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Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework

https://doi.org/10.2196/40755
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/jbmr.3528openalex-snapshot
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Challenges and opportunities beyond structured data in analysis of electronic health records
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Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz
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Boosting and Additive Trees
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ggplot2
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DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
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Density-Based Clustering Based on Hierarchical Density Estimates
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Krebsfrüherkennungsuntersuchungen in Deutschland: Angebot und Inanspruchnahme
inferredcites →https://doi.org/10.1007/s10462-020-09896-5openalex-snapshot
A comparative analysis of gradient boosting algorithms
inferredcites →https://doi.org/10.1016/j.annemergmed.2020.05.026openalex-snapshot
Early Prediction of Acute Kidney Injury in the Emergency Department With Machine-Learning Methods Applied to Electronic Health Record Data
inferredcites →https://doi.org/10.1016/j.ijmedinf.2007.09.001openalex-snapshot
Definition, structure, content, use and impacts of electronic health records: A review of the research literature
inferredcites →https://doi.org/10.1016/j.jbi.2015.01.012openalex-snapshot
Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
inferredcites →https://doi.org/10.1016/j.jbi.2019.103337openalex-snapshot
Deep learning for electronic health records: A comparative review of multiple deep neural architectures
inferredcites →https://doi.org/10.1016/j.khinf.2010.03.002openalex-snapshot
Gesundheitsberichterstattung des Bundes
inferredcites →https://doi.org/10.1016/j.procs.2018.10.299openalex-snapshot
Early Detection of Disease Using Electronic Health Records and Fisher’s Wishart Discriminant Analysis
inferredcites →https://doi.org/10.1016/j.semarthrit.2019.01.002openalex-snapshot
Identifying lupus patients in electronic health records: Development and validation of machine learning algorithms and application of rule-based algorithms
inferredcites →https://doi.org/10.1038/nrg3208openalex-snapshot
Mining electronic health records: towards better research applications and clinical care
inferredcites →https://doi.org/10.1038/s41398-020-01100-0openalex-snapshot
Machine learning for suicide risk prediction in children and adolescents with electronic health records
inferredcites →https://doi.org/10.1038/s41586-019-1390-1openalex-snapshot
A clinically applicable approach to continuous prediction of future acute kidney injury
inferredcites →https://doi.org/10.1038/s41598-020-62922-yopenalex-snapshot
BEHRT: Transformer for Electronic Health Records
inferredcites →https://doi.org/10.1038/s41746-020-00341-zopenalex-snapshot
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
inferredcites →https://doi.org/10.1038/s42256-019-0138-9openalex-snapshot
From local explanations to global understanding with explainable AI for trees
inferredcites →https://doi.org/10.1038/srep26094openalex-snapshot
Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
inferredcites →https://doi.org/10.1093/jamia/ocw135openalex-snapshot
Surrogate-assisted feature extraction for high-throughput phenotyping
inferredcites →https://doi.org/10.1097/00005650-199801000-00004openalex-snapshot
Comorbidity Measures for Use with Administrative Data
inferredcites →https://doi.org/10.1097/01.mlr.0000102367.93252.70openalex-snapshot
Clinical Risk Groups (CRGs)
inferredcites →https://doi.org/10.1097/mlr.0000000000000735openalex-snapshot
Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data
inferredcites →https://doi.org/10.1109/jbhi.2016.2633963openalex-snapshot
$\mathtt {Deepr}$: A Convolutional Net for Medical Records
inferredcites →https://doi.org/10.1109/mci.2018.2840738openalex-snapshot
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
inferredcites →https://doi.org/10.1136/bmjopen-2013-003482openalex-snapshot
Predicting risk of emergency admission to hospital using primary care data: derivation and validation of QAdmissions score
inferredcites →https://doi.org/10.1137/1.9781611974348.49openalex-snapshot
Risk Prediction with Electronic Health Records: A Deep Learning Approach
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
inferredcites →https://doi.org/10.1145/2939672.2939823openalex-snapshot
Multi-layer Representation Learning for Medical Concepts
inferredcites →https://doi.org/10.1145/3240323.3240377openalex-snapshot
Word2vec applied to recommendation
inferredcites →https://doi.org/10.1145/3292500.3330701openalex-snapshot
Optuna
inferredcites →https://doi.org/10.1145/3383313.3418486openalex-snapshot
Tuning Word2vec for Large Scale Recommendation Systems
inferredcites →https://doi.org/10.1145/3439726openalex-snapshot
Deep Learning--based Text Classification
inferredcites →https://doi.org/10.1146/annurev-publhealth-032315-021353openalex-snapshot
Using Electronic Health Records for Population Health Research: A Review of Methods and Applications
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.1176/appi.ajp.2016.16101169openalex-snapshot
Machine Learning and Electronic Health Records: A Paradigm Shift
inferredcites →https://doi.org/10.1177/1077558709332440openalex-snapshot
Review: Use of Electronic Medical Records for Health Outcomes Research
inferredcites →https://doi.org/10.1177/17562864211048336openalex-snapshot
Treatment patterns in pediatric patients with multiple sclerosis in Germany—a nationwide claim-based analysis
inferredcites →https://doi.org/10.1186/s12864-018-5370-xopenalex-snapshot
Gene2vec: distributed representation of genes based on co-expression
inferredcites →https://doi.org/10.1186/s12911-019-1010-xopenalex-snapshot
Assessing stroke severity using electronic health record data: a machine learning approach
inferredcites →https://doi.org/10.1214/aos/1013203451openalex-snapshot
Greedy function approximation: A gradient boosting machine.
inferredcites →https://doi.org/10.1214/aos/1016218223openalex-snapshot
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
inferredcites →https://doi.org/10.1371/journal.pmed.1002695openalex-snapshot
Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records
inferredcites →https://doi.org/10.1371/journal.pmed.1002701openalex-snapshot
Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study
inferredcites →https://doi.org/10.1371/journal.pone.0202344openalex-snapshot
Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease
inferredcites →https://doi.org/10.1609/aaai.v26i2.18981openalex-snapshot
Statistical Relational Learning to Predict Primary Myocardial Infarction from Electronic Health Records
inferredcites →https://doi.org/10.21105/joss.00861openalex-snapshot
UMAP: Uniform Manifold Approximation and Projection
inferredcites →https://doi.org/10.2196/22550openalex-snapshot
Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation
inferredcites →https://doi.org/10.2196/40755openalex-snapshot
Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework
inferredcites →https://doi.org/10.3126/jiee.v3i1.34327openalex-snapshot
Efficient Estimation of Nepali Word Representations in Vector Space
inferredcites →https://doi.org/10.48550/arxiv.1301.3781openalex-snapshot
Efficient Estimation of Word Representations in Vector Space
inferredcites →https://doi.org/10.5281/zenodo.3607804openalex-snapshot
Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset
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Scikit-learn: Machine Learning in Python

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-20291-8_51openalex-snapshot
Predicting Patient Outcome and LOS Using a Two-Stage Approach with Semantically Enriched Features
inferred← citeshttps://doi.org/10.1007/978-3-032-20291-8_53openalex-snapshot
Predicting Hospital Length of Stay and Mortality at Admission Using Machine Learning with Advanced Feature Engineering and ICD-10 Clustering
inferred← citeshttps://doi.org/10.1101/2024.12.03.24318322openalex-snapshot
ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in Medicine
inferred← citeshttps://doi.org/10.1109/cbms61543.2024.00028openalex-snapshot
Encoding breast cancer patients’ medical pathways from reimbursement data using representation learning: a benchmark for clustering tasks
inferred← citeshttps://doi.org/10.1109/cbms65348.2025.00023openalex-snapshot
A Comparative Analysis of Clustering Methods for Identifying Patient Subgroups in Chronic Kidney Disease Using Feature Engineering
inferred← citeshttps://doi.org/10.1186/s12913-025-13817-zopenalex-snapshot
Multimorbidity measurement strategies for predicting hospital visits
inferred← citeshttps://doi.org/10.2196/52508openalex-snapshot
Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS)
inferred← citeshttps://doi.org/10.2196/preprints.52508openalex-snapshot
Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS) (Preprint)
inferred← citeshttps://doi.org/10.2196/preprints.91298openalex-snapshot
Representing Injuries in Trauma Patients: Development and Evaluation of Embeddings for Injuries (Preprint)
inferred← citeshttps://doi.org/10.4018/ijrqeh.348579openalex-snapshot
Probabilistic Model of Patient Classification Using Bayesian Model
inferred← citeshttps://doi.org/10.64030/2997-612x.02.02.01openalex-snapshot
Medical Diagnosis Coding Automation: Similarity Search vs. Generative Ai
inferred← citeshttps://doi.org/10.64898/2026.01.03.26343379openalex-snapshot
Representing Injuries in Trauma Patients: Development and Evaluation of Embeddings for Injuries
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