Text Mining Using N-Grams

Schonlau, Matthias; Guenther, Nick

doi:10.2139/ssrn.2759033

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…N-grams help identify common expressions that contain one word or more (e.g., "pain" is a 1-gram, "severe pain" is a 2-gram, and "very severe pain" is a 3-gram). Using n-grams can help identify expressions with relatively high predictive value for projecting hospitalizations and ED visits (Schonlau & Guenther, 2016).…”

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confidence: 99%

Home Healthcare Clinical Notes Predict Patient Hospitalization and Emergency Department Visits

et al. 2020

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Background: About 30% of home health care patients are hospitalized or visit an emergency department (ED) during a home health care (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. Objectives:To identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).Methods: This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit home health care agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation). Results:Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/ communication, and service use being the most frequent categories.Discussion: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources. Keywordshome health care; natural language processing; nursing informatics; risk prediction; text mining Every year, more than 11,000 home health care (HHC) agencies across the United States provide care to more than 5 million older adults (MedPac, 2014). Currently, about one in three HHC patients are hospitalized or visit an emergency department (ED) during the 30-

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confidence: 99%

Home Healthcare Clinical Notes Predict Patient Hospitalization and Emergency Department Visits

et al. 2020

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“…In the domain of text mining, the extracted text is transformed into variables on which the machine learning, such as classification, can be performed. The n-gram model is one of the approaches to create bag-of-words (BoW) to represent the sequence of the words regardless of the grammar and word order [59].…”

Section: Feature Extractionmentioning

confidence: 99%

Contextual Text Analytics Framework for Citizen Report Classification: A Case Study Using the Indonesian Language

2022

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Citizen science has emerged in many countries to contribute to the prompt resolution of individual field problems and has been shifted toward Information System (IS) research. In the domain of IS, a citizen report mechanism has been introduced in many local governments to understand regional problems based on the public participation. The rising of social media enforces many organizations including the local governments to utilize any information from the citizens including texts. Text mining has been utilized in various types of analyses such as sentiment analysis. However, it shows many challenges when it comes to the local context. The local context of words could cause various conflation errors that highly affect the learning task such as classification methods. This study aims to propose a context-based text processing and feed the proposed approach into a machine learning framework to classify the data of citizen reports-. The context-based text preprocessing utilized statistical-and semantic-based measurements to extract the local context and elaborate domain expertise to verify the misinterpretation for further text processing such as feature extractions. Subsequently, the n-gram language models together with the Term Frequency and Inverse Document Frequency schemes were performed to build the features. The result showed that the context-based text preprocessing improved the classification performance in majority classifiers in about 3% with the combinations of n-gram features.

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“…A gram is a result of slicing a string into substrings using either character or word [3], [6], [33], [34], [35]. Unigrams, bigrams, or trigrams are units in n-gram terms that indicate the length of a gram.…”

Section: B Grams Formationmentioning

confidence: 99%

Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

2022

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Several studies regarding excellent exact string matching algorithms can be used to identify similarity, including the Rabin-Karp, Winnowing, and Horspool Boyer-Moore algorithms. In determining similarities, the Rabin-Karp and Winnowing algorithms use fingerprints, while the Horspool Boyer-Moore algorithm uses a bad-character table. However, previous research focused on identifying similarities using these algorithms based on character n-gram. In contrast, identification based on the word n-gram to determine the similarity based on its linguistic meaning, especially for longer strings, had not been covered yet. Therefore, a word-level trigram was proposed to identify similarities based on the word trigrams using the three algorithms and compare each performance. Based on precision, recall, and running time comparison, the Rabin-Karp algorithm results were 100%, 100%, and 0.19 ms, respectively; the Winnowing algorithm results with the smallest window were 100%, 56%, and 0.18 ms, respectively; and the Horspool algorithm results were 100%, 100%, and 0.06 ms. From these results, it can be concluded that the performance of the Horspool Boyer-Moore algorithm is better in terms of precision, recall, and running time.

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Text Mining Using N-Grams

Cited by 12 publications

References 8 publications

Home Healthcare Clinical Notes Predict Patient Hospitalization and Emergency Department Visits

Home Healthcare Clinical Notes Predict Patient Hospitalization and Emergency Department Visits

Contextual Text Analytics Framework for Citizen Report Classification: A Case Study Using the Indonesian Language

Similarity Identification Based on Word Trigrams Using Exact String Matching Algorithms

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