ArticlePatterns (New York, N.Y.)2023
Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 18 citations in OpenAlex.
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- ClinicRealm: Re-evaluating large language models with conventional machine learning for non-generative clinical prediction tasks.NPJ digital medicine · 2026Article
- Personalizing Maintenance Rituximab in Follicular Lymphoma: A Machine Learning Framework for Risk-Benefit Optimization.Health data science · 2026Article
- PathCare: Integrating Clinical Pathway Information to Enable Healthcare Prediction at the Neuron Level.Bioengineering (Basel, Switzerland) · 2025Article
- Privacy-Preserving Federated Learning Framework for Multi-Source Electronic Health Records Prognosis Prediction.Sensors (Basel, Switzerland) · 2025Article
- Assessing biomarker trajectories for mortality risk in peritoneal dialysis: A focus on multivariate joint modeling.PloS one · 2025Article
- Exploring the Relationship between Dietary Intake and Clinical Outcomes in Peritoneal Dialysis Patients Stratified by Serum Albumin Levels: A 12-Year Follow-Up Using Fine-Grained Electronic Medical Records Data.Health data science · 2025Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
Abstract
The study aims to develop AICare, an interpretable mortality prediction model, using electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) patients. AICare includes a multichannel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AICare outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships and variations in feature importance and provides reference values. An AI-doctor interaction system is developed for visualizing patients' health trajectories and risk indicators.
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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.