ArticlePloS one2016
Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03394859 (Electronic Medical Records and Genomics), which is not on this map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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.
Electronic Medical Records and Genomics (eMERGE) Phase III
Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it, 84 citations in OpenAlex.
- A Systematic Review of Algorithms for Identifying Pediatric Neurodevelopmental Outcomes.Pharmacoepidemiology and drug safety · 2025Pooled it
- Health databases and early identification of autism spectrum disorder: a scoping review.BMJ open · 2026Article
- Pediatric Autism Diagnosis Accuracy and Confidence: A Comparison of Experienced and Inexperienced Clinicians Making Decisions with and without AI Decision Support.Research square · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review.Journal of clinical medicine · 2025Review
- Applying text-mining to clinical notes: the identification of patient characteristics from electronic health records (EHRs).BMC medical informatics and decision making · 2025Article
- Cognition and behavior in neurofibromatosis type 1: report and perspective from the Cognition and Behavior in NF1 (CABIN) Task Force.Genes & development · 2025Article
- Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.Journal of neurodevelopmental disorders · 2024Review
- Transparent deep learning to identify autism spectrum disorders (ASD) in EHR using clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Coding of Childhood Psychiatric and Neurodevelopmental Disorders in Electronic Health Records of a Large Integrated Health Care System: Validation Study.JMIR mental health · 2024Article
- A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning.Children (Basel, Switzerland) · 2024Article
- Assessment of autonomic symptom scales in patients with neurodevelopmental diagnoses using electronic health record data.Research in autism spectrum disorders · 2023Article
- Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches.JMIR mental health · 2023Article
- Brief Report: The Characterization of Medical Comorbidity Prior to Autism Diagnosis in Children Before Age Two.Journal of autism and developmental disorders · 2023Article
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year.JAMA network open · 2023Article
- A practical approach to identifying autistic adults within the electronic health record.Autism research : official journal of the International Society for Autism Research · 2023Article
- Development of a phenotype ontology for autism spectrum disorder by natural language processing on electronic health records.Journal of neurodevelopmental disorders · 2022Article
- Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases.Journal of neurodevelopmental disorders · 2022Review
- Reduced false positives in autism screening via digital biomarkers inferred from deep comorbidity patterns.Science advances · 2021Article
Corrections and comments
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Authors and funding
28 authors at 6 institutions in 1 country.
Funding
Abstract
objectiveCohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD.
methodsWe extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children's Hospital (BCH) (N = 150) and Cincinnati Children's Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups.
resultsThe rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children's Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters.
conclusionsIn a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.
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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.