The TF-IDF measure and analysis of links between words within N-grams in the formation of knowledge units for open tests

Emelyanov, G. M.; Mikhailov, D. V.; Kozlov, A.

doi:10.1134/s1054661817040058

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…We applied the term frequency-inverse document frequency (TF-IDF) algorithm (20)(21)(22) and cosine similarity to match publication abstracts and patent documents by assessing text similarity. For each hospital, we built one TF-IDF library for publications and one for patents and then calculated two text similarity matrixes based on each TF-IDF library.…”

Section: Data Collectionmentioning

confidence: 99%

Evaluation of Chinese healthcare organizations' innovative performance in the digital health era

Shu

Chen

et al. 2023

Front. Public Health

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BackgroundHealthcare workers' relationship with industry is not merely an agent mediating between consumer and vendor, but they are also inventors of the interventions they exist to deliver. Driven by the background of the digital health era, scientific research and technological (Sci-tech) innovation in the medical field are becoming more and more closely integrated. However, scholars shed little light on Sci-tech relevance to evaluate the innovation performance of healthcare organizations, a distinctive feature of healthcare organizations' innovation in the digital health era.MethodsAcademic publications and patents are the manifestations of scientific research outputs and technological innovation outcomes, respectively. The study extracted data from publications and patents of 159 hospitals in China to evaluate their innovation performance. A total of 18 indicators were constructed, four of which were based on text similarity match and represented the Sci-tech relevance. We then applied factor analyses, analytical hierarchy process, and logistic regression to construct an evaluation model. We also examined the relationship between hospitals' innovation performance and their geographical locations. Finally, we implemented a mediation analysis to show the influence of digital health on hospital innovation performance.ResultsA total of 16 indicators were involved, four of which represented the Sci-tech including the number of articles matched per patent (NAMP), the number of patents matched per article (NPMA), the proportion of highly matched patents (HMP), and the proportion of highly matched articles (HMA). Indicators of HMP (r = 0.52, P = 2.40 × 10−12), NAMP (r = 0.52, P = 2.54 × 10−12), and NPMA (r = 0.51, P = 5.53 × 10−12) showed a strong positive correlation with hospital innovation performance score. The evaluation model in this study was different from other Chinese existing hospital ranking systems. The regional innovation performance index (RIP) of healthcare organizations is highly correlated with per capita disposable income (r = 0.58) and regional GDP (r = 0.60). There was a positive correlation between digital health innovation performance scores and overall hospital innovation performance scores (r = 0.20). In addition, the hospitals' digital health innovation performance affected the hospital's overall innovation score with the mediation of Sci-tech relevance indicators (NPMA and HMA). The hospitals' digital health innovation performance score showed a significant correlation with the number of healthcare workers (r = 0.44).ConclusionThis study constructed an assessment model with four invented indicators focusing on Sci-tech relevance to provide a novel tool for researchers to evaluate the innovation performance of healthcare organizations in the digital health era. The regions with high RIP were concentrated on the eastern coastal areas with a higher level of economic development. Therefore, the promotion of scientific and technological innovation policies could be carried out in advance in areas with better economic development. The innovations in the digital health field by healthcare workers enhance the Sci-tech relevance in hospitals and boost their innovation performance. The development of digital health in hospitals depends on the input of medical personnel.

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Section: Data Collectionmentioning

confidence: 99%

Evaluation of Chinese healthcare organizations' innovative performance in the digital health era

Shu

Chen

et al. 2023

Front. Public Health

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“…Once the Document Frequency value of each feature is calculated, appropriate features are selected through the threshold. (Kshirsagar et al, 2020;Mestry et al, 2019;Gao et al, 2019;Kermani et al, 2019;Othman et al, 2019;Sidorov et al, 2019;Maryam et al, 2018;Das et al, 2018;Yamout et al, 2018;White et al, 2018;Alsmadi et al, 2018;Gu et al, 2018;Emelyanov et al, 2017;Chen et al, 2017;Krouska et al, 2016).…”

Section: Features Selection and Extractionmentioning

confidence: 99%

Influence of Pre-Processing Strategies on the Performance of ML Classifiers Exploiting TF-IDF and BOW Features

Pimpalkar

Raj

2020

ADCAIJ

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Data analytics and its associated applications have recently become impor-tant fields of study. The subject of concern for researchers now-a-days is a massive amount of data produced every minute and second as people con-stantly sharing thoughts, opinions about things that are associated with them. Social media info, however, is still unstructured, disseminated and hard to handle and need to be developed a strong foundation so that they can be utilized as valuable information on a particular topic. Processing such unstructured data in this area in terms of noise, co-relevance, emoticons, folksonomies and slangs is really quite challenging and therefore requires proper data pre-processing before getting the right sentiments. The dataset is extracted from Kaggle and Twitter, pre-processing performed using NLTK and Scikit-learn and features selection and extraction is done for Bag of Words (BOW), Term Frequency (TF) and Inverse Document Frequency (IDF) scheme. For polarity identification, we evaluated five different Machine Learning (ML) algorithms viz Multinomial Naive Bayes (MNB), Logistic Regression (LR), Decision Trees (DT), XGBoost (XGB) and Support Vector Machines (SVM). We have performed a comparative analysis of the success for these algorithms in order to decide which algorithm works best for the given data-set in terms of recall, accuracy, F1-score and precision. We assess the effects of various pre-processing techniques on two datasets; one with domain and other not. It is demonstrated that SVM classifier outperformed the other classifiers with superior evaluations of 73.12% and 94.91% for accuracy and precision respectively. It is also highlighted in this research that the selection and representation of features along with various pre-processing techniques have a positive impact on the performance of the classification. The ultimate outcome indicates an improvement in sentiment classification and we noted that pre-processing approaches obviously suggest an improvement in the efficiency of the classifiers.

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“…There are three main types of similarity computations [8]. The first type is based on keyword matching, e.g., the N-gram method [9] or the Jaccard method [10]. The second type is based on vector spaces and is the most widely used method at present.…”

Section: Introductionmentioning

confidence: 99%

A Correlation-Experience-Demand Based Personalized Knowledge Recommendation Approach

et al. 2019

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Knowledge recommendation is an important means of knowledge reuse that can improve the efficiency and quality of product design. However, at present, there is no good way to fully consider the personalized demands of designers while ensuring the applicability of the recommendation results. Previous studies have usually been based on the similarity between tasks and knowledge or use collaborative filtering technology to accomplish knowledge recommendation. However, these methods do not consider the personal experience of designers and the characteristics of knowledge. This paper proposes a knowledge recommendation approach that integrates the degree of correlation between knowledge and tasks, the feedback-based personal experience, the collective experience of designers, and the degree of demand for knowledge based on the forgetting curve. A knowledge assistance score is generated based on these factors, and the knowledge recommendation list is obtained by ranking the knowledge in descending order of this score. Finally, the approach is applied to a machine shop layout design task and a computer numerical control (CNC) machine tool's spindle design and bearings selection task. The experimental results on two tasks demonstrate that the proposed approach outperforms three baselines on three ranking oriented evaluation metrics. This approach can effectively shorten the time for designers to acquire knowledge by recommending applicable knowledge to assist designers in completing design tasks with high quality and efficiency. INDEX TERMS Collaborative filtering, degree of assistance, degree of correlation, degree of demand, knowledge recommendation, ontology model.

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The TF-IDF measure and analysis of links between words within N-grams in the formation of knowledge units for open tests

Cited by 7 publications

References 4 publications

Evaluation of Chinese healthcare organizations' innovative performance in the digital health era

Evaluation of Chinese healthcare organizations' innovative performance in the digital health era

Influence of Pre-Processing Strategies on the Performance of ML Classifiers Exploiting TF-IDF and BOW Features

A Correlation-Experience-Demand Based Personalized Knowledge Recommendation Approach

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