# Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks

arXiv: https://arxiv.org/abs/1904.04990 · HTML: https://mlsanity.com/paper/1904.04990/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case](https://mlsanity.com/paper/2109.01935/) (2021)
- [Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives](https://mlsanity.com/paper/2111.04898/) (2021)
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- [SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing](https://mlsanity.com/paper/2407.16999/) (2024)
- [Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases](https://mlsanity.com/paper/2107.11665/) (2021)
- [A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases](https://mlsanity.com/paper/2205.08891/) (2022)
- [MixEHR-Nest: Identifying Subphenotypes within Electronic Health Records through Hierarchical Guided-Topic Modeling](https://mlsanity.com/paper/2410.13217/) (2024)
- [Latent Factor Point Processes for Patient Representation in Electronic Health Records](https://mlsanity.com/paper/2508.20327/) (2025)
- [A Method for Characterizing Disease Progression from Acute Kidney Injury to Chronic Kidney Disease](https://mlsanity.com/paper/2511.14603/) (2025)
- [Integrating knowledge from case reports: a medical ontology based multimodal information system with structured summary](https://mlsanity.com/paper/2609.19775/) (2026)

## Abstract (arXiv)

Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions. This study used a memory network-based deep learning approach to discover AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis. We leveraged a real world critical care EHR corpus including 37,486 ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype I is with an average age of 63.03 ± 17.25  years, and is characterized by mild loss of kidney excretory function (Serum Creatinine (SCr) 1.55± 0.34 mg/dL, estimated Glomerular Filtration Rate Test (eGFR) 107.65± 54.98 mL/min/1.73m2). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81 ± 10.43  years, and was characterized by severe loss of kidney excretory function (SCr 1.96± 0.49 mg/dL, eGFR 82.19± 55.92 mL/min/1.73m2). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07 ± 11.32  years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr 1.69± 0.32 mg/dL, eGFR 93.97± 56.53 mL/min/1.73m2). Both SCr and eGFR are significantly different across the three sub-phenotypes with statistical testing plus postdoc analysis, and the conclusion still holds after age adjustment.