Assessing patient risk of central line-associated bacteremia via machine learning

Beeler, Cole; Dbeibo, Lana; Kelley, Kristen; Thatcher, Levi; Webb, Douglas; Bah, Amadou; Monahan, Patrick O.; Fowler, Nicole R.; Nicol, Spencer; Judy-Malcolm, Alisa; Azar, José

doi:10.1016/j.ajic.2018.02.021

Cited by 39 publications

(47 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More and more clinical laboratory tests are automated and the complexity of data generated can be more complex. Strategies to implement AI in health-care institutions still need to be developed (Beeler et al, 2018). The first aim is to setup a state-of-the-art data management system.…”

Section: On the Integration Of Ai In Health-care Institutionsmentioning

confidence: 99%

Use of artificial intelligence in infectious diseases

Agrebi

Larbi

2020

Artificial Intelligence in Precision Health

140

View full text Add to dashboard Cite

Section: On the Integration Of Ai In Health-care Institutionsmentioning

confidence: 99%

Use of artificial intelligence in infectious diseases

Agrebi

Larbi

2020

Artificial Intelligence in Precision Health

140

View full text Add to dashboard Cite

“…In total, 21 studies were retrospective 33,35,70-88 and six were prospective [89][90][91][92][93][94] . There were 21 single-center studies 33,70-75, [77][78][79][80][81][82][83][86][87][88][90][91][92]94 and six multi-center studies 35,76,84,85,89,93 . Seven studies were time series 71,78,82,[84][85][86]92 , 18 studies were case series 33,35,70, [72][73][74][75][76]80,81,83,[87][88][89][90][91]93,94 , one was a case-control 77 and one was a matched-controlled study …”

Section: Study Characteristicsmentioning

confidence: 99%

“…Four studies had a sample size below 1000 70, 72,73,79 , three had a sample size between 1001-10,000 33, 71,87 and 12 had a sample size larger than 10,000 35,74,77-78,80-82,84-87,88 . Eight studies had samples even larger than 50,000 35,74,77,78,82,84,85,88 . Large samples were achieved by less restrictive inclusion criteria where all patients admitted to specific ward(s) or hospital(s) over a given time were defined.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…The machine learning algorithms evaluated in the studies were developed to predict a range of diseases. These included sepsis 33,35,72,78,81,85,93,94 , acute kidney injury 70, [78][79][80]82,84,91 , surgical site infections 33,73,76,87,92 , central line-associated bloodstream infections 77,86 , Clostridium difficile 83,88 , pulmonary aspergillosis 89 , bacteremia 90 , fibrosis 71 , urine tract infection 33, 74 and infections in general 75 .…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Machine learning models were developed and validated in 26 studies and subsequently tested in an independent dataset in four studies 35,72,75,77 .…”

Section: Study Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Medic

Atallah

et al. 2019

F1000Res

View full text Add to dashboard Cite

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

show abstract

The Machine‐Learning Approach

Srivastava¹,

Verma²,

Musheer³

2020

Machine Learning for iOS Developers

View full text Add to dashboard Cite

Motivation: With an overwhelming amount of textual information in molecular biology and biomedicine, there is a need for effective and efficient literature mining and knowledge discovery that can help biologists to gather and make use of the knowledge encoded in text documents. In order to make organized and structured information available, automatically recognizing biomedical entity names becomes critical and is important for information retrieval, information extraction and automated knowledge acquisition. Results: In this paper, we present a named entity recognition system in the biomedical domain, called PowerBioNE. In order to deal with the special phenomena of naming conventions in the biomedical domain, we propose various evidential features: (1) word formation pattern; (2) morphological pattern, such as prefix and suffix; (3) part-of-speech; (4) head noun trigger; (5) special verb trigger and (6) name alias feature. All the features are integrated effectively and efficiently through a hidden Markov model (HMM) and a HMM-based named entity recognizer. In addition, a k -Nearest Neighbor (k -NN) algorithm is proposed to resolve the data sparseness problem in our system. Finally, we present a pattern-based post-processing to automatically extract rules from the training data to deal with the cascaded entity name phenomenon. From our best knowledge, PowerBioNE is the first system which deals with the cascaded entity name phenomenon. Evaluation shows that our system achieves the F -measure of 66.6 and 62.2 on the 23 classes of GENIA V3.0 and V1.1, respectively. In particular, our system achieves the F -measure of 75.8 on the 'protein' class of GENIA V3.0. For comparison, our system outperforms the best published result by 7.8 on GENIA V1.1, without help of any dictionaries. It also shows that our HMM and the k -NN algorithm outperform other models, such as back-off HMM, linear interpolated HMM, support vector machines, C4.5, C4.5 rules and RIPPER, by effectively capturing the local context dependency and resolving the data sparseness problem. Moreover, evaluation on GENIA V3.0 shows that the post-processing for the * To whom correspondence should be addressed.cascaded entity name phenomenon improves the F -measure by 3.9. Finally, error analysis shows that about half of the errors are caused by the strict annotation scheme and the annotation inconsistency in the GENIA corpus. This suggests that our system achieves an acceptable F -measure of 83.6 on the 23 classes of GENIA V3.0 and in particular 86.2 on the 'protein' class, without help of any dictionaries. We think that a F -measure of 90 on the 23 classes of GENIA V3.0 and in particular 92 on the 'protein' class, can be achieved through refining of the annotation scheme in the GENIA corpus, such as flexible annotation scheme and annotation consistency, and inclusion of a reasonable biomedical dictionary.

show abstract

Assessing patient risk of central line-associated bacteremia via machine learning

Cited by 39 publications

References 17 publications

Use of artificial intelligence in infectious diseases

Use of artificial intelligence in infectious diseases

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

The Machine‐Learning Approach

Contact Info

Product

Resources

About