Abstract: A system for identifying biomarkers in a digital image of a Hematoxylin and Eosin-stained slide of a target tissue includes a processor and an electronic network; and a memory having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to: process segmented tile images determine a predicted biomarker presence; and transmit the predicted presence. A non-transitory computer-readable medium includes a set of computer-executable instructions that, when executed by one or more processors, cause a computer to: process segmented tile images; determine a predicted biomarker presence; and transmit the predicted presence. A computer-implemented method includes processing segmented tile images; determining a predicted biomarker presence; and transmitting the predicted presence.

Abstract: A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: Systems and methods are provided for predicting metastasis of a cancer in a subject. A plurality of data elements for the subject's cancer is obtained, including sequence features comprising relative abundance values for gene expression in a cancer biopsy of the subject, optional personal characteristics about the subject, and optional clinical features related to the stage, histopathological grade, diagnosis, symptom, comorbidity, and/or treatment of the cancer in the subject, and/or a temporal element associated therewith. One or more models are applied to the plurality of data elements, determining one or more indications of whether the cancer will metastasize. A clinical report comprising the one or more indications is generated.

Abstract: A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

Abstract: A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

Abstract: A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.

Abstract: A generalizable and interpretable deep learning model for predicting microsatellite instability from histopathology slide images is provided. Microsatellite instability (MSI) is an important genomic phenotype that can direct clinical treatment decisions, especially in the context of cancer immunotherapies. A deep learning framework is provided to predict MSI from histopathology images, to improve the generalizability of the predictive model using adversarial training to new domains, such as on new data sources or tumor types, and to provide techniques to visually interpret the topological and morphological features that influence the MSI predictions.

Abstract: A method for checking the quality of data content in a structured clinical record is disclosed. The method may include the steps of providing a data quality test that checks the content of at least a portion of the data content in the structured clinical record, applying the data quality test to the portion of the data content, and returning the results of the data quality test.

Abstract: A system and method are provided for training and using a machine learning model to analyze hematoxylin and eosin (H&E) slide images, where the machine learning model is trained using a training data set comprising a plurality of unmarked H&E images and a plurality of marked H&E images, each marked H&E image being associated with one unmarked H&E image and each marked H&E image including a location of one or more molecules determined by analyzing a multiplex IHC image having at least two IHC stains, each IHC stain having a unique color and a unique target molecule. Predicted molecules and locations identified with the machine learning model result in an immunotherapy response class being assigned to the H&E slide image.

Abstract: A method and system of audibly broadcasting responses to a user based on user queries about a specific patient report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.

Abstract: Systems and methods are provided for implementing a tool for evaluating an effect on an event, such as a medication or treatment, on a subject's condition, using a propensity model that identifies matched treatment and control cohorts within a base population of subjects. A propensity value threshold, which can be obtained based on user input, can be used to adjust the selection of subjects for treatment and control cohorts. The tool allows analyzing features of the subjects in the treatment and control groups, and further allows for evaluation and comparison of survival objectives of subjects in the treatment and control groups.

Abstract: A method and system for storing user application programs and micro-service programs, for each of multiple patients that have cancerous cells and receive treatment, includes obtaining clinical records data in original forms, storing it in a semi-structured first database, generating sequencing data for the patient's cancerous and normal cells using a next generation genomic sequencer, storing the sequencing data in the first database, shaping at least some of the first database data to generate system structured data optimized for searching and including clinical record data, storing the structured data in a second database, for each user application program, selecting an application-specific subset of data from the second database and storing it in a structure optimized for application program interfacing in a third database, wherein an orchestration manager operatively connected to one or more micro-service programs receives status messages and initiates a respective micro-service program when program prere

Abstract: A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A system for personalized depression disorder treatment is disclosed herein. The system includes a server configured to communicate with existing healthcare resources and to receive patient data corresponding to a patient, the server including an analytics module. The system further includes a first database configured to store empirical patient outcomes, and further configured to communicate with the analytics module. Additionally, the system includes a user device having a graphical user interface (GUI) configured to communicate with the server and to display at least one output generated by the analytics module. The analytics module is configured to determine at least one of a personalized depression treatment and a personalized depression state prediction based on the empirical patient outcomes and the patient data.

Abstract: A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.

Abstract: A system, method, and mobile device application are configured to capture, with a mobile device, a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient. The method includes receiving, from a mobile device, an image of a medical document comprising NGS medical information of the patient, extracting a first region from the image, extracting NGS medical information of the patient from the first region into a structured dataset, the extracted NGS medical information including at least one RNA expression, correlating a portion of the extracted NGS medical information that includes the at least one RNA expression with summarized medical information from a cohort of patients similar to the patient, and generating, for display on the mobile device, a clinical decision support report comprising the summarized medical information.

Abstract: A method and system for conducting genomic sequencing, the method comprising storing a set of user application programs wherein each of the programs requires an application specific subset of data, for each of a plurality of patients that have cancerous cells and that receive cancer treatment, obtaining clinical records data in original forms including cancer state information, treatment types and treatment efficacy information, storing the clinical records data in a semi-structured first database, for each patient, using a genomic sequencer to generate genomic sequencing data for the patient's cancerous cells and normal cells, storing the sequencing data in the first database, shaping at least a subset of the first database data to generate system structured data including clinical record data and sequencing data wherein the system structured data is optimized for searching, storing the system structured data in a second database, for each user application program, selecting the application specific subset o

Abstract: Provided herein are novel organoid culture media, organoid culture systems, and methods of culturing tumor organoids using the subject organoid culture media. Also provided herein are tumor organoids developed using such organoid culture systems, methods for assessing the clonal diversity of the tumor organoids, and methods for using such tumor organoids, for example, for tumor modelling and drug development applications. In particular embodiments, the tumor organoid culture media provided herein is substantially free of R-spondins (e.g., R-spondin1).