ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2022
KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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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
8 citing papers in PubMed.
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.IEEE reviews in biomedical engineering · 2026Review
- RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.Patterns (New York, N.Y.) · 2025Article
- Article
- Multi-modal prediction of extracorporeal support-a resource intensive therapy, utilizing a large national database.JAMIA open · 2025Article
- Acute Kidney Injury Prognosis Prediction Using Machine Learning Methods: A Systematic Review.Kidney medicine · 2025Review
- TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.Bioinformatics (Oxford, England) · 2024Article
- A Novel Time-Aware Deep Learning Model Predicting Myopia in Children and Adolescents.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Rapid accumulation of temporal Electronic Health Record (EHR) data and recent advances in deep learning have shown high potential in precisely and timely predicting patients' risks using AI. However, most existing risk prediction approaches ignore the complex asynchronous and irregular problems in real-world EHR data. This paper proposes a novel approach called Knowledge-guIded Time-aware LSTM (KIT-LSTM) for continuous mortality predictions using EHR. KIT-LSTM extends LSTM with two time-aware gates and a knowledge-aware gate to better model EHR and interprets results. Experiments on real-world data for patients with acute kidney injury with dialysis (AKI-D) demonstrate that KIT-LSTM performs better than the state-of-the-art methods for predicting patients' risk trajectories and model interpretation. KIT-LSTM can better support timely decision-making for 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.