ArticlePeerJ. Computer science2018
Supervised deep learning embeddings for the prediction of cervical cancer diagnosis.
Article in PeerJ. Computer science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07215728 (Computational Analysis Using Machine Learning Algorithms of Electronic Medical Record Data From Patients With Osteosarcoma or Ewing's Sarcoma), which is not on this map. Cited by 18 papers.
What it found
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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.
Computational Analysis Using Machine Learning Algorithms of Electronic Medical Record Data From Patients With Osteosarcoma or Ewing's Sarcoma
Who cites it
18 citing papers in PubMed, 94 citations in OpenAlex.
- Three-way multi-criteria decision-making with linguistic information using prospect-regret theory and generalised TODIM for heart failure data analysis.Scientific reports · 2026Article
- Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.iScience · 2026Review
- Predicting cervical cancer risk probabilities using advanced H20 AutoML and local interpretable model-agnostic explanation techniques.PeerJ. Computer science · 2024Article
- Cervical Cancer Detection Techniques: A Chronological Review.Diagnostics (Basel, Switzerland) · 2023Review
- Four-way classification of Alzheimer's disease using deep Siamese convolutional neural network with triplet-loss function.Brain informatics · 2023Article
- Cervical Cancer Diagnosis Using an Integrated System of Principal Component Analysis, Genetic Algorithm, and Multilayer Perceptron.Healthcare (Basel, Switzerland) · 2022Article
- Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning-Enabled Technologies for Bioimage Analysis.Micromachines · 2022Review
- A Deep Clustering Method For Analyzing Uterine Cervix Images Across Imaging Devices.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2021Article
- The effect of psychological nursing on the short- and long-term negative emotions and quality of life of cervical cancer patients undergoing postoperative chemotherapy.American journal of translational research · 2021Article
- Survival prediction of patients with sepsis from age, sex, and septic episode number alone.Scientific reports · 2020Article
- Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.BMC medical informatics and decision making · 2020Article
- The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.BMC genomics · 2020Article
- A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification.International journal of molecular sciences · 2019Review
- SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data.PeerJ. Computer science · 2019Article
- Artificial Intelligence in Pathology.Journal of pathology and translational medicine · 2019Article
- Computational prediction of diagnosis and feature selection on mesothelioma patient health records.PloS one · 2019Article
- CerviXpert: A multi-structural convolutional neural network for predicting cervix type and cervical cell abnormalities.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.
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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.