Peer Reviewed Publications

Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units

  • Ryan M. McAdams, Ravneet Kaur, Yao Sun, Harlieen Bindra, Su Jin Cho, and Harpreet Singh, a systematic review.
  • J Perinatol (2022). VIEW PUBLICATION


Designing a bed-side system for predicting length of stay in a neonatal intensive care unit

  • Singh H, Cho S, Gupta S, Kaur R, Sunidhi, Saluja S, Pandey AK, Bennett M, Lee H, Das R, Palma J, McAdams R, Kaur A, Yadav G, Sun Y
  • Scientific Report (2021).Sci Rep 11, 3342 (2021). VIEW PUBLICATION


Daily apneic events in premature infants treated with caffeine

  • Harpreet Singh, Ryan M. McAdams, Ravneet Kaur, Satish Saluja, and Yao Sun.
  • Pending Publication


iNICU – Integrated Neonatal Care Unit: Capturing Neonatal Journey in an Intelligent Data Way

  • Singh H, Yadav G, Mallaiah R, Joshi P, Joshi V, Kaur R, Bansal S, Brahmachari S
  • J Med Syst (2017)41:132 – Impact Factor 3.058 (2019)
  • Concept of reduced and improved clinical care time, reduction of chances of human error, and ability to ensure earlier intervention when necessary presented VIEW PUBLICATION


Neo-Bedside Monitoring Device for Integrated Neonatal Intensive Care Unit (iNICU)

  • Singh H, Kaur R, Gangadharan A, Pandey A, Manur A, Sun Y, Salluja S, Gupta S, Palma J, Kumar P
  • IEEE Access (2018) doi 10.1109/ACCESS.2018.2886879 – Impact Factor 4.64 (2019)
  • Value of predictive analytics, reduction of human error and ensuring of consistency proven based on dataset of 92 newborns (UCSF Benioff Children’s Hospital and Sir Ganga Ram Hospital) VIEW PUBLICATION


iCHRCloud: Web & Mobile based Child Health Imprints for Smart Healthcare

  • Singh H, Mallaiah R, Yadav G, Verma N, Sawhney A, Brahmachari S
  • J Med Syst (2018)42:14 – Impact Factor 3.058 (2019)
  • Multiple clinical insights obtained from analysis of data from 16,490 babies with clear potential articulated for use in the arena of personalised medicine VIEW PUBLICATION


Development of data dictionary for neonatal intensive care unit: advancement towards a better critical care unit

  • Singh H, Kaur R, Saluja S, Cho S, Kaur A, Pandey A, Gupta S, Das R, Kumar P, Palma J, Yadav G, Sun Y
  • JAMIA Open (2019)1-10 – Impact Factor 4.112 (2019)
  • The value of standardization and a data dictionary, including clinical insight generation and improved clinical care pathways shown through a prospective analysis of 5 dimensions of quality of data from 2 clinical sites VIEW PUBLICATION


Machine Learning-Based Automatic Classification of Video Recorded Neonatal Manipulations and Associated Physiological Parameters: A Feasibility Study

  • Singh H, Kusuda S, McAdams R, Gupta S, Kalra J, Kaur R, Das R, Anand S, Pandey A, Cho S, Saluja S, Boutilier J, Saria S, Palma J, Kaur A, Yadav G, Sun Y
  • Children (2021) – Impact Factor 2.078 (2019)
  • A retrospective observational study of 10 neonates demonstrating a machine learning model able to classify common procedures performed on neonates VIEW PUBLICATION


Development and Validation of High Definition Phenotype (HDP) based mortality prediction in critical care units

  • Sun Y, Kaur R, Gupta S, Paul R, Das R, Cho S, Anand S, Boutilier J, Saria S, Palma J, Saluja S, McAdams R, Kaur A, Yadav G, Singh H
  • JAMIA Open – Impact Factor 4.112 (2019)
  • Promising results showing the potential for early detection of neonatal morbidity and mortality based on data analyzed from 1546 neonates across 8 level III NICUs VIEW PUBLICATION