scholarly journals Patient-oriented natural language processing: Defining a new paradigm for research and development to facilitate adoption and utilization by medical experts (Preprint)

10.2196/18471 ◽  
2020 ◽  
Author(s):  
Abeed Sarker ◽  
Mohammed Ali Al-Garadi ◽  
Yuan-Chi Yang ◽  
Jinho Choi ◽  
Arshed A Quyyumi ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
New Paradigm

scholarly journals Patient-oriented natural language processing: Defining a new paradigm for research and development to facilitate adoption and utilization by medical experts (Preprint)

2020 ◽  
Author(s):  
Abeed Sarker ◽  
Mohammed Ali Al-Garadi ◽  
Yuan-Chi Yang ◽  
Jinho Choi ◽  
Arshed A Quyyumi ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Data Science ◽  
New Paradigm ◽  
Heterogeneous Datasets ◽  
System Properties ◽  
New Research ◽  
Specific Symptoms

UNSTRUCTURED The capabilities of natural language processing (NLP) methods have expanded significantly in recent years, particularly driven by advances in data science and machine learning. However, the utilization of NLP for patient-oriented clinical research and care (POCRC) is still limited. A primary reason behind this is perhaps the fact that clinical NLP methods are developed, optimized, and evaluated on narrow-focus datasets and tasks (e.g., for the detection of specific symptoms from free texts). Such research and development (R&D) approaches may be described as problem-oriented, and the developed systems only perform well for a given specialized task. As standalone systems, they are also typically not suitable for addressing the needs of POCRC, leaving a gap between the capabilities of clinical NLP methods and the needs of patient-facing medical experts. We believe that to make clinical NLP systems more valuable, future R&D efforts need to follow a new research paradigm, one that explicitly incorporates characteristics that are crucial for POCRC. We present our viewpoint about four interrelated characteristics, three representing NLP system properties and one associated with the R&D process—(i) generalizability (capability to characterize patients, not clinical problems), (ii) interpretability (ability to explain system decisions), (iii) customizability (flexibility for adaptation to distinct settings, problems and cohorts), and (iv) cross-evaluation (validated performance on heterogeneous datasets)—that are relevant for NLP systems suitable for POCRC. Using the NLP task of clinical concept detection as an example, we detail these characteristics and discuss how they may lead to increased uptake of NLP systems for POCRC.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

2019 ◽  
Vol 53 (2) ◽  
pp. 3-10
Author(s):  
Muthu Kumar Chandrasekaran ◽  
Philipp Mayr
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Digital Libraries ◽  
State Of The Art ◽  
Shared Task ◽  
Processing Information ◽  
Joint Workshop

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

Research and development in natural language processing at BBN laboratories in the strategic computing program

1986 ◽  
Author(s):  
Ralph Weischedel ◽  
David Stallard ◽  
Remko Scha ◽  
Edward Walker ◽  
Damaris Ayuso ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing

scholarly journals Supporting secondary research in early drug discovery process through a Natural Language Processing based system

2020 ◽  
Vol 2 (1) ◽  
pp. 254-267
Author(s):  
Alina Popa
Keyword(s):  
Natural Language Processing ◽  
Drug Discovery ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Entity Recognition ◽  
Topic Modelling ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Secondary Research

Abstract Last decades were characterised by a constant decline in the productivity of research and development activities of pharmaceutical companies. This is due to the fact that the drug discovery process contains an intrinsic risk that should be managed efficiently. Within this process, the early phase projects could be streamlined by doing more secondary research. These activities would involve the integration of chemical and biological knowledge from scientific literature in order to extract an overview and the evolution of a certain research area. This would then help refine the research and development operations. Considering the vast amount of pharmaceutical studies publications, it is not easy to identify the important information. For this task, a series of projects leveraged the advantages of the open pharmacological space through state-of-the-art technologies. The most popular are Knowledge Graphs methods. Although extremely useful, this technology requires increased investments of time and human resources. An alternative would be to develop a system that uses Natural Language Processing blocks. Still, there is no defined framework and reusable code template for the use-case of compounds development. In this study, it is presented the design and development of a system that uses Dynamic Topic Modelling and Named Entity Recognition modules in order to extract meaningful information from a large volume of unstructured texts. Moreover, the dynamic character of the topic modelling technique allows to analyse the evolution of different subject areas over time. In order to validate the system, a collection of articles from the Pharmaceutical Research Journal was used. Our results show that the system is able to identify the main research areas in the last 20 years, namely crystalline and amorphous systems, insulin resistance, paracellular permeability. Additionally, the evolution of the subjects is a highly valuable resource and should be used to get an in-depth understanding about the shifts that happened in a specific domain. However, a limitation of this system is that it cannot detect association between two concepts or entities if they are not involved in the same document.

A Critical Review of the Current State of Natural Language Processing in Mexico and Chile

2021 ◽  
pp. 365-389
Author(s):  
César Aguilar ◽  
Olga Acosta
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Language Processing ◽  
Critical Review ◽  
Indigenous Languages ◽  
Technological Evolution ◽  
General Review ◽  
Current State ◽  
Strategic Policy

This chapter presents a critical review of the current state of natural language processing in Chile and Mexico. Specifically, a general review is made regarding the technological evolution of these countries in this area of research and development, as well as the progress they have made so far. Subsequently, the remaining problems and challenges are addressed. Specifically, two are analyzed in detail here: (1) the lack of a strategic policy that helps to establish stronger links between academia and industry and (2) the lack of a technological inclusion of the indigenous languages, which causes a deep digital divide between Spanish (considered in Chile and Mexico as their official language) with them.

scholarly journals Box: Natural Language Processing Research Using Amazon Web Services

2015 ◽  
Vol 104 (1) ◽  
pp. 27-38
Author(s):  
Amittai Axelrod
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Research And Development ◽  
Machine Translation ◽  
Language Processing ◽  
State Of The Art ◽  
Research Community ◽  
Development Platform ◽  
Amazon Web Services ◽  
Art Research

Abstract We present a publicly-available state-of-the-art research and development platform for Machine Translation and Natural Language Processing that runs on the Amazon Elastic Compute Cloud. This provides a standardized research environment for all users, and enables perfect reproducibility and compatibility. Box also enables users to use their hardware budget to avoid the management and logistical overhead of maintaining a research lab, yet still participate in global research community with the same state-of-the-art tools.

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