Reference health
Clinical Text Data in Machine Learning: Systematic Review
128/128 checkable references clean · checked 2026-08-16
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
18 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 128 checked references that resolve
resolves10.1136/jamia.2010.007237
Data from clinical notes: a perspective on the tension between structure and flexible documentation
resolves10.1016/j.ijmedinf.2014.06.009
Text mining of cancer-related information: Review of current status and future directions
resolves10.1109/JBHI.2017.2684121
A Natural Language Processing Framework for Assessing Hospital Readmissions for Patients With COPD
resolves10.1148/radiol.2017170549
High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision
resolves10.1038/s41598-018-27946-5
Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives
resolves10.2106/JBJS.16.00167
Preoperative Opioid Use Is Associated with Early Revision After Total Knee Arthroplasty
resolves10.1002/jhrm.21237
Electronic approaches to making sense of the text in the adverse event reporting system
resolves10.1093/jamia/ocx125
Using machine learning for sequence-level automated MRI protocol selection in neuroradiology
resolves10.1016/j.jbi.2017.06.019
Automatic prediction of coronary artery disease from clinical narratives
resolves10.1016/j.jbi.2017.04.011
Automated annotation and classification of BI-RADS assessment from radiology reports
resolves10.1016/j.jbi.2015.09.010
A study of active learning methods for named entity recognition in clinical text
resolves10.1016/j.jbi.2015.09.002
An automatic system to identify heart disease risk factors in clinical texts over time
resolves10.4103/2153-3539.194838
The utility of including pathology reports in improving the computational identification of patients
resolves10.1016/j.jbi.2017.07.005
Automatic classification of RDoC positive valence severity with a neural network
resolves10.1016/j.jbi.2017.04.003
Crowd control: Effectively utilizing unscreened crowd workers for biomedical data annotation
resolves10.4137/BII.S38308
Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates through Natural Language Processing and Machine Learning
resolves10.1016/j.jbi.2015.06.029
Combining knowledge- and data-driven methods for de-identification of clinical narratives
resolves10.1016/j.jbi.2017.06.005
Learning to identify Protected Health Information by integrating knowledge- and data-driven algorithms: A case study on psychiatric evaluation notes
resolves10.1016/j.ijmedinf.2017.05.015
Automated problem list generation and physicians perspective from a pilot study
resolves10.2196/medinform.7140
Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers
resolves10.1186/s12911-018-0626-6
Using natural language processing methods to classify use status of dietary supplements in clinical notes
resolves10.1038/s41598-018-25773-2
Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing
resolves10.1007/s11517-017-1772-1
Classifying clinical notes with pain assessment using machine learning
resolves10.1016/j.ijmedinf.2017.05.005
Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events
resolves10.1016/j.jpainsymman.2018.02.016
Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records
resolves10.1016/j.amjmed.2016.12.008
Using the Electronic Medical Record to Identify Patients at High Risk for Frequent Emergency Department Visits and High System Costs
resolves10.1093/jamia/ocx131
Hierarchical attention networks for information extraction from cancer pathology reports
resolves10.1371/journal.pone.0192360
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
resolves10.1136/eb-2017-102688
Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression
resolves10.1016/j.jbi.2017.05.020
Automatic recognition of symptom severity from psychiatric evaluation records
resolves10.4338/ACI-2016-08-RA-0129
Identification of Long Bone Fractures in Radiology Reports Using Natural Language Processing to support Healthcare Quality Improvement
resolves10.1109/ICHI.2017.31
A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records
resolves10.1016/j.artmed.2015.09.007
Information extraction from multi-institutional radiology reports
resolves10.1007/s10278-015-9823-3
Unsupervised Topic Modeling in a Large Free Text Radiology Report Repository
resolves10.2214/AJR.16.16128
Performance of a Machine Learning Classifier of Knee MRI Reports in Two Large Academic Radiology Practices: A Tool to Estimate Diagnostic Yield
resolves10.1007/s10278-016-9931-8
Characterization of Change and Significance for Clinical Findings in Radiology Reports Through Natural Language Processing
resolves10.1016/j.artmed.2018.05.001
Classifying medical relations in clinical text via convolutional neural networks
resolves10.1371/journal.pone.0174708
Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning
resolves10.18653/v1/n16-1056
Bidirectional RNN for Medical Event Detection in Electronic Health Records
resolves10.1155/2015/636371
Identification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health Records
resolves10.3233/978-1-61499-564-7-599
Classification of Contextual Use of Left Ventricular Ejection Fraction Assessments
resolves10.1016/j.jbi.2015.04.008
Automated misspelling detection and correction in clinical free-text records
resolves10.1016/j.jbi.2015.07.010
Challenges in clinical natural language processing for automated disorder normalization
resolves10.1016/j.jbi.2017.06.006
A hybrid approach to automatic de-identification of psychiatric notes
resolves10.1186/s12911-015-0160-8
An end-to-end hybrid algorithm for automated medication discrepancy detection
resolves10.4338/ACI-2016-01-RA-0015
Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers
resolves10.1016/j.jbi.2015.06.009
Automatic de-identification of electronic medical records using token-level and character-level conditional random fields
resolves10.1186/s12911-017-0468-7
Entity recognition from clinical texts via recurrent neural network
resolves10.1016/j.jbi.2017.05.023
De-identification of clinical notes via recurrent neural network and conditional random field
resolves10.1093/jamia/ocv016
Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text
resolves10.1093/jamia/ocx090
Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes
resolves10.1007/s10488-018-0850-5
Measuring Use of Evidence Based Psychotherapy for Posttraumatic Stress Disorder in a Large National Healthcare System
resolves10.1016/j.jbi.2014.11.009
Automatic detection of patients with invasive fungal disease from free-text computed tomography (CT) scans
resolves10.1093/jamia/ocw097
Congestive heart failure information extraction framework for automated treatment performance measures assessment
resolves10.2196/jmir.6887
Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A PHIS+ Pilot Study
resolves10.2196/publichealth.9361
Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning
resolves10.1016/j.artmed.2016.06.001
Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction
resolves10.1093/jamia/ocw006
Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning
resolves10.1016/j.jbi.2017.05.019
Predictive modeling for classification of positive valence system symptom severity from initial psychiatric evaluation records
resolves10.1016/j.ijmedinf.2017.10.004
Need of informatics in designing interoperable clinical registries
resolves10.1016/j.jbi.2015.06.010
The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs
resolves10.1016/j.compbiomed.2017.12.026
Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives
resolves10.1016/j.jbi.2017.06.007
Counting trees in Random Forests: Predicting symptom severity in psychiatric intake reports
resolves10.4338/ACI-2014-11-RA-0110
Natural Language Processing Techniques for Extracting and Categorizing Finding Measurements in Narrative Radiology Reports
resolves10.1016/j.jss.2016.09.058
Detection of clinically important colorectal surgical site infection using Bayesian network
resolves10.1007/s10278-018-0116-5
Automatic Normalization of Anatomical Phrases in Radiology Reports Using Unsupervised Learning
resolves10.1186/1472-6947-13-S1-S1
Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
resolves10.1093/jamia/ocw071
Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals
resolves10.4338/ACI-2015-09-RA-0114
Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU
resolves10.1016/j.jbi.2015.08.011
Risk factor detection for heart disease by applying text analytics in electronic medical records
resolves10.1016/j.jbi.2017.06.010
Predicting mental conditions based on “history of present illness” in psychiatric notes with deep neural networks
resolves10.1093/jamia/ocx070
NLPReViz: an interactive tool for natural language processing on clinical text
resolves10.1007/s10278-017-0021-3
Automatic Determination of the Need for Intravenous Contrast in Musculoskeletal MRI Examinations Using IBM Watson’s Natural Language Processing Algorithm
resolves10.1186/s12911-017-0518-1
Word2Vec inversion and traditional text classifiers for phenotyping lupus
resolves10.1016/j.jbi.2017.11.007
Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition
resolves10.1002/acr.23140
Identifying Axial Spondyloarthritis in Electronic Medical Records of US Veterans
resolves10.1093/jamia/ocv102
A method for systematic discovery of adverse drug events from clinical notes
resolves10.1186/s12911-017-0483-8
Using multiclass classification to automate the identification of patient safety incident reports by type and severity
resolves10.1097/CCM.0000000000003148
Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay*
resolves10.1186/s12911-017-0556-8
Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach
resolves10.1093/jamia/ocw109
A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD)
resolves10.1111/acem.12859
Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury
resolves10.1016/j.jbi.2015.09.006
A hybrid model for automatic identification of risk factors for heart disease
resolves10.1016/j.jbi.2015.06.015
Automatic detection of protected health information from clinic narratives
resolves10.1371/journal.pone.0174970
A study of the transferability of influenza case detection systems between two large healthcare systems
resolves10.1016/j.jbi.2016.10.005
Tumor reference resolution and characteristic extraction in radiology reports for liver cancer stage prediction
resolves10.1148/radiol.2018171093
Natural Language–based Machine Learning Models for the Annotation of Clinical Radiology Reports
resolves10.1186/s12911-018-0625-7
Discovering and identifying New York heart association classification from electronic health records
resolves10.1007/978-3-319-96893-3_20
Improving Clinical Named Entity Recognition with Global Neural Attention
resolves10.2196/medinform.7235
Effective Information Extraction Framework for Heterogeneous Clinical Reports Using Online Machine Learning and Controlled Vocabularies
resolves10.3233/978-1-61499-564-7-629
Identifying Patients with Depression Using Free-text Clinical Documents
resolves10.1186/s12887-016-0592-z
Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data
resolves10.1093/jamia/ocx138
Clinical documentation variations and NLP system portability: a case study in asthma birth cohorts across institutions
resolves10.1377/hlthaff.2014.0054
Insights From Advanced Analytics At The Veterans Health Administration
resolves10.1136/amiajnl-2011-000203
2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
resolves10.1136/amiajnl-2011-000784
Evaluating the state of the art in coreference resolution for electronic medical records
resolves10.1136/amiajnl-2013-001628
Evaluating temporal relations in clinical text: 2012 i2b2 Challenge
resolves10.1016/j.jbi.2015.07.020
Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus
resolves10.1016/j.jbi.2015.05.009
Annotating risk factors for heart disease in clinical narratives for diabetic patients
resolves10.1016/j.jbi.2017.04.017
Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2
resolves10.1016/j.jbi.2017.06.011
De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1
resolves10.1186/1741-7015-10-100
Integrating pathology and radiology disciplines: an emerging opportunity?
resolves10.1002/jhm.425
Creating a better discharge summary: Improvement in quality and timeliness using an electronic discharge summary
resolves10.1136/amiajnl-2013-001926
Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory: Table 1
resolves10.1136/amiajnl-2014-002887
Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department
resolves10.1093/jamia/ocx079
Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record
resolves10.18653/v1/n18-2072
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
resolves10.18653/v1/d19-1670
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
The 18 references without a DOI — listed, not checked
no DOI — not checkedref4
no DOI — not checkedKitchenhamBDepartment of Computer Science: Keele University20042020-03-24Procedures for Performing Systematic Reviewshttp://www.inf.ufsc.br/~aldo.vw/kitchenham.pdf
no DOI — not checkedref8
no DOI — not checkedref11
no DOI — not checkedref29
no DOI — not checkedref33
no DOI — not checkedref41
no DOI — not checkedref63
no DOI — not checkedref69
no DOI — not checkedref70
no DOI — not checkedref77
no DOI — not checkedref81
no DOI — not checkedref98
no DOI — not checkedref100
no DOI — not checkedref104
no DOI — not checkedHoDLiangEChenXStoicaIAbbeelPPopulation Based Augmentation: Efficient Learning of Augmentation Policy SchedulesProceedings of the International Conference on Machine Learning2019CML'19June 10-15, 2019Long Beach, USA273141
no DOI — not checkedXieZWangSILiJLevyDNieAJurafskyDNgAData Noising as Smoothing in Neural Network Language ModelsProceedings of the 5th International Conference on Learning Representations2017CLR'17April 24-26, 2017Toulon, France
no DOI — not checkedref145
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-08-16 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.2196/17984"><img src="https://citestamp.com/citestamped/10.2196/17984/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.2196/17984)