Pritam Mukherjee, Stanford University

May issue now live

Nature Machine Intelligence is an online-only journal for research and perspectives from the fast-moving fields of artificial intelligence, machine learning and robotics. Launched in january 2019.

Latest Research

  • Article |

    A fundamental problem in network science is how to find an optimal set of key players whose activation or removal significantly impacts network functionality. The authors propose a deep reinforcement learning framework that can be trained on small networks to understand the organizing principles of complex networked systems.

    • Changjun Fan
    • , Li Zeng
    • , Yizhou Sun
    •  & Yang-Yu Liu
  • Article |

    While computerization and digitization of medicine have advanced substantially, management tools in healthcare have not yet benefited much from these developments due to the extreme complexity and variability of healthcare operations. The ability of machine learning algorithms to build strong models from a large number of weakly predictive features, and to identify key factors in complex feature sets, is tested in operational problems involving hospital datasets on workflow and patient waiting time.

    • Oleg S. Pianykh
    • , Steven Guitron
    • , Darren Parke
    • , Chengzhao Zhang
    • , Pari Pandharipande
    • , James Brink
    •  & Daniel Rosenthal
  • Article |

    Predicting overall survival for patients with confirmed non-small-cell lung cancer is an important issue in clinical practice. The authors developed and validated in four independent patient cohorts a shallow convolutional neural network that can predict the outcomes of individuals using pre-treatment CT images. The authors further show that the survival model can be used, via transfer learning, for classifying benign versus malignant nodules.

    • Pritam Mukherjee
    • , Mu Zhou
    • , Edward Lee
    • , Anne Schicht
    • , Yoganand Balagurunathan
    • , Sandy Napel
    • , Robert Gillies
    • , Simon Wong
    • , Alexander Thieme
    • , Ann Leung
    •  & Olivier Gevaert
  • Article |

    Early and accurate clinical assessment of disease severity in COVID-19 patients is essential for planning the allocation of scarce hospital resources. An explainable machine learning tool trained on blood sample data from 485 patients from Wuhan selected three biomarkers for predicting mortality of individual patients with high accuracy.

    • Li Yan
    • , Hai-Tao Zhang
    • , Jorge Goncalves
    • , Yang Xiao
    • , Maolin Wang
    • , Yuqi Guo
    • , Chuan Sun
    • , Xiuchuan Tang
    • , Liang Jing
    • , Mingyang Zhang
    • , Xiang Huang
    • , Ying Xiao
    • , Haosen Cao
    • , Yanyan Chen
    • , Tongxin Ren
    • , Fang Wang
    • , Yaru Xiao
    • , Sufang Huang
    • , Xi Tan
    • , Niannian Huang
    • , Bo Jiao
    • , Cheng Cheng
    • , Yong Zhang
    • , Ailin Luo
    • , Laurent Mombaerts
    • , Junyang Jin
    • , Zhiguo Cao
    • , Shusheng Li
    • , Hui Xu
    •  & Ye Yuan
  • Article |

    Current neural networks attempt to learn spatial and temporal information as a whole, limiting their ability to process complex video data. Pang et al. improve performance by introducing a network structure which learns to implicitly decouple complex spatial and temporal concepts.

    • Bo Pang
    • , Kaiwen Zha
    • , Hanwen Cao
    • , Jiajun Tang
    • , Minghui Yu
    •  & Cewu Lu

News & Comment

  • Comment |

    The COVID-19 pandemic poses a historical challenge to society. The profusion of data requires machine learning to improve and accelerate COVID-19 diagnosis, prognosis and treatment. However, a global and open approach is necessary to avoid pitfalls in these applications.

    • Nathan Peiffer-Smadja
    • , Redwan Maatoug
    • , François-Xavier Lescure
    • , Eric D’Ortenzio
    • , Joëlle Pineau
    •  & Jean-Rémi King
  • Comment |

    In an unprecedented effort of scientific collaboration, researchers across fields are racing to support the response to COVID-19. Making a global impact with AI tools will require scalable approaches for data, model and code sharing; adapting applications to local contexts; and cooperation across borders.

    • Miguel Luengo-Oroz
    • , Katherine Hoffmann Pham
    • , Joseph Bullock
    • , Robert Kirkpatrick
    • , Alexandra Luccioni
    • , Sasha Rubel
    • , Cedric Wachholz
    • , Moez Chakchouk
    • , Phillippa Biggs
    • , Tim Nguyen
    • , Tina Purnat
    •  & Bernardo Mariano
  • Comment |

    The attention and resources of AI researchers have been captured by COVID-19. However, successful adoption of AI models in the fight against the pandemic is facing various challenges, including moving clinical needs as the epidemic progresses and the necessity to translate models to local healthcare situations.

    • Yipeng Hu
    • , Joseph Jacob
    • , Geoffrey J. M. Parker
    • , David J. Hawkes
    • , John R. Hurst
    •  & Danail Stoyanov
  • Editorial |

    Scientists have been getting concerned about the carbon footprint of international meetings and have been asking whether travelling to conferences is the best use of their time and funds. 2020 is turning out to be the year that many organizers decide to go virtual — and this was before COVID-19.

  • News & Views |

    To deploy robot swarms in our daily lives, they need to be resilient to malfunctioning errors and protected against malicious attacks. Blockchain technology could provide an essential level of protection.

    • Andreagiovanni Reina
  • Comment |

    The Catholic Church is challenged by scientific and technological innovation but can help to integrate multiple voices in the ongoing dialogue regarding AI and machine ethics. In this context, a multidisciplinary working group brought together by the Church reflected on roboethics, explored the themes of embodiment, agency and intelligence.

    • Edoardo Sinibaldi
    • , Chris Gastmans
    • , Miguel Yáñez
    • , Richard M. Lerner
    • , László Kovács
    • , Carlo Casalone
    • , Renzo Pegoraro
    •  & Vincenzo Paglia

About the Journal

  • Nature Machine Intelligence aims to bring different fields together in the study, engineering and application of intelligent machines. We publish research on a large variety of topics in machine learning, robotics, cognitive science and a range of AI approaches. We also provide a platform for comments and reviews to discuss emerging inter-disciplinary themes as well as the significant impact that machine intelligence has on other fields in science and on society.

  • Nature Machine Intelligence publishes original research as Articles. We also publish a range of other content types including Reviews, Perspectives, Comments, Correspondences, News & Views and Feature articles.

  • Nature Machine Intelligence is run by a team of full-time editors. The Chief Editor is Liesbeth Venema who was previously a physics editor at Nature. Trenton Jerde started in March 2018, Yann Sweeney joined in July and Jacob Huth joined in November 2018, completing the team.

  • Contact information for editorial staff, submissions, the press office, institutional access and advertising at Nature Machine Intelligence