Change search
Refine search result
12 51 - 55 of 55
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 51.
    Nelson, David W.
    et al.
    Karolinska Institute, Sweden.
    Rudehill, Anders
    Karolinska Institute, Sweden.
    MacCallum, Robert M.
    Imperial College London, UK.
    Holst, Anders
    RISE, Swedish ICT, SICS.
    Wanecek, Michael
    Karolinska Institute, Sweden.
    Weitzberg, Eddie
    Karolinska Institute, Sweden.
    Bellander, Bo Michael
    Karolinska Institute, Sweden.
    Multivariate outcome prediction in traumatic brain injury with focus on laboratory values2012In: Journal of Neurotrauma, ISSN 0897-7151, E-ISSN 1557-9042, Vol. 29, no 17, p. 2613-2624Article in journal (Refereed)
    Abstract [en]

    Traumatic brain injury (TBI) is a major cause of morbidity and mortality. Identifying factors relevant to outcome can provide a better understanding of TBI pathophysiology, in addition to aiding prognostication. Many common laboratory variables have been related to outcome but may not be independent predictors in a multivariate setting. In this study, 757 patients were identified in the Karolinska TBI database who had retrievable early laboratory variables. These were analyzed towards a dichotomized Glasgow Outcome Scale (GOS) with logistic regression and relevance vector machines, a non-linear machine learning method, univariately and controlled for the known important predictors in TBI outcome: age, Glasgow Coma Score (GCS), pupil response, and computed tomography (CT) score. Accuracy was assessed with Nagelkerke's pseudo R2. Of the 18 investigated laboratory variables, 15 were found significant (p<0.05) towards outcome in univariate analyses. In contrast, when adjusting for other predictors, few remained significant. Creatinine was found an independent predictor of TBI outcome. Glucose, albumin, and osmolarity levels were also identified as predictors, depending on analysis method. A worse outcome related to increasing osmolarity may warrant further study. Importantly, hemoglobin was not found significant when adjusted for post-resuscitation GCS as opposed to an admission GCS, and timing of GCS can thus have a major impact on conclusions. In total, laboratory variables added an additional 1.3-4.4% to pseudo R2.

  • 52. Nelson, David W.
    et al.
    Thornquist, Björn
    MacCallum, Robert M.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Nyström, Harriet
    Rudehill, Anders
    Wanecek, Michael
    Bellander, Bo-Michael
    Analysis of cerebral microdialysis in patients with traumatic brain injury; relations to intracranial pressure, cerebral perfusion pressure and catheter placement.2011In: BMC Medicine, Vol. 9Article in journal (Refereed)
  • 53.
    Olsson, Tomas
    et al.
    RISE, Swedish ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    A Probabilistic Approach to Aggregating Anomalies for Unsupervised Anomaly Detection with Industrial Applications2015In: Proceedings of the 28th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2015), 2015, 7, p. 434-439Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel, unsupervised approach to detecting anomalies at the collective level. The method probabilistically aggregates the contribution of the individual anomalies in order to detect significantly anomalous groups of cases. The approach is unsupervised in that as only input, it uses a list of cases ranked according to its individual anomaly score. Thus, any anomaly detection algorithm can be used for scoring individual anomalies, both supervised and unsupervised approaches. The applicability of the proposed approach is shown by applying it to an artificial data set and to two industrial data sets — detecting anomalously moving cranes (model-based detection) and anomalous fuel consumption (neighbour-based detection).

  • 54.
    Sahlgren, Magnus
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Holst, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Kanerva, Pentti
    Permutations as a means to encode order in word space2008Conference paper (Refereed)
    Abstract [en]

    We show that sequence information can be encoded into high-dimensional fixed-width vectors using permutations of coordinates. Computational models of language often represent words with high-dimensional semantic vectors compiled from word-use statistics. A word's semantic vector usually encodes the contexts in which the word appears in a large body of text but ignores word order. However, word order often signals a word's grammatical role in a sentence and thus tells of the word's meaning. Jones and Mewhort (2007) show that word order can be included in the semantic vectors using holographic reduced representation and convolution. We show here that the order information can be captured also by permuting of vector coordinates, thus providing a general and computationally light alternative to convolution.

    Download full text (pdf)
    fulltext
  • 55.
    Stöggl, Thomas Leonhard
    et al.
    University of Salzburg, Austria; Mid Sweden University, Sweden.
    Holst, Anders
    RISE, Swedish ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Jonasson, Arndt
    RISE, Swedish ICT, SICS.
    Andersson, Erik Petrus
    Mid Sweden University, Sweden.
    Wunsch, Tobias
    University of Salzburg, Austria.
    Norström, Christer
    RISE, Swedish ICT, SICS.
    Holmberg, Hans Christer
    Mid Sweden University, Sweden; Swedish Olympic Committee, Sweden.
    Automatic classification of the Sub-Techniques (Gears) used in cross-country ski skating employing a mobile phone2014In: Sensors, E-ISSN 1424-8220, Vol. 14, no 11, p. 20589-20601Article in journal (Refereed)
    Abstract [en]

    The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

12 51 - 55 of 55
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf