ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine2017
Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network.
Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Explanation Beyond Individual Features: Instance-wise Feature Grouping for EHR Predictive Analytics.Journal of healthcare informatics research · 2026Article
- Multimodal Integration in Health Care: Development With Applications in Disease Management.Journal of medical Internet research · 2025Review
- Development of Attention-based Prediction Models for All-cause Mortality, Home Care Need, and Nursing Home Admission in Ageing Adults in Spain Using Longitudinal Electronic Health Record Data.Journal of medical systems · 2025Article
- Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.Journal of biomedical informatics · 2025Article
- Assessment of Prediction Tasks and Time Window Selection in Temporal Modeling of Electronic Health Record Data: a Systematic Review.Journal of healthcare informatics research · 2023Article
- Human understandable thyroid ultrasound imaging AI report system - A bridge between AI and clinicians.iScience · 2023Article
- Strategies of Predictive Schemes and Clinical Diagnosis for Prognosis Using MIMIC-III: A Systematic Review.Healthcare (Basel, Switzerland) · 2023Review
- Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile.International journal of environmental research and public health · 2023Article
- Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review.JMIR medical informatics · 2022Review
- Multi-dimensional patient acuity estimation with longitudinal EHR tokenization and flexible transformer networks.Frontiers in digital health · 2022Article
- Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Interpretable disease prediction using heterogeneous patient records with self-attentive fusion encoder.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks.IEEE journal of biomedical and health informatics · 2021Article
- A comparison of attentional neural network architectures for modeling with electronic medical records.JAMIA open · 2021Article
- Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review.Informatics (MDPI) · 2021Article
- Learning Predictive and Interpretable Timeseries Summaries from ICU Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Cognitive Computing-Based CDSS in Medical Practice.Health data science · 2021Review
- Explainable Deep Learning Models in Medical Image Analysis.Journal of imaging · 2020Review
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.
Funding
Abstract
The increasing accumulation of healthcare data provides researchers with ample opportunities to build machine learning approaches for clinical decision support and to improve the quality of health care. Several studies have developed conventional machine learning approaches that rely heavily on manual feature engineering and result in task-specific models for health care. In contrast, healthcare researchers have begun to use deep learning, which has emerged as a revolutionary machine learning technique that obviates manual feature engineering but still achieves impressive results in research fields such as image classification. However, few of them have addressed the lack of the interpretability of deep learning models although interpretability is essential for the successful adoption of machine learning approaches by healthcare communities. In addition, the unique characteristics of healthcare data such as high dimensionality and temporal dependencies pose challenges for building models on healthcare data. To address these challenges, we develop a gated recurrent unit-based recurrent neural network with hierarchical attention for mortality prediction, and then, using the diagnostic codes from the Medical Information Mart for Intensive Care, we evaluate the model. We find that the prediction accuracy of the model outperforms baseline models and demonstrate the interpretability of the model in visualizations.
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.