ArticleJournal of medical systems2021
Shedding Light on the Black Box: Explaining Deep Neural Network Prediction of Clinical Outcomes.
Article in Journal of medical systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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Who cites it
16 citing papers in PubMed.
- Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.BMC medical informatics and decision making · 2026Article
- Histopathologic basis of a deep learning pelvic computed tomography model for prognostic prediction among patients with advanced high-grade serous ovarian carcinoma.Quantitative imaging in medicine and surgery · 2026Article
- A machine learning-driven and explainable prediction model for in-hospital mortality in patients with diabetes mellitus and congestive heart failure: integrating XGBoost and SHAP approaches.Frontiers in public health · 2026Article
- Muscle-Driven prognostication in gastric cancer: A multicenter deep learning framework integrating Iliopsoas and erector spinae radiomics for 5-Year survival prediction.Scientific reports · 2025Article
- Machine-Learned Codes from EHR Data Predict Hard Outcomes Better than Human-Assigned ICD Codes.Machine learning and knowledge extraction · 2025Article
- A deep learning analysis for dual healthcare system users and risk of opioid use disorder.Scientific reports · 2025Article
- Beware the Little Foxes that Spoil the Vines: Small Inconsistencies in Clinical Data Can Distort Machine Learning Findings.Fortune journal of health sciences · 2025Article
- A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?Journal of experimental orthopaedics · 2024Review
- A Novel Explainable AI Method to Assess Associations between Temporal Patterns in Patient Trajectories and Adverse Outcome Risks: Analyzing Fitness as a Risk Factor of ADRD.medRxiv : the preprint server for health sciences · 2024Article
- AI-Driven Spectral Decomposition: Predicting the Most Probable Protein Compositions from Surface Enhanced Raman Spectroscopy Spectra of Amino Acids.Bioengineering (Basel, Switzerland) · 2024Article
- Clinical Decision Support System Used in Spinal Disorders: Scoping Review.Journal of medical Internet research · 2024Article
- Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis.International journal of public health · 2024Article
- Hybrid Value-Aware Transformer Architecture for Joint Learning from Longitudinal and Non-Longitudinal Clinical Data.Journal of personalized medicine · 2023Article
- Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect.Frontiers in medicine · 2023Review
- Towards Interpretable Camera and LiDAR Data Fusion for Autonomous Ground Vehicles Localisation.Sensors (Basel, Switzerland) · 2022Article
- A model-agnostic approach for understanding heart failure risk factors.BMC research notes · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Deep neural network models are emerging as an important method in healthcare delivery, following the recent success in other domains such as image recognition. Due to the multiple non-linear inner transformations, deep neural networks are viewed by many as black boxes. For practical use, deep learning models require explanations that are intuitive to clinicians. In this study, we developed a deep neural network model to predict outcomes following major cardiovascular procedures, using temporal image representation of past medical history as input. We created a novel explanation for the prediction of the model by defining impact scores that associate clinical observations with the outcome. For comparison, a logistic regression model was fitted to the same dataset. We compared the impact scores and log odds ratios by calculating three types of correlations, which provided a partial validation of the impact scores. The deep neural network model achieved an area under the receiver operating characteristics curve (AUC) of 0.787, compared to 0.746 for the logistic regression model. Moderate correlations were found between the impact scores and the log odds ratios. Impact scores generated by the explanation algorithm has the potential to shed light on the "black box" deep neural network model and could facilitate its adoption by clinicians.
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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.