Explainable artificial intelligence model to predict acute critical illness from electronic health records

Autor: Bo Thiesson, Marianne Johansson Jørgensen, Mathias Vassard Olsen, Morten Skaarup Larsen, Simon Meyer Lauritsen, Mads Kristensen, Katrine Meyer Lauritsen, Jeppe Lange

Publikováno v: Lauritsen, S M, Kristensen, M, Olsen, M V, Larsen, M S, Lauritsen, K M, Jørgensen, M J, Lange, J & Thiesson, B 2020, ' Explainable artificial intelligence model to predict acute critical illness from electronic health records ', Nature Communications, vol. 11, 3852 . https://doi.org/10.1038/s41467-020-17431-x
Lauritsen, S M, Kristensen, M, Olsen, M V, Larsen, M S, Lauritsen, K M, Jørgensen, M J, Lange, J & Thiesson, B 2019, ' Explainable artificial intelligence model to predict acute critical illness from electronic health records ', arXiv .
Lauritsen, S M, Kristensen, M, Olsen, M V, Larsen, M S, Lauritsen, K M, Jørgensen, M J, Lange, J & Thiesson, B 2020, ' Explainable artificial intelligence model to predict acute critical illness from electronic health records ', Nature Communications, vol. 11, no. 1, 3852 . https://doi.org/10.1038/s41467-020-17431-x
Aarhus University
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Nature Communications

Acute critical illness is often preceded by deterioration of routinely measured clinical parameters, e.g., blood pressure and heart rate. Early clinical prediction is typically based on manually calculated screening metrics that simply weigh these pa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::888af5ed6602be114510d789d2b8cc79
http://arxiv.org/abs/1912.01266

SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network

Autor: Kieran O'Brien, Amir Fazlollahi, Lasse Riis Østergaard, Mathias Vassard Olsen, Morten Skaarup Larsen, Mads Jozwiak Pedersen, Christian Langkammer, Matilde Holm Kristensen, Markus Barth, Steffen Bollmann

Publikováno v: Bollmann, S, Kristensen, M H, Larsen, M S, Olsen, M V, Pedersen, M J, Østergaard, L R, O'Brien, K, Langkammer, C, Fazlollahi, A & Barth, M 2019, ' SHARQnet-Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network ', Medical Physics, vol. 29, no. 2, pp. 139-149 . https://doi.org/10.1016/j.zemedi.2019.01.001

Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson’s disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resona

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::003376062138bf89e8b3eafb58d2b2aa
https://vbn.aau.dk/da/publications/bcc87912-a88a-4e1d-b74c-333ca357e474

Frequency of eustachian tube openings based on long-term direct monitoring of middle ear pressure

Autor: Frederik Skou Nielsen, Morten Skaarup Larsen, Simona Padurariu, Lasse Riis ؘstergaard, Henrik Jacobsen, Joris Dirkcx, Michael Gaihede

Publikováno v: Nielsen, F S, Larsen, M S, Padurariu, S, Østergaard, L R, Jacobsen, H, Dirkcx, J & Gaihede, M 2018, ' Frequency of eustachian tube openings based on long-term direct monitoring of middle ear pressure ', Dansk Medicoteknisk Selskab landsmøde 2018, Vejle, Denmark, 09/10/2018-11/10/2018 .
Aalborg University

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::6da3caaaf00c2fa04f9e7d9c4683b7be
https://vbn.aau.dk/da/publications/cb123563-f045-4929-a361-6beb058fef65