Background Myocardial infarction can lead to malignant arrhythmia, heart failure, and sudden death. Clinical studies have shown that early identification of and timely intervention for acute MI can significantly reduce mortality. The traditional MI risk assessment models are subjective, and the data that go into them are difficult to obtain. Generally, the assessment is only conducted among high-risk patient groups. Objective To construct an artificial intelligence–based risk prediction model of myocardial infarction (MI) for continuous and active monitoring of inpatients, especially those in noncardiovascular departments, and early warning of MI. Methods The imbalanced data contain 59 features, which were constructed into a specific dataset through proportional division, upsampling, downsampling, easy ensemble, and w-easy ensemble. Then, the dataset was traversed using supervised machine learning, with recursive feature elimination as the top-layer algorithm and random forest, gradient boosting decision tree (GBDT), logistic regression, and support vector machine as the bottom-layer algorithms, to select the best model out of many through a variety of evaluation indices. Results GBDT was the best bottom-layer algorithm, and downsampling was the best dataset construction method. In the validation set, the F1 score and accuracy of the 24-feature downsampling GBDT model were both 0.84. In the test set, the F1 score and accuracy of the 24-feature downsampling GBDT model were both 0.83, and the area under the curve was 0.91. Conclusion Compared with traditional models, artificial intelligence–based machine learning models have better accuracy and real-time performance and can reduce the occurrence of in-hospital MI from a data-driven perspective, thereby increasing the cure rate of patients and improving their prognosis.
BackgroundIt has been shown that the entities in everyday clinical text are often expressed in a way that varies from how they are expressed in the nomenclature. Owing to lots of synonyms, abbreviations, medical jargons or even misspellings in the daily used physician notes in clinical information system (CIS), the terminology without enough synonyms may not be adequately suitable for the task of Chinese clinical term recognition.MethodsThis paper demonstrates a validated system to retrieve the Chinese term of clinical finding (CTCF) from CIS and map them to the corresponding concepts of international clinical nomenclature, such as SNOMED CT. The system focuses on the SNOMED CT with Chinese synonyms enrichment (SCCSE). The literal similarity and the diagnosis-related similarity metrics were used for concept mapping. Two CTCF recognition methods, the rule- and terminology-based approach (RTBA) and the conditional random field machine learner (CRF), were adopted to identify the concepts in physician notes. The system was validated against the history of present illness annotated by clinical experts. The RTBA and CRF could be combined to predict new CTCFs besides SCCSE persistently.ResultsAround 59,000 CTCF candidates were accepted as valid and 39,000 of them occurred at least once in the history of present illness. 3,729 of them were accordant with the description in referenced Chinese clinical nomenclature, which could cross map to other international nomenclature such as SNOMED CT. With the hybrid similarity metrics, another 7,454 valid CTCFs (synonyms) were succeeded in concept mapping. For CTCF recognition in physician notes, a series of experiments were performed to find out the best CRF feature set, which gained an F-score of 0.887. The RTBA achieved a better F-score of 0.919 by the CTCF dictionary created in this research.ConclusionsThis research demonstrated that it is feasible to help the SNOMED CT with Chinese synonyms enrichment based on physician notes in CIS. With continuous maintenance of SCCSE, the CTCFs could be precisely retrieved from free text, and the CTCFs arranged in semantic hierarchy of SNOMED CT could greatly improve the meaningful use of electronic health record in China. The methodology is also useful for clinical synonyms enrichment in other languages.
This study aims to provide a bibliometric overview of research at nursing informatics and understand the state in nursing informatics in the last ten years. We used the Web of Science to extract relevant literature published from 2009 to 2018. A total of 455 articles were retrieved and analyzed. The total of the top 5 institutions, countries, journals was discussed. This study will help researchers to understand trends and the situation in nursing informatics research.
Kiwifruit is very popular for its unique flavor and nutritional value, and for its potential health benefits, which are closely related to its richness in a variety of natural antioxidant substances, in which polyphenolics play a non-negligible role. This study investigated changes in the fruit quality, phenolic compounds, and antioxidant potential of Chinese red-fleshed kiwifruit “Hongshi No. 2” during postharvest ripening at room temperature (20 ± 1 °C). Results showed that the weight loss rate slowly increased, the firmness rapidly decreased, and the soluble solid concentration gradually increased during the postharvest ripening of red-flesh kiwifruit. In addition, the total phenolic (TPC), total flavonoid (TFC), and total proanthocyanidin (TPAC) contents gradually increased during postharvest ripening. The most abundant phenolic compounds in kiwifruit throughout postharvest ripening were catechin (CC), proanthocyanidin B1 (PB1), and proanthocyanidin B2 (PB2). Furthermore, the methanolic extracts of red-flesh kiwifruit exhibited remarkable antioxidant activities throughout postharvest ripening stages. Indeed, some phenolic compounds showed good correlations with antioxidant activities; for instance, chlorogenic acid (CHL) showed a significantly positive correlation with ferric reducing antioxidant power (FRAP), and isoquercitrin (IS) showed a significantly negative correlation with DPPH free radical scavenging ability. The findings from this study are beneficial to better understanding the quality profile of red-flesh kiwifruit “Hongshi No. 2” during postharvest ripening.
The aim of this study was to understand the status and trend in alert override research over the past two decades (1999–2018). We used the Web of Science core collection (WoSCC) database to extract all papers of alert override in clinical decision support from 1999 to 2018. A total of 150 papers were identified, most (86.67%) being articles. This study presented the key bibliometric indicators such as annual publications, top 5 authors, institutions, countries, and co-occurrence of terms from the titles and abstracts. VOSviewer was used to visualize keywords knowledge maps. The results show that alert override research has a wide variety of research themes and a multidisciplinary character. This study provides a broad view of the current status and trends in alert override research. It may help researchers, clinicians and policymakers better understand alert override research field change and direction in the future.
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