Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Sai Pavan Kumar, Veeranki"'
Autor:
Julian Gutheil, Philip Stampfer, Diether Kramer, Manuel Wechselberger, Sai Pavan Kumar Veeranki, Michael Schrempf, Peter Mrak, Martina Aubel, Franz Feichtner
Publikováno v:
dHealth 2023 ISBN: 9781643683867
Background: Frail individuals are very vulnerable to stressors, which often lead to adverse outcomes. To ensure an adequate therapy, a holistic diagnostic approach is needed which is provided in geriatric wards. It is important to identify frail indi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c619a48ed9eeb212840a4f53caae2ff1
https://doi.org/10.3233/shti230042
https://doi.org/10.3233/shti230042
Autor:
Stefanie Jauk, Diether Kramer, Sai Pavan Kumar Veeranki, Angelika Siml-Fraissler, Angelika Lenz-Waldbauer, Ewald Tax, Werner Leodolter, Markus Gugatschka
Publikováno v:
Dysphagia.
Based on a large number of pre-existing documented electronic health records (EHR), we developed a machine learning (ML) algorithm for detection of dysphagia and aspiration pneumonia. The aim of our study was to prospectively apply this algorithm in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a5f2395baea74b9bc67a13d2894e2d6e
https://doi.org/10.1007/s00455-022-10548-9
https://doi.org/10.1007/s00455-022-10548-9
Autor:
Hans, Moen, Dari, Alhuwail, Jari, Björne, Lori, Block, Sven, Celin, Eunjoo, Jeon, Karl, Kreiner, James, Mitchell, Gabriela, Ožegović, Charlene Esteban, Ronquillo, Lydia, Sequeira, Jude, Tayaben, Maxim, Topaz, Sai Pavan Kumar, Veeranki, Laura-Maria, Peltonen
Publikováno v:
Studies in health technology and informatics. 290
We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::04237c3578a4e57ff1e177c0769e310c
https://pubmed.ncbi.nlm.nih.gov/35673094
https://pubmed.ncbi.nlm.nih.gov/35673094
Autor:
Hans Moen, Dari Alhuwail, Jari Björne, Lori Block, Sven Celin, Eunjoo Jeon, Karl Kreiner, James Mitchell, Gabriela Ožegović, Charlene Esteban Ronquillo, Lydia Sequeira, Jude Tayaben, Maxim Topaz, Sai Pavan Kumar Veeranki, Laura-Maria Peltonen
We evaluate the performance of multiple text classification methods used to automate the screening of article abstracts in terms of their relevance to a topic of interest. The aim is to develop a system that can be first trained on a set of manually
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02e3ba1d54d6dbd83c2ccf7860fb0d8a
https://doi.org/10.3233/shti220155
https://doi.org/10.3233/shti220155
Autor:
Stefanie, Jauk, Sai Pavan Kumar, Veeranki, Diether, Kramer, Stefan, Högler, David, Mühlecker, Erwin, Eberhartl, Arthur, Schueler, Christian, Chvosta, Wolfgang, Strasser, Reinhold, Strasser, Werner, Leodolter
Publikováno v:
Studies in health technology and informatics. 293
Various machine learning (ML) models have been developed for the prediction of clinical outcomes, but there is missing evidence on their performance in clinical routine and external validation.Our aim was to deploy and prospectively evaluate an alrea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::ce00843fc242c4961549611d4b207177
https://pubmed.ncbi.nlm.nih.gov/35592966
https://pubmed.ncbi.nlm.nih.gov/35592966
Autor:
Stefanie Jauk, Sai Pavan Kumar Veeranki, Diether Kramer, Stefan Högler, David Mühlecker, Erwin Eberhartl, Arthur Schueler, Christian Chvosta, Wolfgang Strasser, Reinhold Strasser, Werner Leodolter
Background: Various machine learning (ML) models have been developed for the prediction of clinical outcomes, but there is missing evidence on their performance in clinical routine and external validation. Objectives: Our aim was to deploy and prospe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::124e025a7944d45a2078f0d25b09c67d
https://doi.org/10.3233/shti220353
https://doi.org/10.3233/shti220353
Autor:
Sai Pavan Kumar, Veeranki, Dieter, Hayn, Stefanie, Jauk, Franz, Quehenberger, Diether, Kramer, Werner, Leodolter, Günter, Schreier
Publikováno v:
Studies in health technology and informatics. 264
With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::bbd07671b9d8154a4f0a6016b7646d6c
https://pubmed.ncbi.nlm.nih.gov/31438234
https://pubmed.ncbi.nlm.nih.gov/31438234
Autor:
Sai Pavan Kumar, Veeranki, Diether, Kramer, Dieter, Hayn, Stefanie, Jauk, Alphons, Eggerth, Franz, Quehenberger, Werner, Leodolter, Günter, Schreier
Publikováno v:
Studies in health technology and informatics. 260
Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::64123ca9925a79ef9c66effd916ba94e
https://pubmed.ncbi.nlm.nih.gov/31118336
https://pubmed.ncbi.nlm.nih.gov/31118336
Autor:
Stefanie, Jauk, Diether, Kramer, Franz, Quehenberger, Sai Pavan Kumar, Veeranki, Dieter, Hayn, Günter, Schreier, Werner, Leodolter
Publikováno v:
Studies in health technology and informatics. 260
In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.Our aim was to evaluate the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::e2716daa418e1946d9f846909e8b6117
https://pubmed.ncbi.nlm.nih.gov/31118320
https://pubmed.ncbi.nlm.nih.gov/31118320