| Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI B Vasey, M Nagendran, B Campbell, DA Clifton, GS Collins, S Denaxas, ... Nature medicine 28 (5), 924-933, 2022 | 230 | 2022 |
| Software for calculating blood lactate endurance markers J Newell, D Higgins, N Madden, J Cruickshank, J Einbeck, K McMillan, ... Journal of sports sciences 25 (12), 1403-1409, 2007 | 193 | 2007 |
| From Bit To Bedside: A Practical Framework For Artificial Intelligence Product Development In Healthcare D Higgins, VI Madai Advanced Intelligent Systems 2 (10), 2020 | 79 | 2020 |
| Burst-Dependent Bidirectional Plasticity in the Cerebellum Is Driven by Presynaptic NMDA Receptors G Bouvier, D Higgins, M Spolidoro, D Carrel, B Mathieu, C Léna, ... Cell Reports 15 (1), 104-116, 2016 | 56 | 2016 |
| Memory maintenance in synapses with calcium-based plasticity in the presence of background activity D Higgins, M Graupner, N Brunel PLoS computational biology 10 (10), e1003834, 2014 | 34 | 2014 |
| A machine-learning–based algorithm improves prediction of preeclampsia-associated adverse outcomes LJ Schmidt, O Rieger, M Neznansky, M Hackelöer, LA Dröge, W Henrich, ... American Journal of Obstetrics and Gynecology 227 (1), 77. e1-77. e30, 2022 | 33 | 2022 |
| OnRAMP for Regulating Artificial Intelligence in Medical Products DC Higgins Advanced Intelligent Systems, 2100042, 2021 | 12* | 2021 |
| Validation of artificial intelligence containing products across the regulated healthcare industries DC Higgins, C Johner Therapeutic Innovation & Regulatory Science 57 (4), 797-809, 2023 | 6 | 2023 |
| Artificial Intelligence in Healthcare: Lost In Translation? VI Madai, DC Higgins arXiv preprint arXiv:2107.13454, 2021 | 5 | 2021 |
| Publisher Correction: Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI B Vasey, M Nagendran, B Campbell, DA Clifton, GS Collins, S Denaxas, ... Nature Medicine 28 (10), 2218-2218, 2022 | 4 | 2022 |
| 26th Annual Computational Neuroscience Meeting (CNS* 2017): Part 3 AJH Newton, AH Seidenstein, RA McDougal, A Pérez-Cervera, G Huguet, ... BMC neuroscience 18 (1), 60, 2017 | 3 | 2017 |
| Introducing Matchgraft. AI: Machine Learning Based Risk-Prediction-Tool for Severe Complications of Allogenic Hematopoietic Stem Cell Transplantation (HSCT) like Acute Graft … M Reschke, JP Gross, O Penack, G Sürücü, A Lawitschka, C Summers, ... Transplantation and Cellular Therapy, Official Publication of the American …, 2024 | | 2024 |
| Establishing Standard of Care in Predicting Serious Complications for Patients Planned to Undergo Allogenic Hematopeotic Stem Cell Transplantation M Reschke, JP Gross, J Hegner, J Seiler, G Sürücü, O Penack, ... Blood 142, 7036, 2023 | | 2023 |
| Artificial Intelligence (AI) Based, Machine Learning (ML) Predicting the Individual Absolute Risk of Acute Graft Versus Host Disease (aGvHD) in a Retrospective International Cohort M Reschke, JP Gross, O Penack, G Sürücü, C Summers, J Seiler, ... Blood 142, 2196, 2023 | | 2023 |
| RESULTS OF FEASIBILITY STUDY WITH SELF-LEARNING, ARTIFICIAL INTELLIGENCE BASED, MACHINE LEARNING TOOL PREDICTING THE ABSOLUTE INDIVIDUAL RISK OF ACUTE GRAFT-VERSUS-HOST-DISEASE … M Reschke, JP Gross, J Seiler, D Weschke, DC Higgins, L Oevermann BONE MARROW TRANSPLANTATION 58 (SUPP 1), 313-314, 2023 | | 2023 |
| Entwicklung eines Machine-Learning Models zur Prädiktion adverser Events in Patientinnen mit hohem Risiko für Präeklampsie L Schmidt, O Rieger, M Neznansky, M Hackelöer, L Dröge, W Henrich, ... Zeitschrift für Geburtshilfe und Neonatologie 225 (S 01), P 120, 2021 | | 2021 |
| P-040. Development of a machine-learning model to predict adverse outcomes in patients at risk for preeclampsia LJ Schmidt, O Rieger, M Neznansky, M Hackelöer, LA Dröge, W Henrich, ... Pregnancy Hypertension 25, e41-e42, 2021 | | 2021 |
| Failure to learn during roving, analysing the unsupervised bias hypothesis. D Higgins, M Herzog bioRxiv, 383398, 2018 | | 2018 |
| Interactions of reward predictions and stimulus representations in synaptic reward learning. DC Higgins, H Sprekeler Bernstein Conference 2015, 2015 | | 2015 |
| A Theoretical and Numerical Study of Certain Dynamical Models of Synaptic Plasticity DC Higgins Ecole Normale Supérieure de Paris-ENS Paris, 2014 | | 2014 |
Nicolas BrunelDuke UniversityVerified email at duke.edu
