approximation and projection (UMAP), a very recent, popular dimensionality reduction technique. We showed its effectiveness by using it on a large Spanish clinical database of patients diagnosed with depression, to whom we applied UMAP before grouping them using a hierarchical agglomerative cluster analysis. By extensively studying its behavior and results, validating them with purely unsupervised metrics, we show that they are consistent with well-known relationships, which validates the applicability of UMAP to advance the study of comorbidities.
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