ReviewAmerican journal of epidemiology2023
Deep Learning for Epidemiologists: An Introduction to Neural Networks.
Review in American journal of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 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
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Risk prediction models for lymph node metastasis in early gastric cancer patients: a systematic review and meta-analysis.BMC gastroenterology · 2025Pooled it
- Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Impact of data augmentation and backbone architecture selection on dental caries segmentation in panoramic radiographs: a comparative deep learning study using pre-trained U-Net models.The Saudi dental journal · 2026Article
- Is artificial intelligence a friend or foe to epidemiology?Annals of epidemiology · 2026Review
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Article
- Deep learning-based predictions and analysis of Norovirus dynamics using a fractional-order mathematical model.PloS one · 2026Article
- Dietary branched-chain amino acids intake in relation to general and central obesity among Chinese children and adolescents: a cross-sectional study.Frontiers in nutrition · 2026Article
- Article
- Key dietary amino acids modulating overweight/obesity risk in Chinese children and adolescents: a machine learning analysis of a national survey.Frontiers in nutrition · 2026Article
- Dietary intakes of cysteine, glutamate, proline, and tryptophan are associated with hypertension risk in Chinese children and adolescents: a national cross-sectional study integrating machine learning.Frontiers in nutrition · 2026Article
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- A computed tomography-based deep learning model for non-invasively predicting World Health Organization (WHO)/International Society of Urological Pathology (ISUP) pathological grades of clear cell renal cell carcinoma (ccRCC): a multicenter cohort study.Translational andrology and urology · 2025Article
- Article
- Article
- Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network.Journal of orthopaedic surgery and research · 2025Article
- Invited commentary: deep learning-methods to amplify epidemiologic data collection and analyses.American journal of epidemiology · 2025Article
- A human behavior-based model for respiratory infectious diseases prediction.Frontiers in public health · 2025Article
- Deep learning architectures for influenza dynamics and treatment optimization: a comprehensive review.Frontiers in artificial intelligence · 2025Review
- Pupillometry as a biomarker of postural control: Deep-learning models reveal side-specific pupillary responses to increased intensity of balance tasks.Psychophysiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g., overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article's scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers.
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.