-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
86998c2
commit 08c8054
Showing
4 changed files
with
148 additions
and
146 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file was deleted.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,94 +1,99 @@ | ||
| @article{Bischoff2019a, | ||
| abstract = {This article describes tsmp, an R package that implements the matrix profile concept for time series. The tsmp package is a toolkit that allows all-pairs similarity joins, motif, discords and chains discovery, semantic segmentation, etc. Here we describe how the tsmp package may be used by showing some of the use-cases from the original articles and evaluate the algorithm speed in the R environment. This package can be downloaded at https://CRAN.R-project.org/package=tsmp.}, | ||
| archivePrefix = {arXiv}, | ||
| arxivId = {1904.12626}, | ||
| author = {Bischoff, Francisco and Rodrigues, Pedro Pereira}, | ||
| doi = {10.13140/RG.2.2.13040.30726}, | ||
| eprint = {1904.12626}, | ||
| file = {:D$\backslash$:/Cloud/Mendeley/Bischoff, Rodrigues/Unknown/Bischoff, Rodrigues - 2019 - tsmp An R Package for Time Series with Matrix Profile(2).pdf:pdf;:D$\backslash$:/Cloud/Mendeley/Bischoff, Rodrigues/Unknown/Bischoff, Rodrigues - 2019 - tsmp An R Package for Time Series with Matrix Profile.pdf:pdf}, | ||
| keywords = {R,data mining,matrix profile,time series,tsmp}, | ||
| title = {tsmp: An R Package for Time Series with Matrix Profile}, | ||
| author = {Bischoff, Francisco and Rodrigues, Pedro Pereira}, | ||
| year = 2019, | ||
| month = apr, | ||
| doi = {10.13140/rg.2.2.13040.30726}, | ||
| url = {http://arxiv.org/abs/1904.12626 https://www.researchgate.net/publication/332494860{\%5F}tsmp{\%5F}An{\%5F}R{\%5F}Package{\%5F}for{\%5F}Time{\%5F}Series{\%5F}with{\%5F}Matrix{\%5F}Profile/}, | ||
| archiveprefix = {arXiv}, | ||
| arxivid = {1904.12626}, | ||
| eprint = {1904.12626}, | ||
| keywords = {R,data mining,matrix profile,time series,tsmp}, | ||
| mendeley-tags = {R,data mining,matrix profile,time series,tsmp}, | ||
| month = {apr}, | ||
| title = {{tsmp: An R Package for Time Series with Matrix Profile}}, | ||
| url = {http://arxiv.org/abs/1904.12626 https://www.researchgate.net/publication/332494860{\_}tsmp{\_}An{\_}R{\_}Package{\_}for{\_}Time{\_}Series{\_}with{\_}Matrix{\_}Profile/}, | ||
| year = {2019} | ||
| } | ||
| @article{Lawless1994, | ||
| abstract = {OBJECTIVE To determine the predictive value of patient monitoring alarms as a warning system in a pediatric intensive care unit (ICU). DESIGN Prospective, observational study. SETTING Pediatric ICU of a university affiliated children's hospital. INTERVENTIONS During a 7-day period, ICU staff were asked to record the type and number of alarm soundings. Alarms were recorded as false, significant (resulted in change in therapy), or induced (by staff manipulations; not significant). MEASUREMENTS AND MAIN RESULTS Sixty-six percent of nursing shifts (928 patient hours of care) responded. There were 2,176 alarms soundings: 1,481 (68{\%}) false, 119 (5.5{\%}) significant, and 576 (26.5{\%}) induced. Alarm origins were: 44{\%} pulse oximeter, 1{\%} end-tidal PCO2, 31{\%} ventilator, and 24{\%} electrocardiograph (EKG). The positive predictive value of alarms were: 7{\%} pulse oximeter, 16{\%} end-tidal PCO2, 3{\%} ventilator, and 5{\%} EKG. The negative predictive value of all alarms were {\textgreater} 97{\%}. More alarms sounded during the 7:00 am to 3:00 pm shift than during the 3:00 pm to 11:00 pm or 11:00 pm to 7:00 am shifts (167 +/- 19 vs. 64 +/- 39 vs. 75 +/- 43, p {\textless} .05, respectively). When corrected for number of patients/shift, the occurrence of soundings differed only between day and night (11.4 +/- 1.5/patient/shift vs. 6.1 +/- 1.0, p {\textless} .05). CONCLUSIONS Over 94{\%} of alarm soundings in a pediatric ICU may not be clinically important. Present monitoring systems are poor predictors of untoward events.}, | ||
| author = {Lawless, S T}, | ||
| issn = {0090-3493}, | ||
| journal = {Critical care medicine}, | ||
| month = {jun}, | ||
| number = {6}, | ||
| pages = {981--5}, | ||
| pmid = {8205831}, | ||
| title = {{Crying wolf: false alarms in a pediatric intensive care unit.}}, | ||
| url = {http://www.ncbi.nlm.nih.gov/pubmed/8205831}, | ||
| volume = {22}, | ||
| year = {1994} | ||
| title = {Crying wolf: false alarms in a pediatric intensive care unit.}, | ||
| author = {Lawless, S T}, | ||
| year = 1994, | ||
| month = jun, | ||
| journal = {Critical care medicine}, | ||
| volume = 22, | ||
| number = 6, | ||
| pages = {981--5}, | ||
| issn = {0090-3493}, | ||
| url = {http://www.ncbi.nlm.nih.gov/pubmed/8205831}, | ||
| pmid = 8205831, | ||
| } | ||
| @article{Chambrin2001, | ||
| abstract = {Many alarms, as they now exist in most monitoring systems, are not usually perceived as helpful by the medical staff because of the high incidence of false alarms. This paper gives an overview of the problems related to their current design and the objectives of monitoring. The current approaches used to improve the situation are then presented from two main standpoints: organizational and behavioural on the one hand, and technical on the other.}, | ||
| author = {Chambrin, M C}, | ||
| doi = {10.1186/cc1021}, | ||
| issn = {1364-8535}, | ||
| journal = {Critical care (London, England)}, | ||
| month = {aug}, | ||
| number = {4}, | ||
| pages = {184--8}, | ||
| pmid = {11511330}, | ||
| title = {{Alarms in the intensive care unit: how can the number of false alarms be reduced?}}, | ||
| url = {http://www.ncbi.nlm.nih.gov/pubmed/11511330 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC137277}, | ||
| volume = {5}, | ||
| year = {2001} | ||
| title = {Alarms in the intensive care unit: how can the number of false alarms be reduced?}, | ||
| author = {Chambrin, M C}, | ||
| year = 2001, | ||
| month = aug, | ||
| journal = {Critical care (London, England)}, | ||
| volume = 5, | ||
| number = 4, | ||
| pages = {184--8}, | ||
| doi = {10.1186/cc1021}, | ||
| issn = {1364-8535}, | ||
| url = {http://www.ncbi.nlm.nih.gov/pubmed/11511330 http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC137277}, | ||
| pmid = 11511330, | ||
| } | ||
| @article{Parthasarathy2004, | ||
| author = {Parthasarathy, Sairam and Tobin, Martin J.}, | ||
| doi = {10.1007/s00134-003-2030-6}, | ||
| issn = {0342-4642}, | ||
| journal = {Intensive Care Medicine}, | ||
| month = {feb}, | ||
| number = {2}, | ||
| pages = {197--206}, | ||
| title = {{Sleep in the intensive care unit}}, | ||
| url = {http://link.springer.com/10.1007/s00134-003-2030-6}, | ||
| volume = {30}, | ||
| year = {2004} | ||
| title = {Sleep in the intensive care unit}, | ||
| author = {Parthasarathy, Sairam and Tobin, Martin J.}, | ||
| year = 2004, | ||
| month = feb, | ||
| journal = {Intensive Care Medicine}, | ||
| volume = 30, | ||
| number = 2, | ||
| pages = {197--206}, | ||
| doi = {10.1007/s00134-003-2030-6}, | ||
| issn = {0342-4642}, | ||
| url = {http://link.springer.com/10.1007/s00134-003-2030-6}, | ||
| } | ||
| @article{Goldberger2000, | ||
| author = {Goldberger, A and Amaral, L and Glass, L and Hausdorff, J and Ivanov, PC and Mark, R and Mietus, JE and Moody, GB and Peng, CK and Stanley, HE}, | ||
| journal = {Circulation [online]}, | ||
| number = {23}, | ||
| pages = {e215--e220}, | ||
| title = {{PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.}}, | ||
| volume = {101}, | ||
| year = {2000} | ||
| title = {PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals.}, | ||
| author = {Goldberger, A and Amaral, L and Glass, L and Hausdorff, J and Ivanov, PC and Mark, R and Mietus, JE and Moody, GB and Peng, CK and Stanley, HE}, | ||
| year = 2000, | ||
| journal = {Circulation [online]}, | ||
| volume = 101, | ||
| number = 23, | ||
| pages = {e215--e220}, | ||
| } | ||
| @inproceedings{Clifford2015, | ||
| abstract = {High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 Physio-Net/Computing in Cardiology Challenge provides a set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A collection of 750 data segments was made available for training and a set of 500 was held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge.}, | ||
| author = {Clifford, Gari D. and Silva, Ikaro and Moody, Benjamin and Li, Qiao and Kella, Danesh and Shahin, Abdullah and Kooistra, Tristan and Perry, Diane and Mark, Roger G.}, | ||
| booktitle = {Computing in Cardiology}, | ||
| doi = {10.1109/CIC.2015.7408639}, | ||
| isbn = {9781509006854}, | ||
| issn = {2325887X}, | ||
| title = {{The PhysioNet/Computing in Cardiology Challenge 2015: Reducing false arrhythmia alarms in the ICU}}, | ||
| year = {2015} | ||
| title = {The PhysioNet/Computing in Cardiology Challenge 2015: Reducing false arrhythmia alarms in the ICU}, | ||
| author = {Clifford, Gari D. and Silva, Ikaro and Moody, Benjamin and Li, Qiao and Kella, Danesh and Shahin, Abdullah and Kooistra, Tristan and Perry, Diane and Mark, Roger G.}, | ||
| year = 2015, | ||
| booktitle = {Computing in Cardiology}, | ||
| doi = {10.1109/cic.2015.7408639}, | ||
| isbn = 9781509006854, | ||
| issn = {2325887x}, | ||
| } | ||
| @article{VanBenschoten2020, | ||
| abstract = {We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional changes, e.g. due to interventions, actions of agents and other sources of non-stationarities. We show that under this assumption, the correct causal structural choices lead to faster adaptation to modified distributions because the changes are concentrated in one or just a few mechanisms when the learned knowledge is modularized appropriately. This leads to sparse expected gradients and a lower effective number of degrees of freedom needing to be relearned while adapting to the change. It motivates using the speed of adaptation to a modified distribution as a meta-learning objective. We demonstrate how this can be used to determine the cause-effect relationship between two observed variables. The distributional changes do not need to correspond to standard interventions (clamping a variable), and the learner has no direct knowledge of these interventions. We show that causal structures can be parameterized via continuous variables and learned end-to-end. We then explore how these ideas could be used to also learn an encoder that would map low-level observed variables to unobserved causal variables leading to faster adaptation out-of-distribution, learning a representation space where one can satisfy the assumptions of independent mechanisms and of small and sparse changes in these mechanisms due to actions and non-stationarities.}, | ||
| archivePrefix = {arXiv}, | ||
| arxivId = {1901.10912}, | ||
| author = {{Van Benschoten}, Andrew and Ouyang, Austin and Bischoff, Francisco and Marrs, Tyler}, | ||
| doi = {10.21105/joss.02179}, | ||
| eprint = {1901.10912}, | ||
| file = {:D$\backslash$:/Cloud/Mendeley/Van Benschoten et al/Journal of Open Source Software/Van Benschoten et al. - 2020 - MPA a novel cross-language API for time series analysis.pdf:pdf}, | ||
| issn = {2475-9066}, | ||
| journal = {Journal of Open Source Software}, | ||
| month = {may}, | ||
| number = {49}, | ||
| pages = {2179}, | ||
| title = {{MPA: a novel cross-language API for time series analysis}}, | ||
| url = {https://joss.theoj.org/papers/10.21105/joss.02179 http://arxiv.org/abs/1901.10912}, | ||
| volume = {5}, | ||
| year = {2020} | ||
| title = {MPA: a novel cross-language API for time series analysis}, | ||
| author = {{Van Benschoten}, Andrew and Ouyang, Austin and Bischoff, Francisco and Marrs, Tyler}, | ||
| year = 2020, | ||
| month = may, | ||
| journal = {Journal of Open Source Software}, | ||
| volume = 5, | ||
| number = 49, | ||
| pages = 2179, | ||
| doi = {10.21105/joss.02179}, | ||
| issn = {2475-9066}, | ||
| url = {https://joss.theoj.org/papers/10.21105/joss.02179 http://arxiv.org/abs/1901.10912}, | ||
| archiveprefix = {arXiv}, | ||
| arxivid = {1901.10912}, | ||
| eprint = {1901.10912}, | ||
| } | ||
| @misc{krystalli_2019, | ||
| author = {Krystalli, Anna}, | ||
| year = 2019, | ||
| journal = {R for Reproducible Research}, | ||
| url = {https://annakrystalli.me/rrresearch/}, | ||
| } | ||
| @online{dcc_2013, | ||
| title = {Checklist for a Data Management Plan. v.4.0}, | ||
| author = {Edinburgh: Digital Curation Centre}, | ||
| year = 2013, | ||
| url = {http://www.dcc.ac.uk/resources/data-management-plans}, | ||
| } |