Abstract: Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Abstract: A system for tracking adverse events may include at least one processing device programmed to receive a request from a user to record an adverse event experienced by a patient; receive a search term input by the user; identify, in an adverse event database and based on the search term, at least one database record for an adverse event, wherein the at least one database record includes an adverse event type and at least one characteristic; receive, via an input field, a rating of the at least one characteristic for the patient; generate an adverse event record based on the adverse event type and the rating; and store the adverse event record in an adverse event log.

Abstract: A model-assisted system for identifying a group of patients for a cohort using a generalized biomarker model may include a processor programmed to provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers; receive, from the generalized biomarker model, an output indicating a plurality of individuals with associated likelihoods of at least one of: having an attribute associated with the third biomarker or having been tested for the attribute associated with the first biomarker; determine a likelihood threshold based on a predetermined cohort size associated with the first biomarker and identify, based on the output, a group of the plurality of individuals for inclusion in a cohort, each individual in the group of the plurality of individuals being associated with a likelihood received from the generalized biomarker model that satisfies the likelihood threshold.

Abstract: A computer-implemented system for determining a genomic testing status of a patient may include at least one processor programmed receive, from a source, unstructured information from a plurality of patient records associated with a patient; determine, using a first machine learning model, a primary patient record from among the plurality of patient records, wherein at least a portion of information represented in the primary patient record correlates to genomic testing; determine, using a second machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; and display a user interface comprising an indicator of the genomic testing status of the patient and a link to the primary patient record.

Abstract: A computer-implemented system for determining trials using a metastatic condition of a patient may include at least one processor programmed to receive a selection of a patient; access, in response to the selection of the patient, a patient dataset associated with the patient; receive a predicted metastatic condition associated with the patient; cause display of at least a first portion of the patient dataset and the predicted metastatic condition; determine, based on at least a second portion of the patient dataset or the predicted metastatic condition, a subset of trials for the patient, wherein the subset of trials for the patient is determined from a plurality of trials; and cause display of at least the subset of the trials for the patient.

Abstract: A graphical user interface for displaying an electronic medical record associated with a patient is provided. The graphical user interface may include an area configured to display patient information, which may include at least a name of the patient. The graphical user interface may also include an indicator displayed in association with the name of the patient. The indicator may include information specifying that the patient is potentially eligible for one or more trials, the patient is participating in one or more trials, or the patient has completed one or more trials.

Abstract: Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Abstract: A model-assisted system for identifying candidates for a cohort based on a biomarker may include at least one processor. The processor may be programmed to access a database from which information associated with a population of individuals can be derived; provide, to a generalized biomarker model, a first biomarker associated with a cohort, the generalized biomarker model being trained based on one or more second biomarkers using the information, wherein the first biomarker is different from the one or more second biomarkers; receive, from the generalized biomarker model, a first output indicating a first group of the population of individuals exceeding a first likelihood threshold of having been tested for the first biomarker; and determine, based on the first output, whether an individual from among the first group of the population of individuals is a candidate for the cohort.

Abstract: A graphical user interface for displaying an electronic medical record associated with a patient is provided. The graphical user interface may include an area configured to display patient information, which may include at least a name of the patient. The graphical user interface may also include an indicator displayed in association with the name of the patient. The indicator may include information specifying that the patient is potentially eligible for one or more trials, the patient is participating in one or more trials, or the patient has completed one or more trials.

Abstract: Described herein is a graphical intervention test development system. The graphical intervention test development system provides a graphical intervention test development environment that facilitates computer-based design of an intervention test. The graphical intervention test development environment provides a graphical user interface (GUI) and visualizations of various aspects of an intervention test therein. The graphical intervention test development environment further provides a control interface through which a user can manipulate control parameters that affect outcomes of the intervention test.

Abstract: A computer-implemented system for identifying a patient for a trial may include at least one processor. The at least one processor may be programmed to receive an indication of a selected trial, the selected trial being associated with a testing status criterion; access a plurality of patient records associated with a patient of a plurality of patients; determine, using a machine learning model and based on unstructured information from one at least one of the patient records, a likelihood of an occurrence of genomic testing for the patient; determine a genomic testing status of the patient based on the determined likelihood of the occurrence of genomic testing; determine that the genomic testing status satisfies the testing status criterion; and include the patient in a subset of the plurality of patients based on the genomic testing status satisfying the testing status criterion.

Abstract: A model-assisted system for predicting survivability of a patient may include at least one processor. The processor may be programmed to access a database storing a medical record for the patient. The medical record may include at least one of structured and unstructured information relative to the patient and may lack a structured patient ECOG score. The processor may be further programmed to analyze at least one of the structured and unstructured information relative to the patient; based on the analysis, and in the absence of a structured ECOG score, generate a performance status prediction for the patient; and provide an output indicative of the predicted performance status. The analysis of at least one of the structured and unstructured information and the generation of the predicted performance status may be performed by at least one of a trained machine learning model or a natural language processing algorithm.

Abstract: A system for tracking adverse events may include at least one processing device programmed to receive a request from a user to record an adverse event experienced by a patient; receive a search term input by the user; identify, in an adverse event database and based on the search term, at least one database record for an adverse event, wherein the at least one database record includes an adverse event type and at least one characteristic; receive, via an input field, a rating of the at least one characteristic for the patient; generate an adverse event record based on the adverse event type and the rating; and store the adverse event record in an adverse event log.

Abstract: Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Abstract: A model-assisted system for extracting patient information. A processor may be programmed to access a database storing one or more medical records associated with a patient and determine, using a first machine learning model and based on unstructured information included in the one or more medical records, whether the patient is associated with a condition. The processor may further be programmed to identify a date associated with the patient and determine, using a second machine learning model and based on the unstructured information, whether the patient is associated with the condition relative to the date. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition relative to the date.

Abstract: Described herein are techniques of using machine learning to automatically extract clinical variable values for subjects from clinical record data. The techniques designate certain clinical variables as hybrid variables that can be assigned values by machine learning model prediction. The techniques process, using a machine learning model trained to predict a value of a hybrid variable, clinical record data associated with a subject to obtain a predicted hybrid variable value and an associated confidence score. The techniques set the value of the hybrid variable for the subject to the predicted hybrid variable value when the model prediction is of sufficiently high confidence.

Abstract: Methods and systems for interacting with and/or managing a clinical trial protocol via an electronic clinical trial protocol management system. The electronic clinical trial protocol management system capable of handling one or more treatment cycles within the clinical trial protocol, and having one or more occurrence programmatic elements and one or more treatment cycle programmatic elements. Each occurrence programmatic element includes a formulaic representation of scheduling of a clinical procedure within one or more treatment cycles. Procedure programmatic elements are related to one or more iterations of a treatment cycle programmatic element. Via such a relationship it is possible to determine a proper clinical procedure applicable to a subject at a position in a clinical trial using an occurrence programmatic element.

Abstract: Systems and methods are disclosed for monitoring models for bias. In one implementation, a system for automatically assessing a deployed model for selection of a cohort may include a processing device programmed to: apply the deployed model to data representing a first plurality of individuals, the data including at least one characteristic of the first plurality of individuals; based on the application, select a subset of the first plurality of individuals as a cohort; receive data representing a second plurality of individuals labeled as within the cohort, the data including the at least one characteristic of the second plurality of individuals; compare the selected subset and the second plurality of individuals along the at least one characteristic; and determine whether the comparison results in a difference between the selected subset and the second plurality of individuals greater than a threshold.

Abstract: A model-assisted system for determining a patient event date may include a processor. The processor may be programmed to access a database storing a medical record associated with a patient, the medical record comprising unstructured data; analyze the unstructured data to identify a plurality of snippets of information in the medical record associated with a patient event; determine a date associated with each of the plurality of snippets, identify a plurality of query periods associated with the patient event; and generate, for each of the query periods, a probability of whether the patient event occurred during the query period based on the plurality of snippets and the associated dates.

Abstract: A model-assisted system for determining probabilities associated with a patient attribute. The processor may be programmed to access a database storing an unstructured medical record associated with a patient and analyze the medical record to identify snippets of information associated with the patient attribute. The processor may generate, based on each snippet, a snippet vector comprising a plurality of snippet vector elements comprising weight values associated with at least one word included in the snippet. The processor may analyze the snippet vectors to generate a summary vector comprising a plurality of summary vector elements, wherein each of the plurality of summary vector elements is associated with a corresponding snippet vector element and is determined based on an analysis of the corresponding snippet vector element. The processor may further generate, based on the summary vector, at least one output indicative of a probability associated with the patient attribute.