Learning Patient Static Information from Time-series EHR and an Approach for Safeguarding Privacy and Fairness

Recent work in machine learning for healthcare has raised concerns about patient privacy and algorithmic fairness. For example, previous work has shown that patient self-reported race can be predicted from medical data that does not explicitly contain racial information. However, the extent of data identification is unknown, and we lack ways to develop models whose outcomes are minimally affected by such information. Here we systematically investigated the ability of time-series electronic health record data to predict patient static information. We found that not only the raw time-series data, but also learned representations from machine learning models, can be trained to predict a variety of static information with area under the receiver operating characteristic curve as high as 0.851 for biological sex, 0.869 for binarized age and 0.810 for self-reported race. Such high predictive performance can be extended to a wide range of comorbidity factors and exists even when the model was trained for different tasks, using different cohorts, using different model architectures and databases. Given the privacy and fairness concerns these findings pose, we develop a variational autoencoder-based approach that learns a structured latent space to disentangle patient-sensitive attributes from time-series data. Our work thoroughly investigates the ability of machine learning models to encode patient static information from time-series electronic health records and introduces a general approach to protect patient-sensitive attribute information for downstream tasks

Probing Embryonic Stem Cell Autocrine and Paracrine Signaling Using Microfluidics

Although stem cell fate is traditionally manipulated by exogenously altering the cells' extracellular signaling environment, the endogenous autocrine and paracrine signals produced by the cells also contribute to their two essential processes: self-renewal and differentiation. Autocrine and/or paracrine signals are fundamental to both embryonic stem cell self-renewal and early embryonic development, but the nature and contributions of these signals are often difficult to fully define using conventional methods. Microfluidic techniques have been used to explore the effects of cell-secreted signals by controlling cell organization or by providing precise control over the spatial and temporal cellular microenvironment. Here we review how such techniques have begun to be adapted for use with embryonic stem cells, and we illustrate how many remaining questions in embryonic stem cell biology could be addressed using microfluidic technologies.National Institutes of Health (U.S.) (EB007278)Singapore-MIT AllianceNational Science Foundation (U.S.) (0939511

A Multidatabase ExTRaction PipEline (METRE) for Facile Cross Validation in Critical Care Research

Transforming raw EHR data into machine learning model-ready inputs requires considerable effort. One widely used EHR database is Medical Information Mart for Intensive Care (MIMIC). Prior work on MIMIC-III cannot query the updated and improved MIMIC-IV version. Besides, the need to use multicenter datasets further highlights the challenge of EHR data extraction. Therefore, we developed an extraction pipeline that works on both MIMIC-IV and eICU Collaborative Research Database and allows for model cross validation using these 2 databases. Under the default choices, the pipeline extracted 38766 and 126448 ICU records for MIMIC-IV and eICU, respectively. Using the extracted time-dependent variables, we compared the Area Under the Curve (AUC) performance with prior works on clinically relevant tasks such as in-hospital mortality prediction. METRE achieved comparable performance with AUC 0.723- 0.888 across all tasks. Additionally, when we evaluated the model directly on MIMIC-IV data using a model trained on eICU, we observed that the AUC change can be as small as +0.019 or -0.015. Our open-source pipeline transforms MIMIC-IV and eICU into structured data frames and allows researchers to perform model training and testing using data collected from different institutions, which is of critical importance for model deployment under clinical contexts

A microfabricated dielectrophoretic trapping array for cell-based biological assays

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 143-152).This thesis presents the development of a small planar array of microfabricated traps for holding single cells and performing assays on them. The traps use the phenomenon of dielectrophoresis-the force on polarizable bodies in a non-uniform electric field-to make potential energy wells. These potential energy wells are electrically switchable, arrayable, and amenable to batch fabrication. The trapping arrays have potential use as a cytometer for monitoring the dynamics of populations of single cells and then sorting those cells based upon those dynamics. To design such traps, I have developed a modeling environment that can absolutely predict the ability of DEP-based traps to hold particles against liquid flows, which are the dominant destabilizing force in these systems. I have used the common easy-to-fabricate planar quadrupole trap to verify the accuracy of these modeling tools, and in the process determined why planar quadrupole traps behave as they do. I next used the modeling tools to design an improved quadrupole trap-the extruded quadrupole-that has the potential to hold particles lOx-100x stronger. The extruded quadrupole trap consists of a set of microfabricated gold posts arranged in a trapezoidal fashion, to ease trap loading, and includes metal substrate shunts to improve performance. The fabrication process for small arrays of these traps uses electroplating of gold into an SU-8 mold to achieve the required geometries. The final section of the thesis details experiments using small arrays of these extruded quadrupole traps. Experiments were performed with beads to verify the strong nature of the trap and then with cells to demonstrate qualitative operation of the arrays and the ability to perform dynamic fluorescent assays on multiple single cells followed by sorting. The technology is now well poised to enable the development of biological assays that are currently unavailable.by Joel Voldman.Ph.D

Multiparameter cell-tracking intrinsic cytometry for single-cell characterization

An abundance of label-free microfluidic techniques for measuring cell intrinsic markers exists, yet these techniques are seldom combined because of integration complexity such as restricted physical space and incompatible modes of operation. We introduce a multiparameter intrinsic cytometry approach for the characterization of single cells that combines ≥2 label-free measurement techniques onto the same platform and uses cell tracking to associate the measured properties to cells. Our proof-of-concept implementation can measure up to five intrinsic properties including size, deformability, and polarizability at three frequencies. Each measurement module along with the integrated platform were validated and evaluated in the context of chemically induced changes in the actin cytoskeleton of cells. viSNE and machine learning classification were used to determine the orthogonality between and the contribution of the measured intrinsic markers for cell classification

Cell-Based Biosensor to Report DNA Damage in Micro- and Nanosystems

Understanding how newly engineered micro- and nanoscale materials and systems that interact with cells impact cell physiology is crucial for the development and ultimate adoption of such technologies. Reports regarding the genotoxic impact of forces applied to cells in such systems that can both directly or indirectly damage DNA emphasize the need for developing facile methods to assess how materials and technologies affect cell physiology. To address this need we have developed a TurboRFP-based DNA damage reporter cell line in NIH-3T3 cells that fluoresce to report genotoxic stress caused by a wide variety of agents, from chemical genotoxic agents to UV-C radiation. Our biosensor was successfully implemented in reporting the genotoxic impact of nanomaterials, demonstrating the ability to assess size dependent geno- and cyto-toxicity. The biosensor cells can be assayed in a high throughput, noninvasive manner, with no need for overly sophisticated equipment or additional reagents. We believe that this open-source biosensor is an important resource for the community of micro- and nanomaterials and systems designers and users who wish to evaluate the impact of systems and materials on cell physiology.National Institutes of Health (U.S.) (Grant GM090194

Electrokinetic confinement of axonal growth for dynamically configurable neural networks

Axons in the developing nervous system are directed via guidance cues, whose expression varies both spatially and temporally, to create functional neural circuits. Existing methods to create patterns of neural connectivity in vitro use only static geometries, and are unable to dynamically alter the guidance cues imparted on the cells. We introduce the use of AC electrokinetics to dynamically control axonal growth in cultured rat hippocampal neurons. We find that the application of modest voltages at frequencies on the order of 10[superscript 5] Hz can cause developing axons to be stopped adjacent to the electrodes while axons away from the electric fields exhibit uninhibited growth. By switching electrodes on or off, we can reversibly inhibit or permit axon passage across the electrodes. Our models suggest that dielectrophoresis is the causative AC electrokinetic effect. We make use of our dynamic control over axon elongation to create an axon-diode via an axon-lock system that consists of a pair of electrode ‘gates’ that either permit or prevent axons from passing through. Finally, we developed a neural circuit consisting of three populations of neurons, separated by three axon-locks to demonstrate the assembly of a functional, engineered neural network. Action potential recordings demonstrate that the AC electrokinetic effect does not harm axons, and Ca[superscript 2+] imaging demonstrated the unidirectional nature of the synaptic connections. AC electrokinetic confinement of axonal growth has potential for creating configurable, directional neural networks.National Institutes of Health (U.S.) (R01 EUREKA Award R01-NS066352

Massively Parallel Microfluidic Cell-Pairing Platform for the Statistical Study of Immunological Cell-Cell Interactions

Variability in cell-cell interactions is ubiquitous and particularly relevant for the immune system, where the reliability of cell-cell interactions is critical for the prevention of disease. This variability is poorly understood mainly due to the limitations of current methods. We have therefore designed a highly parallel microfluidic cell-pairing device and optimized its pairing efficiency using fluids modeling. The optimized device can hydrodynamically pair hundreds of primary mouse immune-cells at an efficiency of ~50%. We measured T cell activation dynamics of ~130 primary mouse T cells paired with B cells. Our findings represent the first time that variation has been observed in T cell activation dynamics.National Institutes of Health (U.S.) (NIH (EB008550))Singapore-MIT Allianc

Particle Capture Devices and Methods of Use Thereof

The present invention provides a device and methods of use thereof in microscale particle capturing and particle pairing. This invention provides particle patterning device, which mechanically traps individual particles within first chambers of capture units, transfer the particles to second chambers of opposing capture units, and traps a second type of particle in the same second chamber. The device and methods allow for high yield assaying of trapped cells, high yield fusion of trapped, paired cells, for controlled binding of particles to cells and for specific chemical reactions between particle interfaces and particle contents. The device and method provide means of identification of the particle population and a facile route to particle collection