OBJECTIVE: Identifying patients who meet selection criteria for clinical trials is typically challenging and time-consuming. In this article, we describe our clinical natural language processing (NLP) system to automatically assess patients' eligibility based on their longitudinal medical records. This work was part of the 2018 National NLP Clinical Challenges (n2c2) Shared-Task and Workshop on Cohort Selection for Clinical Trials. MATERIALS AND METHODS: The authors developed an integrated rule-based clinical NLP system which employs a generic rule-based framework plugged in with lexical-, syntactic- and meta-level, task-specific knowledge inputs. In addition, the authors also implemented and evaluated a general clinical NLP (cNLP) system which is built with the Unified Medical Language System and Unstructured Information Management Architecture. RESULTS AND DISCUSSION: The systems were evaluated as part of the 2018 n2c2-1 challenge, and authors' rule-based system obtained an F-measure of 0.9028, ranking fourth at the challenge and had less than 1% difference from the best system. While the general cNLP system didn't achieve performance as good as the rule-based system, it did establish its own advantages and potential in extracting clinical concepts. CONCLUSION: Our results indicate that a well-designed rule-based clinical NLP system is capable of achieving good performance on cohort selection even with a small training data set. In addition, the investigation of a Unified Medical Language System-based general cNLP system suggests that a hybrid system combining these 2 approaches is promising to surpass the state-of-the-art performance.
OBJECTIVE: Identifying patients who meet selection criteria for clinical trials is typically challenging and time-consuming. In this article, we describe our clinical natural language processing (NLP) system to automatically assess patients' eligibility based on their longitudinal medical records. This work was part of the 2018 National NLP Clinical Challenges (n2c2) Shared-Task and Workshop on Cohort Selection for Clinical Trials. MATERIALS AND METHODS: The authors developed an integrated rule-based clinical NLP system which employs a generic rule-based framework plugged in with lexical-, syntactic- and meta-level, task-specific knowledge inputs. In addition, the authors also implemented and evaluated a general clinical NLP (cNLP) system which is built with the Unified Medical Language System and Unstructured Information Management Architecture. RESULTS AND DISCUSSION: The systems were evaluated as part of the 2018 n2c2-1 challenge, and authors' rule-based system obtained an F-measure of 0.9028, ranking fourth at the challenge and had less than 1% difference from the best system. While the general cNLP system didn't achieve performance as good as the rule-based system, it did establish its own advantages and potential in extracting clinical concepts. CONCLUSION: Our results indicate that a well-designed rule-based clinical NLP system is capable of achieving good performance on cohort selection even with a small training data set. In addition, the investigation of a Unified Medical Language System-based general cNLP system suggests that a hybrid system combining these 2 approaches is promising to surpass the state-of-the-art performance.
Authors: Saeed Mehrabi; Anand Krishnan; Sunghwan Sohn; Alexandra M Roch; Heidi Schmidt; Joe Kesterson; Chris Beesley; Paul Dexter; C Max Schmidt; Hongfang Liu; Mathew Palakal Journal: J Biomed Inform Date: 2015-03-16 Impact factor: 6.317
Authors: Benjamin S Glicksberg; Riccardo Miotto; Kipp W Johnson; Khader Shameer; Li Li; Rong Chen; Joel T Dudley Journal: Pac Symp Biocomput Date: 2018
Authors: George Karystianis; Azad Dehghan; Aleksandar Kovacevic; John A Keane; Goran Nenadic Journal: J Biomed Inform Date: 2015-06-29 Impact factor: 6.317
Authors: Yaguang Zheng; Victoria Vaughan Dickson; Saul Blecker; Jason M Ng; Brynne Campbell Rice; Gail D'Eramo Melkus; Liat Shenkar; Marie Claire R Mortejo; Stephen B Johnson Journal: JMIR Diabetes Date: 2022-05-16