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Discriminative Prediction of Adverse Events for Optimized Therapies Following Traumatic Brain Injury
Umeå universitet, Samhällsvetenskapliga fakulteten, Handelshögskolan vid Umeå universitet, Statistik.ORCID-id: 0000-0003-1654-9148
Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
2019 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Traumatic brain injury (TBI) causes temporary or perma- nent alteration in brain functions. At intensive care units, TBI patients are usually multimodally monitored, thus rendering large volumes of data on many physiological variables. For the physician, these data are difficult to interpret due to their complexity, speed and volume. Thus, computa- tional aids are recommended, e.g., for predicting patient’s clinical status in near future. In this article, we describe a probabilistic model that can be used for aiding physician’s decision making process in TBI patient care in real time. Our model tries to capture time varying patterns of patient’s clinical information. The model is built by using a discrimi- native model learning framework so that it can predict adverse clinical events with a higher level of accuracy. That is, our model is built so that prediction of certain desired events are given more attention than that of the other less important ones. This can be achieved by estimating model parameters in such a way, for e.g. using a cost function, when a suitable model structure has been selected, that again can be done dis- criminatively. However, such estimation procedures have no closed form solutions, so numerical optimization methods are used.

Ort, förlag, år, upplaga, sidor
Umeå, 2019. artikel-id 3
Nyckelord [en]
Dependence, Accuracy, Clinical, Real time
Nationell ämneskategori
Sannolikhetsteori och statistik
Forskningsämne
statistik
Identifikatorer
URN: urn:nbn:se:umu:diva-160522OAI: oai:DiVA.org:umu-160522DiVA, id: diva2:1327497
Konferens
31st Swedish AI Society Workshop 2019, June 18–19, 2019, Umeå, Sweden
Tillgänglig från: 2019-06-19 Skapad: 2019-06-19 Senast uppdaterad: 2019-06-20Bibliografiskt granskad

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Wijayatunga, PriyanthaSundström, Nina

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Sannolikhetsteori och statistik

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