International Business Machines Corporation

Roslyn I Hickson

Annalisa Jean Swan

Olivia Jayne Smith

Timothy Michael Lynar

John Michael Wagner

Benjamin W Goudey

Shiwan Zhao

Jing Mei

Christine Schieber

Eryu Xia

Melanie E Roberts

Noel Garry Faux

Manoj Gambhir

Chia Yeow Khiang

Chathurika K H Hettiarachchige

Bowen Fung

Method and apparatus for predicting amyloid beta (A&#x3b2;) and phosphorylated tau (p-tau) biomarker levels in the cerebrospinal fluid (CSF) of patients. Embodiments include determining current values for a plurality of easily-measurable attributes of a first patient. Embodiments include analyzing data associated with a cohort of patients having known measurements of A&#x3b2; and p-tau biomarker levels, including determined values for the plurality of easily-measureable attributes. Embodiments include generating a predicted value for A&#x3b2; and/or p-tau biomarker levels for the first patient. Embodiments include generating a risk of the first patient developing AD at a future time, generating a probability of a patient's predicted rate of decline, and/or generating a probability of a patient's age at the onset of dementia, based on the predicted values for A&#x3b2; and/or p-tau biomarker levels.

Machine learning for amyloid and tau pathology prediction

A system, method and program product for a mechanistic model search engine. A system is disclosed having: a search interface for receiving input including text and functions relating to mechanistic models; a source interface for crawling online sources for mechanistic model information, wherein the information sources include text, tables, graphs, and functions; a context analysis system that determines parameter-based details from text information; a parameter processing system that derives parameter-based details from functions; and a search algorithm that compares parameter-based details determined from the input against parameter-based details determined from the information sources to identify mechanistic model search results.

Mechanistic mathematical model search engine

Systems, methods and program products for administering and modifying a simulation that incorporates the use of biometric data collection and computer-based predictive modeling techniques to reliably identify changes in the stress level of simulation participants while predictively adapting the simulation environment to manage each participants' stress levels within a pre-set or desired range. Each participant may have their biometric data collected and continuously monitored by wearing a computing device comprising biometric sensors, camera systems and audio recording devices. As the participants biometric data changes, the system monitors the participant for changes in stress level and maintains a desired stress level in the participant by modifying the adjustable properties of one or more virtual objects or computer-accessible real physical objects of the simulation environment to increase or decrease the stress level of the simulation on participants.

Dynamically adjustable training simulation

A computer-implemented method for predicting non-communicable diseases with infectious risk factors using artificial intelligence includes detecting one or more risk factors associated with a non-communicable disease based on a graph associated with person-to-person links, generating a data structure for compactly representing the graph to compute at least one person-to-person distance, and performing a machine learning technique with regularization of the at least one person-to-person distance.

Predicting non-communicable disease with infectious risk factors using artificial intelligence

A method of positioning an aquaculture pen includes retrieving data about the aquaculture pen disposed in a water environment, determining a forecasted location of the aquaculture pen, forecasting at least one factor of interest based on the data about the aquaculture pen for a time period of interest, predicting a factor of interest within a feasible range of movement for the aquaculture pen, determining a potential new location within the feasible range of movement using the data about the aquaculture pen, the feasible range of movement and the factor of interest within the feasible range of movement for the aquaculture pen, and moving the aquaculture pen to the potential new location.

Automated aquaculture pen location

Growing community of inventors

Fitzroy North, Australia

Roslyn I Hickson