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: An intrusion detection apparatus, including: a printed circuit board (PCB), including: an electrical switch, the electrical switch including: conductive pads positioned on a top layer of the PCB, and a conductive ground ring positioned on the top layer and surrounding the conductive pads; and a mechanical pin coupled to a first portion of a chassis of the information handling system at a first end of the mechanical pin, the mechanical pin including a conductive gasket positioned at a second end of the mechanical pin, the first end opposite to the second end, wherein the ground ring provides an intrusion signal when a foreign conductive object is in contact with the ground ring to indicate a presence of the foreign conductive object.

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: An online concierge system facilitates a checkout bypass process for shoppers. The online concierge system detects when the shopper procuring an order for a customer is ready to exit the warehouse after obtaining the items in the order. The online concierge system applies a trained risk model to automatically determining whether to initiate an audit of the shopper. Responsive to determining to initiate the audit, the online concierge system invokes an auditing process. The online concierge system receives, via an auditor application, verification of the order from the auditor mobile application. Responsive to the verification, the online concierge system completes the order and generates routing instructions via the shopper mobile application for facilitating delivery by the shopper to the customer.

Abstract: An intrusion detection apparatus, including: a printed circuit board (PCB), including: an electrical switch, the electrical switch including: conductive pads positioned on a top layer of the PCB, and a conductive ground ring positioned on the top layer and surrounding the conductive pads; and a mechanical pin coupled to a first portion of a chassis of the information handling system at a first end of the mechanical pin, the mechanical pin including a conductive gasket positioned at a second end of the mechanical pin, the first end opposite to the second end, wherein the ground ring provides an intrusion signal when a foreign conductive object is in contact with the ground ring to indicate a presence of the foreign conductive object.

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 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 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: Systems and methods are provided for retraining a machine learning model to learn a retrained machine learning model. After a computing system receives an execution log of a prediction service and an observed user action, it may create retraining data. From the retraining data, the computing system may learn a retrained machine learning model. The retrained machine learning model can be used to compute future predictions of user actions if it has an improved performance over the original machine learning model.