Extracting novel antimicrobial emergence events from scientific literature and medical reports

Mendelsohn, Emma; Ross, Noam; White, Allison M.; Whiting, Karissa; Basaraba, Cale; Madubuonwu, Brooke Watson; Johnson, Erica; Dualeh, Mushtaq; Matson, Zach; Dattaray, Sonia; Ezeokoli, Nchedochukwu; Lieberman, Melvin; Kotcher, Jacob; Maher, Samantha; Zambrana‐Torrelio, Carlos; Daszak, Peter

doi:10.12688/f1000research.26870.1

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…4 Imipenem is a carbapenem antibacterial drug with a good therapeutic effect on K. pneumoniae, however, the resistance of K. pneumoniae to imipern has increased year by year, resulting the great difficulties to clinical experience. 5,6 This study is proposed to establish a prediction model of K. pneumoniae to imipenem drug sensitivity by LASSO, LR, SVM, and NN machine learning algorithms based on matrix-assisted laser desorption/ionization time-of-flight mass (MALDI-TOF MS) spectrometry, and compare the diagnostic efficacy of different algorithms to explore the potential clinically assisted decision support methods.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model of imipenem‐resistant Klebsiella pneumoniae based on MALDI‐TOF‐MS platform: An observational study

Zeng

Wang

et al. 2023

Health Science Reports

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Background and AimMachine learning is an important branch and supporting technology of artificial intelligence, we established four machine learning model for the drug sensitivity of Klebsiella pneumoniae to imipenem based on matrix‐assisted laser desorption ionization time‐of‐flight mass spectrometry (MALDI‐TOF‐MS) and compared their diagnostic effect.MethodsThe data of MALDI‐TOF‐MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem‐sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS‐DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3‐fold Cross‐validation respectively, the accuracy of the prediction model was verified by test set confusion matrix.ResultsThe R²Y and Q² of OPLS‐DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively.ConclusionThe LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.

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Section: Introductionmentioning

confidence: 99%

Machine learning model of imipenem‐resistant Klebsiella pneumoniae based on MALDI‐TOF‐MS platform: An observational study

Zeng

Wang

et al. 2023

Health Science Reports

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Past and future uses of text mining in ecology and evolution

et al. 2022

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Ecology and evolutionary biology, like other scientific fields, are experiencing an exponential growth of academic manuscripts. As domain knowledge accumulates, scientists will need new computational approaches for identifying relevant literature to read and include in formal literature reviews and meta-analyses. Importantly, these approaches can also facilitate automated, large-scale data synthesis tasks and build structured databases from the information in the texts of primary journal articles, books, grey literature, and websites. The increasing availability of digital text, computational resources, and machine-learning based language models have led to a revolution in text analysis and natural language processing (NLP) in recent years. NLP has been widely adopted across the biomedical sciences but is rarely used in ecology and evolutionary biology. Applying computational tools from text mining and NLP will increase the efficiency of data synthesis, improve the reproducibility of literature reviews, formalize analyses of research biases and knowledge gaps, and promote data-driven discovery of patterns across ecology and evolutionary biology. Here we present recent use cases from ecology and evolution, and discuss future applications, limitations and ethical issues.

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Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study

et al. 2023

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Epidemic Intelligence (EI) encompasses all activities related to early identification, verification, analysis, assessment, and investigation of health threats. It integrates an indicator-based (IBS) component using systematically collected surveillance data, and an event-based component (EBS), using non-official, non-verified, non-structured data from multiple sources. We described current EI practices in Europe by conducting a survey of national Public Health (PH) and Animal Health (AH) agencies. We included generic questions on the structure, mandate and scope of the institute, on the existence and coordination of EI activities, followed by a section where respondents provided a description of EI activities for three diseases out of seven disease models. Out of 81 gatekeeper agencies from 41 countries contacted, 34 agencies (42%) from 26 (63%) different countries responded, out of which, 32 conducted EI activities. Less than half (15/32; 47%) had teams dedicated to EI activities and 56% (18/34) had Standard Operating Procedures (SOPs) in place. On a national level, a combination of IBS and EBS was the most common data source. Most respondents monitored the epidemiological situation in bordering countries, the rest of Europe and the world. EI systems were heterogeneous across countries and diseases. National IBS activities strongly relied on mandatory laboratory-based surveillance systems. The collection, analysis and interpretation of IBS information was performed manually for most disease models. Depending on the disease, some respondents did not have any EBS activity. Most respondents conducted signal assessment manually through expert review. Cross-sectoral collaboration was heterogeneous. More than half of the responding institutes collaborated on various levels (data sharing, communication, etc.) with neighbouring countries and/or international structures, across most disease models. Our findings emphasise a notable engagement in EI activities across PH and AH institutes of Europe, but opportunities exist for better integration, standardisation, and automatization of these efforts. A strong reliance on traditional IBS and laboratory-based surveillance systems, emphasises the key role of in-country laboratories networks. EI activities may benefit particularly from investments in cross-border collaboration, the development of methods that can automatise signal assessment in both IBS and EBS data, as well as further investments in the collection of EBS data beyond scientific literature and mainstream media.

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Extracting novel antimicrobial emergence events from scientific literature and medical reports

Cited by 3 publications

References 12 publications

Machine learning model of imipenem‐resistant Klebsiella pneumoniae based on MALDI‐TOF‐MS platform: An observational study

Machine learning model of imipenem‐resistant Klebsiella pneumoniae based on MALDI‐TOF‐MS platform: An observational study

Past and future uses of text mining in ecology and evolution

Epidemic intelligence activities among national public and animal health agencies: a European cross-sectional study

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