Combining clinical data from millions of patients, and behaviors of tens of thousands of care providers to assist doctors and patients in making better diagnoses, prognoses and therapeutic decisions.
- Tianxi Cai
- Maha Farhat
- Isaac Kohane
- Arjun Manrai
- Chirag Patel
- Pranav Rajpurkar
- Kun-Hsing Yu
- Marinka Zitnik
Areas of Focus
AI to Accelerate the Life Sciences/Drug Discovery
- Network Medicine Framework for Identifying Drug Repurposing Opportunities for COVID-19. Deisy Morselli Gysi, Italo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, J.J. Patten, Robert A. Devey, Joseph Loscalzo, and Albert-Laszlo Barabasi (Proceedings of the National Academy of Sciences, 2021).
- Identification of Disease Treatment Mechanisms through the Multiscale Interactome. Camilo Ruiz, Marinka Zitnik, and Jure Leskovec (Nature Communications, 2021).
- Population-Scale Identification of Differential Adverse Events Before and During a Pandemic. Xiang Zhang, Marissa Sumathipala, and Marinka Zitnik (Nature Computational Science, 2021).
- Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development. Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik (Proceedings of Neural Information Processing Systems, 2021).
- Evolution of Resilience in Protein Interactomes Across the Tree of Life. Marinka Zitnik, Rok Sosic, Marcus W. Feldman, and Jure Leskovec (Proceedings of the National Academy of Sciences, 2019).
- MARS: Discovering Novel Cell Types across Heterogeneous Single-Cell Experiments. Maria Brbic, Marinka Zitnik, Sheng Wang, Angela O. Pisco, Russ B. Altman, Spyros Darmanis, and Jure Leskovec (Nature Methods, 2020).
- Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles. Samuel G. Finlayson, Matthew B.A. McDermott, Alex V. Pickering, Scott L. Lipnick and Isaac S. Kohane (Pac Symp Biocomput., 2021).
Alternative Models to Trials Using Real-World Data
- Illustrating potential effects of alternate control populations on real-world evidence-based statistical analyses. Yidi Huang, William Yuan, Isaac S Kohane, and Brett K Beaulieu-Jones (JAMIA Open, 2021).
Driving Gene-by-Environment Discovery in Biobanked Data
- Data-driven assessment, contextualisation and implementation of 134 variables in the risk for type 2 diabetes: an analysis of Lifelines, a prospective cohort study in the Netherlands. Thomas P van der Meer, Bruce H R Wolffenbuttel, and Chirag J Patel (Diabetologia, 2021).
- Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes. Yixuan He, Chirag M Lakhani, Danielle Rasooly, Arjun K Manrai, Ioanna Tzoulaki, and Chirag J Patel (Diabetes Care, 2021).
Leveraging EHR Biobank Data to Explore Alternative Indications and Potential Side Effects
- Association of Interleukin 6 Receptor Variant With Cardiovascular Disease Effects of Interleukin 6 Receptor Blocking Therapy: A Phenome-Wide Association Study. Cai T, Zhang Y, Ho YL, Link N, Sun J, Huang J, Cai TA, Damrauer S, Ahuja Y, Honerlaw J, Huang J, Costa L, Schubert P, Hong C, Gagnon D, Sun YV, Gaziano JM, Wilson P, Cho K, Tsao P, O'Donnell CJ, Liao KP; VA Million Veteran Program (JAMA Cardiol., 2018).
Shortening the Duration of Clinical Trials
- Quantifying the feasibility of shortening clinical trial duration using surrogate markers. Wang, X., Cai, T., Tian, L., Bourgeois, F. and Parast, L., (Statistics in Medicine, 2021).
Cautions and Limitations of AI
- Adversarial attacks on medical machine learning . Samuel G. Finlayson, John D. Bowers, Joichi Ito, Jonathan L. Zittrain, Andrew L. Beam, and Isaac S. Kohane (Science, 2019).
- The Clinician and Dataset Shift in Artificial Intelligence. Samuel G Finlayson, Adarsh Subbaswamy, Karandeep Singh, John Bowers, Annabel Kupke, Jonathan Zittrain, Isaac S Kohane, and Suchi Saria (NEJM, 2021).
- Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?. Brett K Beaulieu-Jones, William Yuan, Gabriel A Brat, Andrew L Beam, Griffin Weber, Marshall Ruffin, and Isaac S Kohane (NPJ Digit Med, 2021).
- Temporal bias in case-control design: preventing reliable predictions of the future. William Yuan, Brett K Beaulieu-Jones, Kun-Hsing Yu, Scott L Lipnick, Nathan Palmer, Joseph Loscalzo, Tianxi Cai, and Isaac S Kohane (Nat Commun, 2021)
- Framing the challenges of artificial intelligence in medicine. Kun-Hsing Yu and Isaac S. Kohane. (BMJ Quality & Safety, 2019)
Meta-Science Approaches and Data to Probe Assumptions in Model Building
- Development and validation pathways of artificial intelligence tools evaluated in randomised clinical trials. George C M Siontis, Romy Sweda, Peter A Noseworthy, Paul A Friedman, Konstantinos C Siontis, and Chirag J Patel (BMJ Health Care Inform., 2021).
- Leveraging vibration of effects analysis for robust discovery in observational biomedical data science. Braden T Tierney, Elizabeth Anderson, Yingxuan Tan, Kajal Claypool, Sivateja Tangirala, Aleksandar D Kostic, Arjun K Manrai, and Chirag J Patel (PLoS Biol., 2021).
- A standard database for drug repositioning. Adam S Brown and Chirag J Patel (Sci Data, 2017).
Clinical Applications of AI
Antibiotic Resistance Prediction
Autism Detection and Retaxonomization
Medical Image Interpretation
- CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV. Pranav Rajpurkar, Chloe O’Connell, Amit Schechter, Nishit Asnani, Jason Li, and others (npj Digital Medicine, 2020).
- RadGraph: Extracting Clinical Entities and Relations from Radiology Reports. Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P Lungren, Andrew Y Ng, Curtis Langlotz, and Pranav Rajpurkar (Proceedings of Neural Information Processing Systems, NeurIPS, 2021).
- Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Awni Y Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H Tison, and others (Nature Medicine, 2019).
- Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. Pranav Rajpurkar, Jeremy Irvin, Robyn L Ball, and others (PLOS Medicine, 2018).
Pathology Image Analysis
- Development of a Histopathology Informatics Pipeline for Classification and Prediction of Clinical Outcome in Subtypes of Renal Cell Carcinoma. Eliana Marostica, Rebecca Barber, Thomas Denize, Isaac S. Kohane, Sabina Signoretti, Jeffrey A Golden, and Kun-Hsing Yu (Clinical Cancer Research, 2021).
- Integrative Multiomics-Histopathology Analysis for Breast Cancer Classification. Yasha Ektefaie, William Yuan, Deborah A. Dillon, Nancy U. Lin, Jeffrey A. Golden, Isaac S. Kohane, and Kun-Hsing Yu. (npj Breast Cancer, 2021)
- Deciphering Serous Ovarian Carcinoma Histopathology and Platinum Response by Convolutional Neural Networks. Kun-Hsing Yu, Vincent Hu, Feiran Wang, Ursula Matulonis, George L. Mutter, Jeffrey A. Golden, and Isaac S. Kohane (BMC Medicine, 2020).
- Classifying Non-Small Cell Lung Cancer Types and Transcriptomic Subtypes using Convolutional Neural Networks. Kun-Hsing Yu, Feiran Wang, Gerald J. Berry, Christopher Ré, Russ B. Altman, Michael Snyder, and Isaac S. Kohane. (JAMIA, 2020)
Fundamentals of AI
Federated Learning with Heterogeneous High-Dimensional Data from Multiple Institutions
Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints. Molei Liu, Yin Xia, Kelly Cho, and Tianxi Cai (JMLR, 2020).
- Individual data protected integrative regression analysis of high-dimensional heterogeneous data. Tianxi Cai, Molei Liu, and Yin Xia (JASA, 2021).
Learning from Limited Labeled Data Using Self-Supervised and Supervised Pre-Training
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations. Bryan Gopal, Ryan W Han, Gautham Raghupathi, Andrew Y Ng, Geoffrey H Tison, and Pranav Rajpurkar (Proceedings of ML4H, 2021)
- CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation. Alexander Ke, William Ellsworth, Oishi Banerjee, Andrew Y Ng, and Pranav Rajpurkar (Proceedings of ACM Conference on Health, Inference, and Learning (ACM-CHIL), 2021)
- AppendiXNet: Deep Learning for Diagnosis of Appendicitis from A Small Dataset of CT Exams Using Video Pretraining. Pranav Rajpurkar, Allison Park, Jeremy Irvin, Chris Chute, and others (Scientific Reports, 2020).
- MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation. Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y Ng, and Pranav Rajpurkar (Proceedings of Machine Learning for Healthcare (MLHC), 2021).
Learning from Multimodal Data with Graph Neural Networks
- Towards a Unified Framework for Fair and Stable Graph Representation Learning. Chirag Agarwal, Himabindu Lakkaraju, and Marinka Zitnik (International Conference on Uncertainty in Artificial Intelligence, UAI, 2021).
- Subgraph Neural Networks. Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, and Marinka Zitnik. (Proceedings of Neural Information Processing Systems, NeurIPS, 2020).
- GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. Xiang Zhang and Marinka Zitnik (Proceedings of Neural Information Processing Systems, NeurIPS, 2020).
- Graph Meta Learning via Local Subgraphs. Kexin Huang and Marinka Zitnik (Proceedings of Neural Information Processing Systems, NeurIPS, 2020).
- Pre-training Graph Neural Networks. Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec (Proceedings of the International Conference on Learning Representations, ICLR, 2020).
- GNN Explainer: Generating Explanations for Graph Neural Networks. Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec (Proceedings of Neural Information Processing Systems, NeurIPS, 2019).
ML to Assist in Cross-Institutional EHR Data Harmonization
Spherical regression under mismatch corruption with application to automated knowledge translation. Xu Shi, Xiaoou Li, and Tianxi Cai (JASA, 2020).
In the News
Adverse Drug Effects During the COVID-19 Pandemic (October 13, 2021)
Research links public health emergencies, adverse drug reactions
More Intelligent Medicine (March 11, 2021)
Leaders in biomedical informatics chart roadmap for harnessing the promise of medical AI
Got Resistance? (April 29, 2019)
When it comes to TB, the computer has the answer
The Doctor and the Machine (April 3, 2019)
In new report, Harvard Med, Google scientists outline the promises and pitfalls of machine learning in medicine.
One Giant Step (Winter 2019)
Researchers are building an artificial intelligence system that can mimic human clinical decision making