Automated Modeling of Clinical Narrative with High Definition Natural Language Processing Using Solor and Analysis Normal Form

Objective: One important concept in informatics is data which meets the principles of Findability, Accessibility, Interoperability and Reusability (FAIR). Standards, such as terminologies (findability), assist with important tasks like interoperability, Natural Language Processing (NLP) (accessibility) and decision support (reusability). One terminology, Solor, integrates SNOMED CT, LOINC and RxNorm. We describe Solor, HL7 Analysis Normal Form (ANF), and their use with the high definition natural language processing (HD-NLP) program. Methods: We used HD-NLP to process 694 clinical narratives prior modeled by human experts into Solor and ANF. We compared HD-NLP output to the expert gold standard for 20% of the sample. Each clinical statement was judged “correct” if HD-NLP output matched ANF structure and Solor concepts, or “incorrect” if any ANF structure or Solor concepts were missing or incorrect. Judgements were summed to give totals for “correct” and “incorrect”. Results: 113 (80.7%) correct, 26 (18.6%) incorrect, and 1 error. Inter-rater reliability was 97.5% with Cohen’s kappa of 0.948. Conclusion: The HD-NLP software provides useable complex standards-based representations for important clinical statements designed to drive CDS.

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Applying Natural Language Processing, Information Retrieval and Machine Learning to Decision Support in Medical Coordination in an Emergency Medicine Context

2015 IEEE 28th International Symposium on Computer-Based Medical Systems ◽

10.1109/cbms.2015.82 ◽

2015 ◽

Cited By ~ 2

Author(s):

Juliana Tarossi Pollettini ◽

Hugo Cesar Pessotti ◽

Antonio Pazin Filho ◽

Evandro Eduardo Seron Ruiz ◽

Mario Sergio Adolfi Junior

Keyword(s):

Machine Learning ◽

Emergency Medicine ◽

Information Retrieval ◽

Natural Language Processing ◽

Decision Support ◽

Natural Language ◽

Language Processing ◽

Processing Information

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Clinical Decision Support With Natural Language Processing Facilitates Determination of Colonoscopy Surveillance Intervals

Clinical Gastroenterology and Hepatology ◽

10.1016/j.cgh.2013.11.025 ◽

2014 ◽

Vol 12 (7) ◽

pp. 1130-1136 ◽

Cited By ~ 20

Author(s):

Timothy D. Imler ◽

Justin Morea ◽

Thomas F. Imperiale

Keyword(s):

Natural Language Processing ◽

Decision Support ◽

Natural Language ◽

Clinical Decision Support ◽

Language Processing ◽

Clinical Decision ◽

Colonoscopy Surveillance

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Constituency Parser for Clinical Narratives using NLP

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8865.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 2277-2279

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Sentence Recall ◽

Structured Report ◽

Clinical Narrative ◽

Medical Domain ◽

Readable Format ◽

F Measure

Clinical parsing is useful in medical domain .Clinical narratives are difficult to understand as it is in unstructured format .Medical Natural language processing systems are used to make these clinical narratives in readable format. Clinical Parser is the combination of natural language processing and medical lexicon .For making clinical narrative understandable parsing technique is used .In this paper we are discussing about constituency parser for clinical narratives, which is based on phrase structured grammar. This parser convert unstructured clinical narratives into structured report. This paper focus on clinical sentences which is in unstructured format after parsing convert into structured format. For each sentence recall ,precision and bracketing f- measure are calculated .

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LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

Journal of Information & Knowledge Management ◽

10.1142/s0219649221500064 ◽

2021 ◽

pp. 2150006

Author(s):

Saravanakumar Kandasamy ◽

Aswani Kumar Cherukuri

Keyword(s):

Information Retrieval ◽

Natural Language Processing ◽

Natural Language ◽

Semantic Similarity ◽

Language Processing ◽

Gold Standard ◽

Question Answering ◽

Knowledge Based ◽

Benchmark Datasets ◽

Processing Information

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

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Annotation of phenotypes using ontologies: a gold standard for the training and evaluation of natural language processing systems

Database ◽

10.1093/database/bay110 ◽

2018 ◽

Vol 2018 ◽

Cited By ~ 8

Author(s):

Wasila Dahdul ◽

Prashanti Manda ◽

Hong Cui ◽

James P Balhoff ◽

T Alexander Dececchi ◽

...

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Gold Standard

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Textual entailment graphs

Natural Language Engineering ◽

10.1017/s1351324915000108 ◽

2015 ◽

Vol 21 (5) ◽

pp. 699-724 ◽

Cited By ~ 6

Author(s):

LILI KOTLERMAN ◽

IDO DAGAN ◽

BERNARDO MAGNINI ◽

LUISA BENTIVOGLI

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Gold Standard ◽

State Of The Art ◽

Text Analytics ◽

Joint Work ◽

Gold Standard Dataset ◽

Textual Entailment ◽

Interesting Task

AbstractIn this work, we present a novel type of graphs for natural language processing (NLP), namely textual entailment graphs (TEGs). We describe the complete methodology we developed for the construction of such graphs and provide some baselines for this task by evaluating relevant state-of-the-art technology. We situate our research in the context of text exploration, since it was motivated by joint work with industrial partners in the text analytics area. Accordingly, we present our motivating scenario and the first gold-standard dataset of TEGs. However, while our own motivation and the dataset focus on the text exploration setting, we suggest that TEGs can have different usages and suggest that automatic creation of such graphs is an interesting task for the community.

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Clinical decision support system, using expert consensus-derived logic and natural language processing, decreased sedation-type order errors for patients undergoing endoscopy

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa250 ◽

2020 ◽

Author(s):

Lin Shen ◽

Adam Wright ◽

Linda S Lee ◽

Kunal Jajoo ◽

Jennifer Nayor ◽

...

Keyword(s):

Natural Language Processing ◽

Decision Support ◽

Natural Language ◽

Clinical Decision Support ◽

Language Processing ◽

Support System ◽

Clinical Decision Support System ◽

Clinical Decision ◽

Expert Consensus ◽

Order Error

Abstract Objective Determination of appropriate endoscopy sedation strategy is an important preprocedural consideration. To address manual workflow gaps that lead to sedation-type order errors at our institution, we designed and implemented a clinical decision support system (CDSS) to review orders for patients undergoing outpatient endoscopy. Materials and Methods The CDSS was developed and implemented by an expert panel using an agile approach. The CDSS queried patient-specific historical endoscopy records and applied expert consensus-derived logic and natural language processing to identify possible sedation order errors for human review. A retrospective analysis was conducted to evaluate impact, comparing 4-month pre-pilot and 12-month pilot periods. Results 22 755 endoscopy cases were included (pre-pilot 6434 cases, pilot 16 321 cases). The CDSS decreased the sedation-type order error rate on day of endoscopy (pre-pilot 0.39%, pilot 0.037%, Odds Ratio = 0.094, P-value < 1e-8). There was no difference in background prevalence of erroneous orders (pre-pilot 0.39%, pilot 0.34%, P = .54). Discussion At our institution, low prevalence and high volume of cases prevented routine manual review to verify sedation order appropriateness. Using a cohort-enrichment strategy, a CDSS was able to reduce number of chart reviews needed per sedation-order error from 296.7 to 3.5, allowing for integration into the existing workflow to intercept rare but important ordering errors. Conclusion A workflow-integrated CDSS with expert consensus-derived logic rules and natural language processing significantly reduced endoscopy sedation-type order errors on day of endoscopy at our institution.

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The Concept of Integrating Artificial Intelligence Technologies Into Human Resources in a Digital Paradigm

Management of the personnel and intellectual resources in Russia ◽

10.12737/2305-7807-2020-5-9 ◽

2020 ◽

Vol 9 (2) ◽

pp. 5-9

Author(s):

Oksana Chulanova

Keyword(s):

Artificial Intelligence ◽

Computer Vision ◽

Natural Language Processing ◽

Decision Support ◽

Speech Recognition ◽

Human Resources ◽

Natural Language ◽

Language Processing

The article discusses the capabilities of artificial intelligence technologies - technologies based on the use of artificial intelligence, including natural language processing, intellectual decision support, computer vision, speech recognition and synthesis, and promising methods of artificial intelligence. The results of the author's study and the analysis of artificial intelligence technologies and their capabilities for optimizing work with staff are presented. A study conducted by the author allowed us to develop an author's concept of integrating artificial intelligence technologies into work with personnel in the digital paradigm.

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