Abstract: A method for generating classifiers for identifying neuropsychiatric disease includes acquiring functional neuroimaging data. The acquired functional neuroimaging data may be registered to an atlas of the brain. A discriminative mask is generated based on the registered functional neuroimaging data and the generated discriminative mask is applied to the registered functional neuroimaging data. One or more classifiers are generated for identifying neuropsychiatric disease based on the masked functional neuroimaging data. The accuracy of the generated classifiers may be verified. The generated classifiers may then be used to identify neuropsychiatric disease.

Abstract: A system and method for mild cognitive impairment (MCI) class discovery using gene expression data are provided. The method comprises: acquiring gene expression data of a patient having MCI; and identifying a putative MCI subtype based on an expression signature in the gene expression data, wherein the putative MCI subtype is identified by using a boosting tree.

Abstract: A method for diagnosing depression includes providing surface-enhanced laser desorption/ionization mass spectrometric (SELDI-MS) data of a plurality of proteins, said data obtained from a patient and comprising a plurality of peak values, and analyzing said peak values with an alternating decision tree comprising a set of tests of said data peaks values and associated prediction values, wherein said data is predictive of depression if a sum of the prediction values of said tree is greater than 1.0.

Abstract: A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.

Abstract: A system for providing medical decision support for diagnosis and treatment of disease comprises a medical knowledge database comprising medical information, the medical information including probabilities of disease outcomes for a disease of interest, a memory device for storing a program, a processor in communication with the memory device, the processor operative with the program to obtain patient information and in vitro test results for a patient, and automatically generate a recommendation for a medical test based on a combination of the patient information, the in vitro test results, and medical information from the medical knowledge database.

Abstract: A method for generating classifiers for identifying neuropsychiatric disease includes acquiring functional neuroimaging data. The acquired functional neuroimaging data may be registered to an atlas of the brain. A discriminative mask is generated based on the registered functional neuroimaging data and the generated discriminative mask is applied to the registered functional neuroimaging data. One or more classifiers are generated for identifying neuropsychiatric disease based on the masked functional neuroimaging data. The accuracy of the generated classifiers may be verified. The generated classifiers may then be used to identify neuropsychiatric disease.

Abstract: A method for diagnosing Amyotrophic lateral sclerosis includes providing surface-enhanced laser desorption/ionisation mass spectrometric (SELDI-MS) data of a plurality of proteins, said data obtained from a patient and comprising a plurality of peak values, and analysing said peak values with an alternating decision tree comprising a set of tests of said data peaks values and associated prediction values, wherein said data is predictive of depression if a sum of the prediction values of said tree is greater than 1.0.

Abstract: A system for providing medical decision support for diagnosis and treatment of disease comprises a medical knowledge database comprising medical information, the medical information including probabilities of disease outcomes for a disease of interest, a memory device for storing a program, a processor in communication with the memory device, the processor operative with the program to obtain patient information and in vitro test results for a patient, and automatically generate a recommendation for a medical test based on a combination of the patient information, the in vitro test results, and medical information from the medical knowledge database.

Abstract: A program storage device is provided readable by machine, tangibly embodying a program of instructions executable by the machine to perform method steps for classification of biological tissue by gene expression profiling. The method steps include providing a training set of gene expression profiles of known tissue samples, providing a first-layer strong classifier of the known tissue samples by combining weak classifiers using boosting, creating two sample sets based on the first classifier, populating the two sample sets with a next-layer of classifiers based on a previous-layer classifier, organizing the classifiers in a tree data structure, and outputting the tree data structure as a probabilistic boosting tree classifier for tissue sample classification and disease subtype discovery. A multi-class diagnosis problem is transformed to a two-class diagnosis process by finding an optimal feature and dividing the multi-class problem into two-classes.

Abstract: A system and method for using heterogeneous data from multiple healthcare information sources in a medical decision support system is disclosed. Each healthcare information system stores medical data using a different local schema. The medical decision support system provides responses to user queries. A query is received from a user that is generated in a standardized global schema. The query includes information from medical ontologies. Database queries are generated from the user queries that use the medical ontologies to generate constraints in the queries. The medical ontologies are also used to infer database queries. The generated query is translated into multiple queries for the multiple healthcare systems wherein each query is in the local schema of the healthcare information system that is being queried. Each database query is transmitted to one of the healthcare information systems based on the local schema of the particular query.

Abstract: A method for diagnosing Amyotrophic lateral sclerosis includes providing surface-enhanced laser desorption/ionisation mass spectrometric (SELDI-MS) data of a plurality of proteins, said data obtained from a patient and comprising a plurality of peak values, and analysing said peak values with an alternating decision tree comprising a set of tests of said data peaks values and associated prediction values, wherein said data is predictive of depression if a sum of the prediction values of said tree is greater than 1.0.

Abstract: A method for diagnosing depression includes providing surface-enhanced laser desorption/ionisation mass spectrometric (SELDI-MS) data of a plurality of proteins, said data obtained from a patient and comprising a plurality of peak values, and analysing said peak values with an alternating decision tree comprising a set of tests of said data peaks values and associated prediction values, wherein said data is predictive of depression if a sum of the prediction values of said tree is greater than 1.0.

Abstract: A system and method for providing personalized health care for Alzheimer's disease (AD) are provided. A method for providing personalized healthcare to a patient suspect of having or having AD, includes: receiving heterogeneous data of the patient; fusing the heterogeneous data by using one of an information fusion or machine learning technique; and providing one of a diagnosis, prognosis or treatment for the patient based on the fused heterogeneous data.

Abstract: A system and method for mild cognitive impairment (MCI) class discovery using gene expression data are provided. The method comprises: acquiring gene expression data of a patient having MCI; and identifying a putative MCI subtype based on an expression signature in the gene expression data, wherein the putative MCI subtype is identified by using a boosting tree.

Abstract: By inputting genetic and imaging information into a trained classification system, a second opinion diagnosis, a prognosis, clinical trial qualification or similar case information are output to assist a user. Consistent analysis may be provided using image based measurements in combination with pharmacogenomics studies. Fusing imaging, genetics and other information, such as clinical data, may more likely reduce the number of uncertainties in medical analysis, possibly improving patient safety, identifying new capabilities and supporting research.