Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
Nenad Tomašev, Natalie Harris, Sebastien Baur, Anne Mottram, Xavier Glorot, Jack W. Rae, Michał Zieliński, Harry Askham, André Saraiva, Valerio Magliulo, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, Alistair Connell, Cían Hughes, Alan Karthikesalingam, Julien Cornebise, Hugh Montgomery, Geraint Rees, Chris Laing, Clifton R. Baker, Thomas F. Osborne, Ruth Reeves, Demis Hassabis, Dominic King, Mustafa Suleyman, Trevor Back, Christopher Nielson, Martin Seneviratne, Joseph R. Ledsam, Shakir Mohamed
Topics & Concepts
WorkflowComputer scienceCodebaseGeneralizability theoryProtocol (science)Machine learningArtificial intelligenceDeep learningPatient safetyElectronic health recordHealth recordsClinical decision support systemHealth careData scienceData miningMedicineDecision support systemSoftwareDatabaseProgramming languageEconomicsPathologyMathematicsStatisticsAlternative medicineEconomic growthMachine Learning in HealthcareExplainable Artificial Intelligence (XAI)Sepsis Diagnosis and Treatment