| Chapter title |
Natural language processing in biomedicine: a unified system architecture overview.
|
|---|---|
| Chapter number | 16 |
| Book title |
Clinical Bioinformatics
|
| Published in |
Methods in molecular biology, January 2014
|
| DOI | 10.1007/978-1-4939-0847-9_16 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-0846-2, 978-1-4939-0847-9
|
| Authors |
Son Doan, Mike Conway, Tu Minh Phuong, Lucila Ohno-Machado, Doan, Son, Conway, Mike, Phuong, Tu Minh, Ohno-Machado, Lucila |
| Abstract |
In contemporary electronic medical records much of the clinically important data-signs and symptoms, symptom severity, disease status, etc.-are not provided in structured data fields but rather are encoded in clinician-generated narrative text. Natural language processing (NLP) provides a means of unlocking this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we review briefly. Additionally, the challenge facing current research efforts in biomedical NLP includes the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Philippines | 1 | 25% |
| Norway | 1 | 25% |
| Argentina | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Practitioners (doctors, other healthcare professionals) | 2 | 50% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 3% |
| Germany | 2 | 1% |
| Spain | 2 | 1% |
| South Africa | 1 | <1% |
| Indonesia | 1 | <1% |
| Australia | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 154 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 30 | 18% |
| Student > Ph. D. Student | 28 | 17% |
| Student > Master | 15 | 9% |
| Other | 11 | 7% |
| Student > Bachelor | 11 | 7% |
| Other | 35 | 21% |
| Unknown | 37 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 55 | 33% |
| Medicine and Dentistry | 29 | 17% |
| Agricultural and Biological Sciences | 11 | 7% |
| Nursing and Health Professions | 3 | 2% |
| Psychology | 3 | 2% |
| Other | 18 | 11% |
| Unknown | 48 | 29% |