Harnessing computational methods to lead a global transformation in health care
MIT is concentrating its efforts on the creation and commercialization of high-precision, affordable, and scalable machine learning technologies, making a profound impact on health care delivery across three key areas:
Our Main Areas of Focus:
Creating preventative medicine methods and technologies that can stop non-infectious disease in its tracks
At the intersection of biology, data collection, and machine learning, faculty and students at MIT are working on the development of new methods and technologies that have the potential to change the course of noninfectious disease by stopping it in its tracks. The promise is better outcomes across a wide range of conditions that affect millions—from cancer and neurodegenerative disorders to chronic illnesses such as diabetes, kidney disease, and asthma—as well lower costs associated with disease detection.
Making An Impact
Led by Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science, MIT researchers focused on breast cancer are collaborating with Massachusetts General Hospital (MGH) to develop new machine learning technologies that combine multifaceted patient data with biological insights targeted toward specific patient characteristics.
Developing cost-effective diagnostic tests to detect and alleviate health problems
MIT researchers are working on embedding machine learning techniques into devices such as wearables and wireless biosensors that may be able to both detect and alleviate health problems. With these low-cost devices, machine learning and data inference can be performed in a small footprint, mitigating the need for large, cloud-based infrastructures. The potential for broad reach and global impact is high.
Making An Impact
Feng Zhang, the James and Patricia Poitras Professor in Neuroscience, and James Collins, the Termeer Professor of Medical Engineering and Science, have pioneered a technology called SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) that adapts the CRISPR gene-editing technology to target RNA and can be used as a rapid, inexpensive diagnostic tool.
Discovering and developing new pharmaceuticals that can be tailored to
the individual patient
Conventional methods of discovering new therapeutics are declining in productivity while rising in cost. Machine learning has the powerful potential to address these challenges. This revolutionary approach could change drug manufacturing by determining the most efficient and cost-effective way to produce a given chemical compound. MIT’s work focuses on designing molecules with target biological properties, which is essential for drug tailoring and personalized medicine.
Making An Impact
A cross-disciplinary team of MIT researchers including William Green, Klavs Jensen, Regina Barzilay, and Tommi Jaakkola have developed a machine learning algorithm trained to predict outcomes of chemical reactions, assess product properties, and select optimal conditions for reactions. After training on close to a million reactions, the algorithm outperforms human experts.
Machine Learning for Home-Based Ultrasonic Ovarian Cancer Screening
Prof. Yonina Eldar,
Prof. Anthony E. Samir
This project is targeted at developing new methods and systems to detect ovarian cancer at the early curable stage before it has spread beyond the ovary in order to drastically improve ovarian cancer survival.
Shaping the Future of Disease Diagnostics
Prof. Eric Alm, Prof. David Sontag, Dr. Andrew Allegretti, Prof. Jordan Smoller
This project aims to improve healthcare decisions in challenging diagnostic areas. We envision the development of a non-invasive molecular test, effective across many disease areas, returning results rapidly.
Causal Experimentation and Modeling with Uncertain Disease Labels
Prof. Caroline Uhler
This project will address the challenges of how data is collected and analyzed on Amyotrophic Lateral Sclerosis (ALS) using algorithms to design experiments on neurons derived from ALS patients.
iBOCA: A Machine Learning App for Early Detection of Cognitive Impairment
Dr. Kalyan Veeramachaneni, Prof. Saman Amarasinghe, Dr. Chun Lim, Dr. Dan Press, Dr. John Torous
This project will develop a machine learning based iPad app for doctors to administer cognitive tests for assessing mild cognitive impairment. Our goals are (1) to develop machine learning based models for early detection and diagnosis, and (2) to enhance the app to collect more detailed data that will in turn enable rapid detection within 6 minutes.
Cardiovascular Data Science for Personalized Medicine
Prof. Collin M. Stultz,
Dr. Aaron D. Aguirre
This project will build and test models that identify personalized therapeutic interventions that minimize adverse outcomes in patients who are admitted with congestive heart failure.
Improving the Accuracy of Personalized Machine Learning to Monitor Depression
Prof. Rosalind W. Picard
This project will create a novel method to assess depressive symptoms for individuals by using machine learning analytics applied to objective data from phone sensors and wrist-band sensors. This machine learning work thus has the potential to significantly impact treatment of the foremost cause of disability.
Harnessing Signals of Clinical Effectiveness from Electronic Health Records (EHR) Data to Repurpose Existing Drugs for Unmet Medical Needs
Prof. Roy Welsch, Prof. Stan Finkelstein
This project has a clinical goal to identify signals of clinical effectiveness from EHR data to repurpose currently marketed drugs. And also a methodological goal to create machine learning algorithms that use observational data to compare cohorts of patients to emulate clinical trials.
Nocturnal Seizure Detection and Prediction in Epileptic Patients Using Radio Signals
Prof. Dina Katabi, Prof. Tim Morgenthaler, Dr. Melissa Lipford, Dr. Mithri Junna
In this project, we will augment the Emerald WiFi-like device with new machine learning models to detect epilepsy seizures simply by analyzing the surrounding wireless signals. We will also use the Emerald device to monitor the relationship between seizures and the patient’s vital signs and sleep stages. We will further study the feasibility of predicting seizures before they occur.
Deep Generative Models for Cryo-EM Reconstruction of Heterogeneous Biomolecular Structures
Prof. Joey Davis, Prof. Bonnie Berger, Prof. Bridget Carragher, Prof. Clint Potter
In this project we propose to develop revolutionary machine learning based techniques to determine structural ensembles of “misfolded” complexes. This work leverages the Berger Lab’s expertise in such computational approaches, MIT’s recent investments in cryo-EM instrumentation (MIT.nano), and Davis Lab’s expertise in producing, isolating and imaging such molecules.
Machine Learning Based Noninvasive Continuous Absolute Blood Pressure Monitoring
Prof. Hae-Seung Lee, Prof. Charles G. Sodini, Prof. Song Han
In this project, we investigate a new ML based continuous absolute blood pressure (BP) waveform monitoring method. This is based on our on-going research on pulse pressure monitoring using ultrasonography, which yields a continuous BP waveform instead of just systolic and diastolic pressure levels.
A Deep Learning System to Identify Combinatorial MEG and PET-based Biomarkers for Early Detection and Monitoring of Alzheimer’s Disease
Dr. Dimitrios Pantazis, Prof. Quanzheng Li, Prof. Fernando Maetsu
This proposal aims to first construct and monitor biomarkers using combined brain imaging modalities, then use a deep learning model that integrates connectome-structured data to discover multimodal features that predict an individual’s risk for AD.
Application of AI/Machine Learning to Clinical Mental Health: Depression, Bipolar Disorder, and Anxiety
Prof. John Gabrieli, Prof. Peter Szolovits
The goal of the present project is to create a new AI / machine learning bridge between one research group in the area of the diagnosis and treatment of mental disorders and another group in the Computer Science and Artificial Intelligence Lab (CSAIL).
Semi-Supervised Deep Learning for Red Blood Cell Morphological Classification with Applications to Sickle Cell Disease and Hyposplenism
Dr. Ming Dao
In this project we propose to develop a robust fully-automated real-time Red blood cells (RBC) classification method with applications to sickle cell disease (SCD) and hyposplenism (impaired spleen function), based on semi-supervised deep learning approach using generative adversarial network (GAN).
Project InCyte: Enhancing Histopathological Diagnosis with Digitization and Machine Learning
Prof. Pawan Sinha
In this project we will digitize the stored collections of pathology slides in Indian hospitals, and then use the resulting collection of images to train computational classifiers to infer disease labels from histo- and cyto-morphology. Additionally, we believe this will allow us to understand the trade-off between the quality of archiving processes and the long-term predictive value of data sources.
Interpretable and Transferable Prediction and Extraction Methods for Medical Reports
Prof. Tommi S. Jaakkola
In this project we focus on realizing and testing automated tools to reason about and extract information from medical reports and records. We design tools that are interpretable, verifiable, and transferable.
AI-Driven Diagnosis with Documentation in the Emergency Department
Prof. David Sontag
This project seeks to re-envision clinical documentation as cognitive aid by the creation of AI-driven user interfaces that are tightly integrated into the clinical workflow.
Private and Scalable Collaboration on Medical Images
Prof. Vinod Vaikuntanathan
In this project we propose to apply powerful methods in cryptography, called secure multiparty computation and homomorphic encryption, to design nimble solutions to the privacy-usefulness conundrum, computing together on the distributed data and models, while guaranteeing privacy to the individual stakeholders.
Deep Learning Peptide Representations for Optimal Cellular Delivery of Gene-Editing Cargo
Prof. Rafael Gomez-Bombarelli, Prof. Bradley L. Pentelute
In this project we will train convolutional neural networks on novel 2D representations of peptides that include variational topological representations of natural and unnatural amino acids.