Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care

Mahendra, Malini; Luo, Y; Mills, Hunter; Schenk, Gundolf; Butte, Atul J.; Dudley, R. Adams

doi:10.1097/cce.0000000000000450

Cited by 7 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impact of preprocessing methods on the model performance can be significant and those methods are therefore essential to report. 30 The sparse Bag-of-Words and TFIDF representations and the dense word and document embeddings were most frequently used and we found an association between the types of text representation and machine learning methods. The neural network methods generally used a dense text representation, while regularized logistic regression methods, random forests, or SVMs largely took sparse representations as input.…”

Section: Discussionmentioning

confidence: 78%

Use of unstructured text in prognostic clinical prediction models: a systematic review

Seinen

Friðgeirsson

Ioannou

et al. 2022

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

Objective This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance. Materials and Methods We searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic prediction models using information extracted from unstructured text in a data-driven manner, published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models. Results We identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared with using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and attention for the explainability of the developed models were limited. Conclusion The use of unstructured text in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The text data are source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.

show abstract

Section: Discussionmentioning

confidence: 78%

Use of unstructured text in prognostic clinical prediction models: a systematic review

Seinen

Friðgeirsson

Ioannou

et al. 2022

Journal of the American Medical Informatics Association

View full text Add to dashboard Cite

show abstract

“…Relevant for us is the fact that stemming has been shown to add semantic value in feature selection, as for example Biba and Gjati (2014) proved that stemming of composite words greatly improves classification of fake news. Moreover, Mahendra et al (2021) showed that cleaning and stemming resulted in the greatest model performance on the medical domain for the task in mortality prediction on ICU (Intensive Care Unit) patients. We refer readers to a thorough survey of stemmers spanning over the past 50 years by Singh and Gupta (2016).…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)

2023

View full text Add to dashboard Cite

This year's workshop consists of 3 oral and 5 poster presentations of accepted papers (54% overall acceptance rate), 12 poster presentations from EACL Findings papers, presentations from 4 invited speakers, as well as a panel discussion with 6 panellists. The workshop is held in hybrid mode with in-person and virtual poster sessions, live-streamed panel discussion, oral presentations, and invited talks.The organisers would like to thank the authors of all submitted papers, the reviewers, the panelists, and the invited speakers for their efforts, and we are looking forward to next year's edition.

show abstract

“…To give just one example, the word vectors produced contain both the adjective and adverb forms of the word 12 . Mahendra et al 13 used the Term Frequency-Inverse Document Frequency technique to find a middle ground between the advantages of the association between words and documents and words and corpus. They overcame the problem of existing word vectors not being able to effectively present a document’s data.…”

Section: Research Reviewmentioning

confidence: 99%

“…They overcame the problem of existing word vectors not being able to effectively present a document’s data. It turned out that word2vec works well when combined with the estimated word weights 13 . Yadav et al 14 employed Convolutional Neural Networks (CNNs) in conjunction with attention mechanisms, leveraging deep learning techniques to develop a digital system for managing ICH and creating an automatic classification model.…”

Section: Research Reviewmentioning

confidence: 99%

Fusion of the word2vec word embedding model and cluster analysis for the communication of music intangible cultural heritage

Ning,

Chen

2023

Sci Rep

View full text Add to dashboard Cite

This article aims to propose a method for computing the similarity between lengthy texts on intangible cultural heritage (ICH), to facilitate the swift and efficient acquisition of knowledge about it by the public and promote the dissemination and preservation of this culture. This proposed method builds on traditional text similarity techniques. The ultimate goal is to group together those lengthy texts on ICH that exhibit a high degree of similarity. First of all, the word2vec model is utilized to construct the feature word vector of music ICH communication. This includes the acquisition of long text data on music ICH, the word segmentation of music ICH communication based on the dictionary method in the field of ICH, and the creation of a word2vec model of music ICH communication. A clustering algorithm analyzes and categorizes ICH communication within the music. This procedure involves employing text semantic similarity, utilizing a similarity calculation method based on optimized Word Mover Distance (WMD), and designing long ICH communication clustering. The main objective of this analysis is to enhance the understanding and classification of the intricate nature of ICH within the musical realm. Finally, experiments are conducted to confirm the model’s effectiveness. The results show that: (1) the text word vector training based on the word2vec model is highly accurate; (2) with the increase in K value, the effect of each category of intangible word vector is improving; (3) the final F1-measure value of the clustering experiment based on the optimized WMD is 0.84. These findings affirm the usefulness and accuracy of the proposed methodology.

show abstract

Impact of Different Approaches to Preparing Notes for Analysis With Natural Language Processing on the Performance of Prediction Models in Intensive Care

Abstract: Supplemental Digital Content is available in the text.

Cited by 7 publications

References 31 publications

Use of unstructured text in prognostic clinical prediction models: a systematic review

Use of unstructured text in prognostic clinical prediction models: a systematic review

Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)

Fusion of the word2vec word embedding model and cluster analysis for the communication of music intangible cultural heritage

Contact Info

Product

Resources

About