Abstract: The present disclosure relates to providing personalized prediction and prevention of various types of medical events (e.g., emergency department visits, hospital admissions, complications) using machine-learning algorithms. An exemplary method comprises: obtaining a plurality of feature values of the patient; providing the plurality of feature values to a set of one or more trained machine-learning models to obtain: a first probabilistic value indicating a likelihood of a future medical event, a second probabilistic value indicating a likelihood of a reason for the future medical event, a third probabilistic value indicating a likelihood that the future medical event can be prevented, displaying, on the display, a risk value of the future medical event based on the first probabilistic value, a reason of the future medical event based on the second probabilistic value, an interceptability value of the future medical event based on the third probabilistic value.

Abstract: The present invention provides methods and systems for patient-specific seizure onset detection. In one embodiment, at least one EEG waveform of the patient is recorded, and at least one epoch (sample) of the waveform is extracted. The waveform sample is decomposed into one or more subband signals via a wavelet decomposition of the waveform sample, and one or more feature vectors are computed based on the subband signals. A seizure onset can then be identified based on classification of the feature vectors to a seizure or a non-seizure class by comparing the feature vectors with a decision measure previously computed for that patient. The decision measure can be derived based on reference seizure and non-seizure EEG waveforms of the patient. In another aspect, similar methodology is employed for automatic detection of alpha waves. In other aspects, the invention provides diagnostic and imaging systems that incorporate the above seizure-onset and alpha-wave detection methodology.

Abstract: The present invention provides systems and methods for performing automated auscultation and diagnosis of conditions of the cardiovascular system. The invention acquires an acoustic signal emanating from the cardiovascular system via an sensor. In addition, in certain embodiments of the invention an electrical signal, e.g., an electrocardiogram (EKG) is simultaneously acquired. The signals are digitized, and, optionally, filtered to remove noise. The invention then processes and analyses the signal(s) so as to provide a clinically relevant conclusion or recommendation such as a diagnosis or suggested additional tests or therapy.