ReviewPatterns (New York, N.Y.)2024
Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.
Review in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Benchmarking Multimodal Deep Fusion Strategies for Heterogeneous Neuroimaging and Cognitive Data Using a Controlled Sex Classification Task.Brain sciences · 2026Article
- Host-microbiome interactions in leukemia: mechanisms, treatment response, and clinical implications.Frontiers in cellular and infection microbiology · 2026Review
- Evoked potentials in stroke rehabilitation: current applications, emerging technologies, and future directions.Frontiers in neuroscience · 2026Review
- Computer models and artificial intelligence increase the fidelity and efficiency of the in vitro models for hearing loss.Biomedical engineering online · 2025Review
- Application of machine learning for the analysis of peripheral blood biomarkers in oral mucosal diseases: a cross-sectional study.BMC oral health · 2025Article
- Transfer Learning with Clinical Concept Embeddings from Large Language Models.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Primary Care Informatics: Vitalizing the Bedrock of Health Care.Journal of medical Internet research · 2024Article
- Review
- A practical guide to FAIR data management in the age of multi-OMICS and AI.Frontiers in immunology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C
Identifiers
What Socratic 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 Socratic graph.