| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432894 |
| 005 | | 20200225104115 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781088375228 |
| 035 | |
▼a (MiAaPQ)AAI22591988 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 610 |
| 100 | 1 |
▼a Trivedi, Gaurav. |
| 245 | 10 |
▼a Interactive Natural Language Processing for Clinical Text. |
| 260 | |
▼a [S.l.]:
▼b University of Pittsburgh.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 121 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Hochheiser, Harry. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Pittsburgh, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Free-text allows clinicians to capture rich information about patients in narratives and first-person stories. Care providers are likely to continue using free-text in Electronic Medical Records (EMRs) for the foreseeable future due to convenience and utility offered. However, this complicates information extraction tasks for big-data applications. Despite advances in Natural Language Processing (NLP) techniques, building models on clinical text is often expensive and time-consuming. Current approaches require a long collaboration between clinicians and data-scientists. Clinicians provide annotations and training data, while data-scientists build the models. With the current approaches, the domain experts - clinicians and clinical researchers - do not have provisions to inspect these models or give direct feedback. This forms a barrier to NLP adoption and limits its power and utility for real-world clinical applications.Interactive learning systems may allow clinicians without machine learning experience to build NLP models on their own. Interactive methods are particularly attractive for clinical text due to the diversity of tasks that need customized training data. Interactivity could enable end-users (clinicians) to review model outputs and provide feedback for model revisions within a closed feedback loop. This approach may make it feasible to extract understanding from unstructured text in patient records |
| 590 | |
▼a School code: 0178. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Medicine. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0564 |
| 710 | 20 |
▼a University of Pittsburgh.
▼b School of Computing and Information. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0178 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493203
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |