ReviewiScience2020
Integrating Machine Learning with Human Knowledge.
Review in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis 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
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study.Journal of epidemiology and global health · 2024Pooled it
- Hybrid machine learning approach for landslide prediction, Uttarakhand, India.Scientific reports · 2022Trial
- Immunotherapy drug target identification using machine learning and patient-derived tumour explant validation.Nature machine intelligence · 2026Article
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion, Crown, Restoration, Dental Caries) in Intraoral Periapical Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring.Nature biomedical engineering · 2025Review
- Precision Rehabilitation After Youth Anterior Cruciate Ligament Reconstruction: Individualized Reinjury Risk Stratification and Modifiable Risk Factor Identification to Guide Late-Phase Rehabilitation.Orthopaedic journal of sports medicine · 2025Article
- Revolutionizing ESCC prognosis: the efficiency of tumor-infiltrating immune cells (TIIC) signature score.Discover oncology · 2025Article
- Deep learning application in prediction of cancer molecular alterations based on pathological images: a bibliographic analysis via CiteSpace.Journal of cancer research and clinical oncology · 2024Article
- DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol.BMJ open respiratory research · 2024Article
- Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.BMC palliative care · 2024Article
- Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease.Cell reports. Medicine · 2024Review
- Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.Briefings in bioinformatics · 2024Article
- Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning - A Bibliometric Analysis.Current neuropharmacology · 2024Review
- Thick Data Analytics (TDA): An Iterative and Inductive Framework for Algorithmic Improvement.The American statistician · 2024Article
- Machine learning-based protein signatures for differentiating hypertensive disorders of pregnancy.Hypertension research : official journal of the Japanese Society of Hypertension · 2023Article
- Article
- Environmental insults and compensative responses: when microbiome meets cancer.Discover oncology · 2023Review
- Data-driven strategies for the computational design of enzyme thermal stability: trends, perspectives, and prospects.Acta biochimica et biophysica Sinica · 2023Review
- Machine learning versus crop growth models: an ally, not a rival.AoB PLANTS · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. This paper gives an overview of the knowledge and its representations that can be integrated into machine learning and the methodology. We cover the fundamentals, current status, and recent progress of the methods, with a focus on popular and new topics. The perspectives on future directions are also discussed.
Indexed as
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.