Group Mornin for Healthcare AI
Mengling 'Mornin' Feng (PhD)
Awards
- Singapore Data Science Consortium Dissertation Research Fellowship (PhD student) 2021
- Second runner-up Physionet Challenge 2020
- Finalist for MedTech Innovator Asia Pacific 2020
- Graduate Student Research Award (my PhD student) 2020
- Gold and Silver medals for Kaggle Medical Imaging AI competitions 2019
- 1st Runner-up ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Competition, 2018
- Top 5 (Team “Eagle Eye”) in The Digital Mammography DREAM Challenge 2017
- Merit Award of MINDEF Data Challenge and Hackathon 2015
- McKinsey Insight Program, 2015
- MIT Teaching & Learning Laboratory Kaufman Teaching Certificate, 2015
- Second runner-up of Best Student Paper Award, KDD Working Group, AMIA, 2014
Ongoing Research Grants
- Co-Principal Investigator, “JARVIS: Transforming Chronic Care for Diabetes, Hypertension and HyperLipidemia (DHL) with AI”, AI Singapore, S$25 Mil 2021 ~ 2026
- Principal Investigator, “Surgical Risk Stratification and Outcomes Prediction”, AI Singapore 100E program (sponsored by Singapore General Hospital), S$350,000 2020~2022
- Co-Principal Investigator, “Automating the detection of frailty”, NUHS Seed Grant, S$95,000 2020~2022
- Principal Investigator, “An AI assistant to radiologists that reads mammograms to automatically detect breast cancers and generate diagnostic reports: a technology combines the learning of images and free text”, Ministry of Health, Health Service Research Grant, S$1.04 Mil 2018~2022
- Co-Principal Investigator, “An Explainable AI System for Community Care”, AI.SG Healthcare Grand Challenge, S$5 Mil 2019-2021
- Co-Investigator “Development of a novel imaging-based machine-learning algorithm for the screening of osteoporosis using dental radiographs”, iHealthTech, $100K 2019-2021
- Principal Investigator, “Al Assisted Breast Cancer Diagnosis: Prototype Develop and Clinical Validation”, Singapore-MIT Alliance Research and Technology, S$244K 2019-2021
- Co-Investigator, “Automatic X-ray fracture detection using deep machine learningfactors impacting model performance”, NUHS Seed Grant, S$172K 2019-2021
- Principal Investigator, “Artificial Intelligence Models to Assist Breast Cancer Diagnosis with Mammogram Image Data”, NUHS, S$167K 2018~2020
- Principal Investigator, NUS-USydn Joint Research and Joint Seminar Grant, S$40K 2020~2021
- Principal Investigator, JSPS-NUS Joint Research and Joint Seminar Grant, S$38K 2018~2019
- Co-Investigator, Clinical Scientist Award, NUHS, $3 Mil 2017~2022
- Co-Investigator, National Medical Research Council, Center Grant in collaboration with Khoo Teck Puat Hospital, S$1.5 Mil 2017~2022
Recent Professional Service
- International Advisory Board, “Lancet Digital Health”
- Editorial Board member, “Applied Clinical Information Journal”
- Reviewer, Radiology: Artificial Intelligence
- Reviewer, Signal, Image and Video Processing (SIVP)
- Reviewer, Nature Scientific Data
- Editorial board member, “Healthcare Data Science” Journal
- Chapter Chair, OHDSI, Singapore
- Program Committee, Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
- Reviewer, Journal of Medical Internet Research
- Reviewer, Informatics (Journal),
Research Interests
- Artificial Intelligence Solutions for Healthcare Challenges
- Casual Inference for Evidence-based Medicine
- Deep learning models for medical image analysis
- Generative models, such as Generative Adversarial Network (GAN), for Medical Time Series Analysis
- Reinforcement Learning and Recommendation System for Treatment Optimization
- Federated Learning for Cross-Institute Research
Publications
Selected Publications
- Ng D, Lan X, Yao MM, Chan WP, Mengling Feng. Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets. Quantitative Imaging in Medicine and Surgery. 2021 Feb;11(2):852.
- Liu, S., See, K.C., Ngiam, K.Y., Celi, L.A., Sun, X. and Mengling Feng., 2020. Reinforcement learning for clinical decision support in critical care: comprehensive review. Journal of medical Internet research, 22(7), p.e18477 (IF 5.3).
- van den Boom, W., Hoy, M., Sankaran, J., Liu, M., Chahed, H., See, K.C and Mengling Feng., 2020. The search for optimal oxygen saturation targets in critically ill patients: observational data from large ICU databases. Chest (IF 9.65), 157(3), pp.566-573.
- Mengling Feng, et al. "Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms." JAMA network open 3.3 (2020): e200265-e200265 (IF 5).
- Mengling Feng et al. Transthoracic Echocardiography and Mortality in Sepsis: Analysis of the MIMIC-III Database. Intensive Care Medicine (IF 12). In press.
- Fuchs, Lior, Matthew Anstey, Mengling Feng, Ronen Toledano, Slava Kogan, Michael D. Howell, Peter Clardy, Leo Celi, Daniel Talmor, and Victor Novack. Quantifying the mortality impact of do-not-resuscitate orders in the ICU. Critical care medicine (IF 7.05) 45, no. 6 (2017): 1019-1027.
Address
12 Science Drive 2, Singapore 117549
ephfm{at}nus{dot}edu{dot}sg or mornin{at}gmail{dot}com
Mengling Feng @ MIT, US © 2017