Abstract: The present invention relates to computer-based clinical decision support tools including, computer-implemented methods, computer systems, and computer program products for clinical decision support. These tools assist the clinician in identifying epilepsy patients who are candidates for surgery and utilize a combination of natural language processing, corpus linguistics, and machine learning techniques.

Abstract: Described are methods for treating a patient with a therapeutic drug using a combination of genetic and non-genetic information to tailor the dose of the drug to the patient.

Abstract: The present invention relates to computer-based clinical decision support tools including, computer-implemented methods, computer systems, and computer program products for clinical decision support. These tools assist the clinician in identifying epilepsy patients who are candidates for surgery and utilize a combination of natural language processing, corpus linguistics, and machine learning techniques.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Abstract: The present invention relates to computer-based clinical decision support tools including, computer-implemented methods, computer systems, and computer program products for clinical decision support. These tools assist the clinician in identifying epilepsy patients who are candidates for surgery and utilize a combination of natural language processing, corpus linguistics, and machine learning techniques.

Abstract: The disclosure provides methods for assessing a neuropsychiatric condition of a human subject by combining the subject's biomarker data and thought marker data into a quantitative assessment of the subject's neuropsychiatric condition.

Abstract: A method for assessing a neuropsychiatric condition (such as, but not limited to, a risk that a subject may attempt to commit suicide or repeat an attempt to commit suicide, a risk that terminally ill patient is not being care-for or treated according to the patient's true wishes, a risk that a subject may perform or repeat a criminal act and/or a harmful act, a risk of the subject having a psychiatric illness, and/or a risk of a subject feigning a psychiatric illness) may include a plurality of steps. A step may include receiving biomarker data associated from an analysis of the subject's biological sample and a step of receiving thought-marker data obtained pertaining to one or more of the subject's recorded thoughts, spoken words, transcribed speech, and writings. A step may include generating a biomarker score associated with the neuropsychiatric condition from the biomarker data.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Abstract: Described are methods for treating a patient with a therapeutic drug using a combination of genetic and non-genetic information to tailor the dose of the drug to the patient.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Abstract: The present invention relates to computer-based clinical decision support tools including, computer-implemented methods, computer systems, and computer program products for clinical decision support. These tools assist the clinician in identifying epilepsy patients who are candidates for surgery and utilize a combination of natural language processing, corpus linguistics, and machine learning techniques.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Abstract: A method for performing natural language processing of free text using domain-specific spreading activation. Embodiments of the present invention ontologize free text using an algorithm based on neurocognitive theory by simulating human recognition, semantic, and episodic memory approaches. Embodiments of the invention may be used to process clinical text for assignment of billing codes, analyze suicide notes or legal discovery materials, and for processing other collections of text. Further, embodiments of the invention may be used to more effectively search large databases, such as a database containing a large number of medical publications.

Abstract: The invention provides systems and methods for producing a ranked list of drugs for administration to a patient in need thereof, the ranked list produced by calculating for each drug a score that is the product of a patient vector, a weighting vector, and the drug's column value in a disease matrix.

Abstract: The invention provides systems and methods for producing a ranked list of drugs for administration to a patient in need thereof, the ranked list produced by calculating for each drug a score that is the product of a patient vector, a weighting vector, and the drug's column value in a disease matrix.

Abstract: The invention provides population models, methods, and algorithms for targeting a dosing regimen or compound selection to an individual patient. The methods and algorithms of the invention utilize population models that incorporate genotype information for genes encoding drug metabolizing enzymes for one or more compounds of interest. The methods allow integration of genotype information for one or more genes encoding a drug metabolizing enzyme, particularly a cytochrome P450 gene with patient data. The methods allow integration of genotype information and the effect of one or more compounds on one or more drug metabolizing enzymes. The methods allow iterative feedback of drug metabolizing data obtained from a patient into the process of generating a dosage regimen recommendation for a compound of interest for an individual patient.

Abstract: A method for assessing a neuropsychiatric condition (such as, but not limited to, a risk that a subject may attempt to commit suicide or repeat an attempt to commit suicide, a risk that terminally ill patient is not being cared-for or treated according to the patient's true wishes, a risk that a subject may perform or repeat a criminal act and/or a harmful act, a risk of the subject having a psychiatric illness, and/or a risk of a subject feigning a psychiatric illness) may include a plurality of steps. A step may include receiving biomarker data associated from an analysis of the subject's biological sample and a step of receiving thought-marker data obtained pertaining to one or more of the subject's recorded thoughts, spoken words, transcribed speech, and writings. A step may include generating a biomarker score associated with the neuropsychiatric condition from the biomarker data.

Abstract: The invention provides population models, methods, and algorithms for targeting a dosing regimen or compound selection to an individual patient. The methods and algorithms of the invention utilize population models that incorporate genotype information for genes encoding drug metabolizing enzymes for one or more compounds of interest. The methods allow integration of genotype information for one or more genes encoding a drug metabolizing enzyme, particularly a cytochrome P450 gene with patient data. The methods allow integration of genotype information and the effect of one or more compounds on one or more drug metabolizing enzymes. The methods allow iterative feedback of drug metabolizing data obtained from a patient into the process of generating a dosage regimen recommendation for a compound of interest for an individual patient.