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 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: A data provision method includes a data provision step in which electronic data are generated by at least one data generation device, and includes a provision step in which the electronic data are provided in the form of data packages via a data transmission network, wherein in the provision step the data packages are first of all, preferably directly, written into a blockchain or into a distributed ledger, and wherein in the provision step the data packages are provided, preferably directly, from the blockchain or from the distributed ledger via the data transmission network, wherein provided data packages include at least one encrypted and/or digitally signed data field with a reference to an, in particular random or quasi-random, subsequent address of the next data package following the data package.

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 method of automatic detection of a required peak for sample machining by a focused ion beam uses for a filtration of a measured signal of secondary particles of a discrete wavelet transformation followed by a peak detection, and stops sample machining after the required a number of peaks has been reached.

Abstract: An electrical connection device, in particular an electrical plug connector device, having at least one connection element which is configured for a physical connection to a data line, the electrical connection device further comprising a signal harvesting unit, in particular an RS-232 signal harvesting unit, which is configured to divert at least a portion of an electrical data signal emitted by the data line in order to obtain electrical energy, in particular at least to obtain electrical operating energy for signal-based data conversion and/or for radio-based signal transmission.

Abstract: A copy protection method for an electronic system has at least one electronic unit and at least one component which interacts with the electronic unit, in which at least one public-key infrastructure having at least one certification authority is used, wherein the certification authority issues at least one first certificate for the electronic unit and at least one second certificate for the at least one component, based on an identification feature of the electronic unit and of the component, respectively, and wherein, to check authenticity at least of the at least one component by means of the electronic unit, the respective certificates are at least mutually checked, wherein at least the first certificate is premade and loaded onto the electronic unit when producing and/or configuring the electronic unit, and/or at least the second certificate is premade and loaded onto the at least one component when producing and/or configuring the component.

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: 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: A method performs a planar and cross-sectional etching of semiconductor devices made up of multiple layers of dissimilar materials, like metals and dielectrics. The method results in the removal of multiple layers with the aim of either exposing a single layer of interest or cross-sectioning several layers to perform various applications including but not limited to, nanoprobing, circuit edit and failure analysis. The method comprises directing an ion beam toward a defined area on the semiconductor device in the presence of an etching agent, thereby removing at least a portion of the mixed metal and dielectric layer and producing a recess with at least one substantially smooth and planar surface in the milled area. The used etching agent comprises oxidizing and reducing elements in a ratio optimized such that the quantity of oxidizing elements is one element more than the quantity of reducing elements.

Abstract: A method of uniformly removing material from a sample surface includes the steps of sputtering by means of scanning the surface with a focused ion beam and a simultaneous observing of the sample during sputtering. Uniform sputtering of different materials is achieved by high-angle sputtering from multiple directions, wherein the directions are rotated relative to each other by a non-zero angle.

Abstract: The subject matter of the invention is a method of uniformly removing material from a sample surface by sputtering by means of scanning the surface with a focused ion beam and a simultaneous observing of the sample during sputtering. Uniform sputtering of different materials is achieved by high-angle sputtering from multiple directions, wherein the directions are rotated relative to each other by a non-zero angle.

Abstract: Example systems and methods of classifying web visitor sessions based on clickstreams are presented. In one example, a plurality of web pages of a website is organized into a plurality of web page categories. A clickstream of each visitor to visit the plurality of web page categories of the website are divided into a plurality of visitor sessions. A mathematical distance between each of the plurality of visitor sessions is determined using a visitation metric based on the web page categories. Each of the visitor sessions is classified into a target group or a non-target group based on the mathematical distance between each of the visitor sessions and on an identification of at least one of the visitor sessions with an event corresponding to the target group.

Abstract: Example systems and methods of classifying web visitor sessions based on clickstreams are presented. In one example, a plurality of web pages of a website is organized into a plurality of web page categories. A clickstream of each visitor to visit the plurality of web page categories of the website are divided into a plurality of visitor sessions. A mathematical distance between each of the plurality of visitor sessions is determined using a visitation metric based on the web page categories. Each of the visitor sessions is classified into a target group or a non-target group based on the mathematical distance between each of the visitor sessions and on an identification of at least one of the visitor sessions with an event corresponding to the target group.

Abstract: Selecting a service for a mobile computing device includes receiving a message from the mobile computing device via a wireless network, storing data from the message, querying the data to identify a characteristic, selecting a service from a plurality of services based on the identified characteristic, and sending the selected service to the mobile computing device.

Abstract: Selecting a service for a mobile computing device includes receiving a message from the mobile computing device via a wireless network, storing data from the message, querying the data to identify a characteristic, selecting a service from a plurality of services based on the identified characteristic, and sending the selected service to the mobile computing device