Comparison of ACM and CLAMP for Entity Extraction in Clinical Notes

2021 ◽  
Author(s):  
Fatemeh Shah-Mohammadi ◽  
Wanting Cui ◽  
Joseph Finkelstein
Keyword(s):  
Entity Extraction ◽  
Clinical Notes

scholarly journals Entity Extraction for Clinical Notes, a Comparison Between MetaMap and Amazon Comprehend Medical

2021 ◽  
Author(s):  
Fatemeh Shah-Mohammadi ◽  
Wanting Cui ◽  
Joseph Finkelstein
Keyword(s):  
Risk Factors ◽  
Clinical Research ◽  
Healthcare Quality ◽  
Entity Extraction ◽  
Average Precision ◽  
Extraction Capacity ◽  
Clinical Notes ◽  
Meaningful Information ◽  
Medical Entity ◽  
Discharge Summaries

Extracting meaningful information from clinical notes is challenging due to their semi- or unstructured format. Clinical notes such as discharge summaries contain information about diseases, their risk factors, and treatment approaches associated to them. As such, it is critical for healthcare quality as well as for clinical research to extract those information and make them accessible to other computerized applications that rely on coded data. In this context, the goal of this paper is to compare the automatic medical entity extraction capacity of two available entity extraction tools: MetaMap (MM) and Amazon Comprehend Medical (ACM). Recall, precision and F-score have been used to evaluate the performance of the tools. The results show that ACM achieves higher average recall, average precision, and average F-score in comparison with MM.

Some Clinical Notes on Forced Stuttering

1972 ◽  
Vol 37 (2) ◽  
pp. 177-186 ◽  
Author(s):  
Oliver Bloodstein ◽  
Roberta Levy Shogan
Keyword(s):  
Loss Of Control ◽  
Clinical Notes ◽  
High Degree

Stutterers sometimes report that by exerting articulatory pressure they can force themselves to have “real” blocks. A procedure was devised for instructing subjects to force stuttering under various conditions and for recording their introspections. Most subjects were able to force at least a few blocks which they regarded as real. Most of the words on which the attempts were said to succeed were feared or difficult words, and at times subjects assisted the process by “telling” themselves that they would not be able to say the word. Fewer subjects were able to force blocks on isolated sounds than on words, and almost none claimed to succeed on mere articulatory contacts. Subjects repeatedly characterized “real” stuttering as involving feelings of physical tension and loss of control over speech. The nature of the forced block is discussed with reference to a concept of stuttering as a struggle reaction which has acquired a high degree of automaticity.

scholarly journals Improving Topic Coherence Using Entity Extraction Denoising

2018 ◽  
Vol 110 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Ronald Cardenas ◽  
Kevin Bello ◽  
Alberto Coronado ◽  
Elizabeth Villota
Keyword(s):  
Topic Modeling ◽  
Human Perception ◽  
Relevant Information ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Fine Grained ◽  
Job Advertisement ◽  
Coherence Score ◽  
Probabilistic Topic Modeling ◽  
Promising Solution

Abstract Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method and the evaluation of this model is an interesting problem on its own. Topic interpretability measures have been developed in recent years as a more natural option for topic quality evaluation, emulating human perception of coherence with word sets correlation scores. In this paper, we show experimental evidence of the improvement of topic coherence score by restricting the training corpus to that of relevant information in the document obtained by Entity Recognition. We experiment with job advertisement data and find that with this approach topic models improve interpretability in about 40 percentage points on average. Our analysis reveals as well that using the extracted text chunks, some redundant topics are joined while others are split into more skill-specific topics. Fine-grained topics observed in models using the whole text are preserved.

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