| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/jamia/OsborneWWWBG16 |
| P356 | DOI | 10.1093/JAMIA/OCW006 |
| P932 | PMC publication ID | 5070519 |
| P698 | PubMed publication ID | 27026618 |
| P50 | author | Andrew O Westfall | Q44237002 |
| Steven Bethard | Q55777403 | ||
| John D Osborne | Q56978898 | ||
| P2093 | author name string | James Willig | |
| Matthew Wyatt | |||
| Geoff Gordon | |||
| P2860 | cites work | The Unified Medical Language System (UMLS): integrating biomedical terminology | Q29547612 |
| Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program | Q29614363 | ||
| Comparing methods for identifying pancreatic cancer patients using electronic data sources. | Q33828084 | ||
| Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases | Q34111090 | ||
| Symbolic rule-based classification of lung cancer stages from free-text pathology reports | Q34371657 | ||
| An overview of MetaMap: historical perspective and recent advances | Q34372056 | ||
| Text mining of cancer-related information: review of current status and future directions | Q35204085 | ||
| Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model. | Q45964124 | ||
| Facilitating cancer research using natural language processing of pathology reports. | Q51989930 | ||
| Pattern-based information extraction from pathology reports for cancer registration | Q84587901 | ||
| P433 | issue | 6 | |
| P921 | main subject | machine learning | Q2539 |
| natural language processing | Q30642 | ||
| P304 | page(s) | 1077-1084 | |
| P577 | publication date | 2016-03-28 | |
| P1433 | published in | Journal of the American Medical Informatics Association | Q152037 |
| P1476 | title | Efficient identification of nationally mandated reportable cancer cases using natural language processing and machine learning | |
| P478 | volume | 23 |
| Q52626036 | A bibliometric analysis of natural language processing in medical research. |
| Q64115165 | A text-mining approach to obtain detailed treatment information from free-text fields in population-based cancer registries: A study of non-small cell lung cancer in California |
| Q99238010 | Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology |
| Q38922709 | Automated Cancer Registry Notifications: Validation of a Medical Text Analytics System for Identifying Patients with Cancer from a State-Wide Pathology Repository |
| Q49717412 | CUILESS2016: a clinical corpus applying compositional normalization of text mentions. |
| Q47261230 | Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record |
| Q98953108 | Development of paediatric non-stage prognosticator guidelines for population-based cancer registries and updates to the 2014 Toronto Paediatric Cancer Stage Guidelines |
| Q90744600 | Enhancing timeliness of drug overdose mortality surveillance: a machine learning approach |
| Q39215614 | Identifying Axial Spondyloarthritis in Electronic Medical Records of US Veterans |
| Q91847375 | Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review |
| Q91989303 | Phenotype Detection Registry System (PheDRS) - Implementation of a Generalizable Single Institution Clinical Registry Architecture |
| Q124537515 | Precision information extraction for rare disease epidemiology at scale |
| Q62491494 | Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives |
| Q92413756 | Unlocking the potential of population-based cancer registries |
| Q64115283 | Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study |
| Q90415656 | Validation of an alcohol misuse classifier in hospitalized patients |
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