| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/jamia/YuLSGCSMKC15 |
| P356 | DOI | 10.1093/JAMIA/OCV034 |
| P3181 | OpenCitations bibliographic resource ID | 928042 |
| P932 | PMC publication ID | 4986664 |
| P698 | PubMed publication ID | 25929596 |
| P50 | author | Peter Szolovits | Q43046435 |
| Tianxi Cai | Q57915962 | ||
| Sheng Yu | Q58865421 | ||
| Isaac Kohane | Q60431480 | ||
| Shawn N. Murphy | Q112735786 | ||
| P2093 | author name string | Katherine P Liao | |
| Stanley Y Shaw | |||
| Vivian S Gainer | |||
| Susanne E Churchill | |||
| P2860 | cites work | QT interval and antidepressant use: a cross sectional study of electronic health records | Q24629713 |
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| Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies | Q35286565 | ||
| Substance use disorders and comorbid Axis I and II psychiatric disorders among young psychiatric patients: findings from a large electronic health records database. | Q35380871 | ||
| Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels. | Q35551787 | ||
| Analyzing the heterogeneity and complexity of Electronic Health Record oriented phenotyping algorithms. | Q35625126 | ||
| Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis | Q35625338 | ||
| Patterns of cardiovascular risk in rheumatoid arthritis | Q35638493 | ||
| Portability of an algorithm to identify rheumatoid arthritis in electronic health records. | Q36085131 | ||
| Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. | Q36101689 | ||
| Latent Semantic Indexing of medical diagnoses using UMLS semantic structures | Q36468502 | ||
| Associations of autoantibodies, autoimmune risk alleles, and clinical diagnoses from the electronic medical records in rheumatoid arthritis cases and non-rheumatoid arthritis controls | Q36639920 | ||
| The validity of ICD-9-CM codes in identifying postoperative deep vein thrombosis and pulmonary embolism | Q36848687 | ||
| A comparison of bootstrap methods and an adjusted bootstrap approach for estimating the prediction error in microarray classification | Q36877269 | ||
| Improving case definition of Crohn's disease and ulcerative colitis in electronic medical records using natural language processing: a novel informatics approach | Q36881504 | ||
| Validation of psoriatic arthritis diagnoses in electronic medical records using natural language processing | Q36953165 | ||
| Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk | Q37020400 | ||
| Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network | Q37024603 | ||
| Modeling disease severity in multiple sclerosis using electronic health records | Q37297607 | ||
| Using electronic health records to drive discovery in disease genomics | Q37876693 | ||
| An evaluation of concept based latent semantic indexing for clinical information retrieval | Q38474650 | ||
| ON THE ADAPTIVE ELASTIC-NET WITH A DIVERGING NUMBER OF PARAMETERS. | Q40349223 | ||
| Inaccuracy of the International Classification of Diseases (ICD-9-CM) in identifying the diagnosis of ischemic cerebrovascular disease. | Q41598493 | ||
| Pneumonia identification using statistical feature selection | Q41998551 | ||
| Comparative effectiveness research using electronic health records: impacts of oral antidiabetic drugs on the development of chronic kidney disease | Q45795381 | ||
| The risk of developing coronary artery disease or congestive heart failure, and overall mortality, in type 2 diabetic patients receiving rosiglitazone, pioglitazone, metformin, or sulfonylureas: a retrospective analysis | Q46132812 | ||
| An automated technique for identifying associations between medications, laboratory results and problems. | Q51652711 | ||
| Improvements on Cross-Validation: The .632+ Bootstrap Method | Q56019665 | ||
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| The risk of overall mortality in patients with Type 2 diabetes receiving different combinations of sulfonylureas and metformin: a retrospective analysis | Q63966651 | ||
| P433 | issue | 5 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | automation | Q184199 |
| feature extraction | Q1026626 | ||
| bias | Q742736 | ||
| P304 | page(s) | 993-1000 | |
| P577 | publication date | 2015-09-01 | |
| P1433 | published in | Journal of the American Medical Informatics Association | Q152037 |
| P1476 | title | Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources | |
| P478 | volume | 22 |
| Q38720515 | A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry |
| Q31111183 | A comparison between physicians and computer algorithms for form CMS-2728 data reporting |
| Q57765144 | Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models |
| Q38652487 | Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression |
| Q36188138 | Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing |
| Q47584431 | Automated disease cohort selection using word embeddings from Electronic Health Records |
| Q38386031 | Automated learning of domain taxonomies from text using background knowledge |
| Q38796894 | Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records |
| Q36096822 | Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics |
| Q38911464 | Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations |
| Q47261230 | Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record |
| Q38384891 | Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest |
| Q31141724 | Clinical Research Informatics for Big Data and Precision Medicine |
| Q33823874 | Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods |
| Q38755964 | Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups. |
| Q51741133 | Development of an automated phenotyping algorithm for hepatorenal syndrome. |
| Q33682411 | Discriminative and Distinct Phenotyping by Constrained Tensor Factorization |
| Q38836310 | EHR-based phenotyping: Bulk learning and evaluation |
| Q38621194 | Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network |
| Q47360634 | Enabling phenotypic big data with PheNorm. |
| Q31172379 | Ensembles of NLP Tools for Data Element Extraction from Clinical Notes |
| Q64116625 | Feature extraction for phenotyping from semantic and knowledge resources |
| Q42695608 | Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care |
| Q38377853 | HTP-NLP: A New NLP System for High Throughput Phenotyping |
| Q52370449 | High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records. |
| Q47776412 | High-fidelity phenotyping: richness and freedom from bias |
| Q90714210 | High-throughput multimodal automated phenotyping (MAP) with application to PheWAS |
| Q91362876 | High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP) |
| Q37063866 | Implementation of Electronic Consent at a Biobank: An Opportunity for Precision Medicine Research. |
| Q92074973 | Integration of genetic and clinical information to improve imputation of data missing from electronic health records |
| Q47314886 | Learning Bundled Care Opportunities from Electronic Medical Records |
| Q31095809 | Learning statistical models of phenotypes using noisy labeled training data |
| Q47601493 | Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records |
| Q89559893 | Natural Language Processing for EHR-Based Computational Phenotyping |
| Q49361968 | Performing an Informatics Consult: Methods and Challenges |
| Q39879045 | PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability |
| Q38621165 | PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature |
| Q99713069 | PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records |
| Q64261636 | Phenotyping to Facilitate Accrual for a Cardiovascular Intervention |
| Q89675923 | Polar Labeling: Silver standard algorithm for training disease classifiers |
| Q36365430 | Secondary Use and Analysis of Big Data Collected for Patient Care. Contribution from the IMIA Working Group on Data Mining and Big Data Analytics |
| Q36020433 | Study design for non-recurring, time-to-event outcomes in the presence of error-prone diagnostic tests or self-reports |
| Q39385499 | Surrogate-assisted feature extraction for high-throughput phenotyping |
| Q36180839 | Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health |
| Q36736000 | The Biobank Portal for Partners Personalized Medicine: A Query Tool for Working with Consented Biobank Samples, Genotypes, and Phenotypes Using i2b2. |
| Q38756857 | The use of electronic health records for psychiatric phenotyping and genomics. |
| Q39085388 | The utility of including pathology reports in improving the computational identification of patients |
| Q28596404 | Trends in biomedical informatics: automated topic analysis of JAMIA articles |
| Q58707548 | Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis |
| Q38732876 | Unravelling the human genome-phenome relationship using phenome-wide association studies. |
| Q60927138 | Using natural language processing and machine learning to identify breast cancer local recurrence |
| Q90153234 | Validation of an Electronic Medical Record-Based Algorithm for Identifying Posttraumatic Stress Disorder in U.S. Veterans |
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