Abstract: A system for biomarker data normalization in training datasets. The system includes one or more processors and one or more memory devices storing instructions that configure the one or more processors to perform operations. The operations may include receiving, data files comprising biomarker records (each of the biomarker records comprising a plurality of metadata fields), identifying a template record for normalization, the template record comprising template metadata fields, and generating a normalization vector comprising mismatching biomarker records. The operations may also include identifying adjustment functions for each one of the plurality of metadata fields, modifying data fields of biomarker records in the normalization vector by applying the adjustment functions, and generating a normalized data file comprising the modified biomarker records.

Abstract: A system for generating an interactive patient dashboard displaying metrics of a type of host response. The system may include processors and memory devices with instructions that configure the processors to perform operations. The operations may include transmitting a patient ID to a management platform or one or more devices (e.g., client devices), receiving electronic records, and employing a machine learning model to generate an acuity score based on the patient data. The operations may also include identifying critical parameters in the patient data by comparing parameters in the patient data with a distribution of parameters. The system can determine a ranking of the parameters, and generating a patient dashboard graphical user interface (GUI) for display. The dashboard GUI may include a prognostic indicator, and a list displaying the parameters according to the ranking.

Abstract: A system for adapting a machine learning model for a specific population. The system includes a processor and memory devices storing instructions that configure the memory devices to perform operations. The operations may include receive a local dataset comprising local records of patients associated with the healthcare facility, perform a clustering function, and retrieving a template dataset comprising template records organized in clusters with variable centroids. The operations may also include calculating a similarity metric between the local and template records, generating a synthetic dataset by combining template and local records, segregating the synthetic dataset into a training synthetic dataset and a testing synthetic dataset, and generating and/or validating a machine learning predictive model by tuning a template model according to the training synthetic dataset and/or generating a new predictive model.

Abstract: A method for making dynamic risk predictions is provided. The method includes receiving a dataset with a first data field and a second data field. The first data field is populated with a measured value. The method also includes imputing a first predicted value to the second data field, generating a first risk score and a first set of associated metrics based on the measured value and the first predicted value, and imputing a second predicted value to the second data field. The method also includes calculating a statistically derived metric and determining whether the statistically derived metric exceeds a predetermined threshold, wherein a predetermined action is recommended if the statistically derived metric exceeds the predetermined threshold. A system and a non-transitory, computer readable medium storing instructions to cause the system to perform the above method are also provided.

Abstract: A method for ranking an unmeasured feature for an instance given at least one feature is measured is provided. The method includes imputing a first value to the unmeasured feature in the instance while holding the other remaining unmeasured features constant and evaluating a first outcome with a model using the first value in the instance. The method includes imputing a second value to the unmeasured feature in the dataset while holding the other remaining unmeasured features constant, evaluating a second outcome with the model using the second value in the instance, and determining a statistical parameter with the first outcome and the second outcome. The method also includes assigning the unmeasured feature a ranking corresponding to the determined statistical parameter. A system and a non-transitory, computer readable medium storing instructions to perform the above method are also presented.

Abstract: A stacked testing assembly (100) includes a microfluidic cartridge (10) for analyzing a fluid sample and a testing setup, said microfluidic cartridge includes a number of layers (1, 2) stacked in a height direction with many different kinds of combinations, said testing setup (20) is capable of assembling and testing all kinds of said layers combinations with no change to the setup.

Abstract: Provided are methods and devices for the label free detection of analytes in solution, including analytes suspended in a biological fluid. A field effect transistor (FET) is positioned in close proximity to a paired set of reference electrodes and the reference electrodes electrically biased to provide desalting and a stable gate voltage to the FET. In this manner, charged ions are depleted in the sensing region of the sensor and device sensitivity to analyte detection improved by the removal of charge that otherwise interferes with measurement. Also provided are methods and systems providing increased in reference electrode surface area and/or decrease in droplet volume to further improve label-free detection of analytes.

Abstract: Provided are methods and devices for the label free detection of analytes in solution, including analytes suspended in a biological fluid. A field effect transistor (FET) is positioned in close proximity to a paired set of reference electrodes and the reference electrodes electrically biased to provide desalting and a stable gate voltage to the FET. In this manner, charged ions are depleted in the sensing region of the sensor and device sensitivity to analyte detection improved by the removal of charge that otherwise interferes with measurement. Also provided are methods and systems providing increased in reference electrode surface area and/or decrease in droplet volume to further improve label-free detection of analytes.

Abstract: Provided are methods and systems that characterize a property of a particle suspended in a fluid sample in a label-free manner. Detection elements are provided fluidically adjacent upstream and downstream from a modulation element. Fluid sample containing particles flows across a first detection element and a first particle parameter detected for each particle that passes the first detection element or a first aggregate particle parameter for a plurality of particles that pass the first detection element. The particles flow from the first detection element to a first modulation element, wherein the first modulation element effects a change in a property of the particles flowing past the first modulation element. A second detection element then detects the particle parameter again or a second aggregate particle parameter for a plurality of particles that pass the second detection element. Comparing the first and second particle or aggregate parameters thereby characterizes the particle property.

Abstract: A stacked testing assembly (100) includes a microfluidic cartridge (10) for analyzing a fluid sample and a testing setup, said microfluidic cartridge includes a number of layers (1, 2) stacked in a height direction with many different kinds of combinations, said testing setup (20) is capable of assembling and testing all kinds of said layers combinations with no change to the setup.

Abstract: Provided herein are methods and devices for measuring pH and for amplifying a pH signal to obtain ultrasensitive detection of changes in pH. This is achieved by providing a sensor and a transducer, wherein the sensor transconductance is sensitive to changes in pH and the transducer transconductance is not affected by pH change. The transducer instead compensates for changes in the sensor transconductance arising from pH change. The unique configuration of the sensor and transducer with respect to each other provides substantial increases in a pH amplification factor, thereby providing pH sensing devices with a giant Nernst response and, therefore, effectively increased pH sensitivity.

Abstract: Provided are methods and devices for the label free detection of analytes in solution, including analytes suspended in a biological fluid. A field effect transistor (FET) is positioned in close proximity to a paired set of reference electrodes and the reference electrodes electrically biased to provide desalting and a stable gate voltage to the FET. In this manner, charged ions are depleted in the sensing region of the sensor and device sensitivity to analyte detection improved by the removal of charge that otherwise interferes with measurement. Also provided are methods and systems providing increased in reference electrode surface area and/or decrease in droplet volume to further improve label-free detection of analytes.

Abstract: In one aspect, described herein are field effect chemical sensor devices useful for chemical and/or biochemical sensing. Also provided herein are methods for single molecule detection. In another aspect, described herein are methods useful for amplification of target molecules by PCR.

Abstract: Provided herein are methods and devices for measuring pH and for amplifying a pH signal to obtain ultrasensitive detection of changes in pH. This is achieved by providing a sensor and a transducer, wherein the sensor transconductance is sensitive to changes in pH and the transducer transconductance is not affected by pH change. The transducer instead compensates for changes in the sensor transconductance arising from pH change. The unique configuration of the sensor and transducer with respect to each other provides substantial increases in a pH amplification factor, thereby providing pH sensing devices with a giant Nernst response and, therefore, effectively increased pH sensitivity.

Abstract: Provided are semiconductor field effect sensors including a high-k thin film gate dielectric. The semiconductor field effect sensors described herein exhibit high detection sensitivity and enhanced reliability when placed in contact with liquids. Also disclosed are semiconductor field effect sensors having optimized fluid gate electrode voltages and/or back gate electrode voltages for improved detection sensitivity.

Abstract: In one aspect, described herein are field effect chemical sensor devices useful for chemical and/or biochemical sensing. Also provided herein are methods for single molecule detection. In another aspect, described herein are methods useful for amplification of target molecules by PCR.

Abstract: Apparatus and methods for detecting molecules in a fluidic environment are provided, including nanodevices and methods for fabricating, functionalizing, and operating such nanodevices. At least one of the methods includes selective heating of nanodevices in an array.