ReviewNPJ digital medicine2021
Digital medicine and the curse of dimensionality.
Review in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
134 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Applications of digital health technologies and artificial intelligence algorithms in COPD: systematic review.BMC medical informatics and decision making · 2025Pooled it
- Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- Diagnostic prediction models to identify patients at risk for healthcare-facility-onsetInfection control and hospital epidemiology · 2024Pooled it
- Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2023Pooled it
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Article
- Quantitative imaging analysis in hypertrophic cardiomyopathy: phenotypic differentiation to prognostic stratification.European heart journal. Imaging methods and practice · 2026Review
- Understanding the influence of training schedules on short-term load forecasting via deep residual networks: An empirical study.Scientific reports · 2026Article
- Multi-omics data integration using time-to event endpoint and supervised Cox penalized regression: a comprehensive review.Clinical and experimental medicine · 2026Review
- DNA methylation biomarkers-based pan-cancer classifier: predictive modeling for cancer classification.Genome medicine · 2026Article
- Immortal AI, Mortal Life: Long-Term Perspectives on AI and Human Knowledge.Journal of medical Internet research · 2026Article
- A simulation-based deep learning framework for spatially explicit malaria modeling of CRISPR suppression gene drive mosquitoes.iScience · 2026Article
- Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.Scientific reports · 2026Article
- Vocal Markers of Schizophrenia: Assessing the Generalizability of Machine Learning Models and Their Clinical Applicability.Schizophrenia bulletin · 2026Article
- Exploring the Predictors of Nurses' Turnover Intentions Through Neural Network Modeling: A National Cross-Sectional Study in Lithuania.Healthcare (Basel, Switzerland) · 2026Article
- Prediction of Aspiration Risk by Using Vocal Biomarkers: Machine Learning Development and Validation Study.JMIR formative research · 2026Article
- Clinical psychopathology-based early relapse prediction model using speech and language in psychosis.Schizophrenia research. Cognition · 2026Review
- A novel approach to preeclampsia early prediction addressing predictive uncertainty due to missing data in clinical dataset.Scientific reports · 2026Article
- An explainable and transparent machine learning approach for predicting dental caries: a cross-national validation study.BMC oral health · 2026Article
74 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Digital health data are multimodal and high-dimensional. A patient's health state can be characterized by a multitude of signals including medical imaging, clinical variables, genome sequencing, conversations between clinicians and patients, and continuous signals from wearables, among others. This high volume, personalized data stream aggregated over patients' lives has spurred interest in developing new artificial intelligence (AI) models for higher-precision diagnosis, prognosis, and tracking. While the promise of these algorithms is undeniable, their dissemination and adoption have been slow, owing partially to unpredictable AI model performance once deployed in the real world. We posit that one of the rate-limiting factors in developing algorithms that generalize to real-world scenarios is the very attribute that makes the data exciting-their high-dimensional nature. This paper considers how the large number of features in vast digital health data can challenge the development of robust AI models-a phenomenon known as "the curse of dimensionality" in statistical learning theory. We provide an overview of the curse of dimensionality in the context of digital health, demonstrate how it can negatively impact out-of-sample performance, and highlight important considerations for researchers and algorithm designers.
Identifiers
What OpenQuestion holds
Registered trials
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.