ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2016
Interpretable Deep Models for ICU Outcome Prediction.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 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
66 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Scaling vision models does not consistently improve localisation-based explanation quality.Scientific reports · 2026Article
- Artificial Intelligence in Predicting Mortality Risk for Critically Ill Children in Pediatric Intensive Care Units: A Systematic Review.Health science reports · 2026Review
- Stable Longitudinal Screening of Latent Physiological Dysregulation from Psychometric Data Using Machine Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Predicting amyloid status in Primary Progressive Aphasia using explainable artificial intelligence.NPJ dementia · 2026Article
- A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion.Frontiers in big data · 2026Article
- Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study.Journal of medical Internet research · 2025Observational
- Tackling the small imbalanced horizontal dataset regressions by Stability Selection and SMOGN: a case study of ventilation-free days prediction in the pediatric intensive care unit and the importance of PRISM.International journal of medical informatics · 2025Article
- Responsible artificial intelligence in healthcare: a systematic review on the use of ethical principles in the development and deployment of artificial intelligence.BMJ digital health & AI · 2025Article
- Explainable federated learning scheme for secure healthcare data sharing.Health information science and systems · 2024Article
- Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data.BMJ paediatrics open · 2024Article
- Prototype Learning for Medical Time Series Classification via Human-Machine Collaboration.Sensors (Basel, Switzerland) · 2024Article
- Article
- Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?Bioinformatics (Oxford, England) · 2023Article
- Prognosis of COVID-19 patients using lab tests: A data mining approach.Health science reports · 2023Article
- "Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations.AI & society · 2022Article
- A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System.Sensors (Basel, Switzerland) · 2022Review
- Status Forecasting Based on the Baseline Information Using Logistic Regression.Entropy (Basel, Switzerland) · 2022Article
- Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care.Neurocritical care · 2022Article
6 more citing papers are in PubMed but not listed here.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians.
Indexed as
Identifiers
28269832PMC5333206What 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.