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 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 for control of a basic oxygen furnace includes creating a computer-implemented model of the basic oxygen furnace based on support vector regression (SVR) such that the model is configured to produce a prediction of first turndown quality of the basic oxygen furnace. Historical data associated the first turndown quality is received. The model is applied to the historical data to produce a prediction of first turndown quality, which is compared to an actual measurement of first turndown quality. The control recipe for the basic oxygen furnace based at least in part upon a result of the comparison. The system generally includes a preprocessor, a database configured to store historical data associated with first turndown quality of the basic oxygen furnace, a prediction module coupled to the database, and a run-to-run control module. A graphical user interface is provided for visualization and adjustment of data.

Abstract: A method for control of a basic oxygen furnace includes creating a computer-implemented model of the basic oxygen furnace based on support vector regression (SVR) such that the model is configured to produce a prediction of first turndown quality of the basic oxygen furnace. Historical data associated the first turndown quality is received. The model is applied to the historical data to produce a prediction of first turndown quality, which is compared to an actual measurement of first turndown quality. The control recipe for the basic oxygen furnace based at least in part upon a result of the comparison. The system generally includes a preprocessor, a database configured to store historical data associated with first turndown quality of the basic oxygen furnace, a prediction module coupled to the database, and a run-to-run control module. A graphical user interface is provided for visualization and adjustment of data.