Now showing items 1-5 of 5

    • Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation 

      Sarlabous, Leonardo; Aquino‑Esperanza, José; Magrans, Rudys; De Haro, Candelaria; López-Aguilar, Josefina; Subirà Cuyàs, Carles; Batlle, Montserrat; Rué, Montserrat; Gomà Fernández, Gemma; Ochagavia, Ana; Fernández Fernández, Rafael; Blanch, Lluís (Scientific Reports, 2020)
      Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), ...
    • Kinematic analysis of human gait in healthy young adults using IMU sensors: exploring relevant machine learning features for clinical applications 

      Marimon Serra, Xavier; Mengual, Itziar; López-de-Celis, Carlos; Portela Otaño, Alejandro; Rodríguez-Sanz, Jacobo; Herráez Cabezas, Iria Andrea; Albert, Pérez-Bellmunt (Bioengineering, 2024)
      Background: Gait is the manner or style of walking, involving motor control and coordination to adapt to the surrounding environment. Knowing the kinesthetic markers of normal gait is essential for the ...
    • Machine learning risk prediction of mortality for patients undergoing surgery with perioperative SARS-CoV-2: the COVIDSurg mortality score 

      RUBIO-PALAU, JOSEP; COVIDSurg Collaborative (British Journal of Surgery, 2021)
      Since the beginning of the COVID-19 pandemic tens of millions of operations have been cancelled1 as a result of excessive postoperative pulmonary complications (51.2 per cent) and mortality rates (23.8 ...
    • Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms 

      Mora, Toni; Roche, David; Rodríguez-Sánchez, Beatriz (Diabetes Research and Clinical Practice, 2023)
      Aims: Using machine learning algorithms and administrative data, we aimed to predict the risk of being diagnosed with several diabetes-related complications after one-, two- and three-year post-diabetes ...
    • Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts 

      Haggenmüller, Sarah; Maron, Roman C.; Hekler, Achim; Utikal, Jochen S.; Barata, Catarina; Barnhill, Raymond L.; Beltraminelli, Helmut; Berking, Carola; Betz-Stablein, Brigid; Blum, Andreas; Braun, Stephan A.; Carr, Richard; Combalia, Marc; Fernandez Figueras, Maria-Teresa; Ferrara, Gerardo; Fraitag, Sylvie; French, Lars E.; Gellrich, Frank F.; Ghoreschi, Kamran; Goebeler, Matthias; Guitera, Pascale; Haenssle, Holger A.; Haferkamp, Sebastian; Heinzerling, Lucie; Heppt, Markus V.; Hilke, Franz J.; Hobelsberger, Sarah; Krahl, Dieter; Kutzner, Heinz; Lallas, Aimilios; Liopyris, Konstantinos; Llamas-Velasco, Mar; Malvehy, Josep; Meier, Friedegund; Müller, Cornelia S.L.; Navarini, Alexander A.; Navarrete-Dechent, Cristián; Perasole, Antonio; Poch, Gabriela; Podlipnik, Sebastian; Requena, Luis; Rotemberg, Veronica M.; Saggini, Andrea; Sangueza, Omar P.; Santonja, Carlos; Schadendorf, Dirk; Schilling, Bastian; Schlaak, Max; Schlager, Justin G.; Sergon, Mildred; Sondermann, Wiebke; Soyer, H. Peter; Starz, Hans; Stolz, Wilhelm; Vale, Esmeralda; Weyers, Wolfgang; Zink, Alexander; Krieghoff-Henning, Eva I.; Kather, Jakob N.; Von Kalle, Christof; Lipka, Daniel B.; Fröhling, Stefan; Hauschild, Axel; Kittler, Harald; Brinker, Titus J. (European Journal of Cancer, 2021-10)
      Background: Multiple studies have compared the performance of artificial intelligence (AI)–based models for automated skin cancer classification to human experts, thus setting the cornerstone for a ...