Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Development of an automated phenotyping algorithm for hepatorenal syndrome.

Journal of biomedical informatics - 1 Apr 2018

Koola Jejo D, Davis Sharon E, Al-Nimri Omar, Parr Sharidan K, Fabbri Daniel, Malin Bradley A, Ho Samuel B, Matheny Michael E

Abstract excerpt

OBJECTIVE: Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification. MATERIALS AND METHODS: A national...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Acute Kidney Injury
Share to Diagnosis, Computer-Assisted

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Electronic Health Records

Share to Liver Cirrhosis

Share to Natural Language Processing

Share to Odds Ratio

Share to ROC Curve

Share to Support Vector Machine

Source

Open in PubMed Open full text

Identifiers

PMID: 29530803
DOI: 10.1016/j.jbi.2018.03.001
PMCID: PMC5920557

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Development of an automated phenotyping algorithm for hepatorenal syndrome.PMID 29530803Read ↗

Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data.Semantic proximity Read ↗

A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury.Semantic proximity Read ↗

Rapid identification and phenotyping of nonalcoholic fatty liver disease patients using a machine-based approach in diverse healthcare systems.Semantic proximity Read ↗

Integrating etiological insights with machine learning for precision diagnosis of obstructive jaundice: Findings from a high-volume centerSemantic proximity Read ↗

Single center Automated, Multi-Source deeply Phenotyped Heart Transplant Registry as a template to build tailored data infrastructureSemantic proximity Read ↗

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping.Semantic proximity Read ↗

Time-to-event modeling with multimodal clinical and genetic features improves risk stratification of liver complications in chronic hepatitis CSemantic proximity Read ↗

Machine Learning Approaches for Electronic Health Records Phenotyping: A Methodical ReviewSemantic proximity Read ↗

Application of concise machine learning to construct accurate and interpretable EHR computable phenotypesSemantic proximity Read ↗

Rapid Identification and Phenotyping of Nonalcoholic Fatty Liver Disease Patients Using an Algorithmic Approach in Diverse, Urban Healthcare SystemsSemantic proximity Read ↗

Predicting Wilson’s Disease Progression using Machine Learning with Real-World Electronic Health RecordsSemantic proximity Read ↗

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.