The disclosed apparatus, systems and methods relate to predicting, screening, and monitoring for mortality and other negative patient outcomes. Systems and methods may include receiving one or more signals from one or more sensing devices; processing the one or more signals to extract one or more features from the one or more signals; analyzing the one or more features to determine one or more values for each of the one or more features; comparing at least one of the one or more values or a measure based on at least one of the one or more values to a threshold; determining a presence, absence, or likelihood of the subsequent mortality, falls or extended hospital stays for a patient based on the comparison; and outputting an indication of the presence, absence, or likelihood of the subsequent development of poor outcomes or death for the patient.

Methods enabling prediction, screening, early diagnosis, and recommended intervention or treatment selection of chronic medical conditions using artificial intelligence operating in conjunction with large medical datasets. Logic is applied to historic population data to extract medical features and identify subjects with diagnosed chronic conditions, and the pre-diagnosis medical data is used to train a diagnosis classification algorithm. A self-supervised learning mechanism is separately used to generate a feature embedding transformation of the patient's medical history into representational feature vectors. These patient feature vectors together with their expected diagnoses are used to train a multi-label classifier model using supervised learning. The embedding transformation and the multi-label classifier are then applied to a current subject's data to generate a patient diagnosis probability vector, predicting the existence of chronic conditions. These methods are applied to diagnose progressive, chronic disorders in many different physiological systems.

Systems and methods obtain functional connectivity data in the whole brain to detect and predict brain disorders. This whole brain data is regionalized and then manipulated to derive functional connectivity data sets that can be used to show measured functional connectivity changes. This whole brain data may also be analyzed to determine changes in functional activity in both increased and decreased neural network connectivity. By identifying and then quantifying the functional connectivity differences between healthy and diseased subjects, a classification for individual subjects can be made.

A wearable gait detection device, a walking ability improvement system and a wearable gait detection system detect manifestation in a brain based on the wearer's gait and preventive measures. A load measurement part measures a load of a sole of the wearer's feet, a foot movement detection part at the shoes detects acceleration and/or angular velocity during feet movement, a centroid position calculation part calculates a centroid position of the feet based on changes in measured load, a movement locus calculation part calculates a movement locus of the feet based on acceleration and/or angular velocity detected by the foot movement detection part, a manifestation recognition part recognizes manifestation in the brain according to a specificity of centroid fluctuation of the feet based on the calculated centroid position and movement locus of the feet, and a sensory output part feeds back a sensation to the wearer based on a recognition result.

Apparatuses, systems, methods, and computer program products are disclosed for medical assessment based on voice. A query module is configured to audibly question a user from a speaker of a mobile computing device with one or more open ended questions. A response module is configured to receive a conversational verbal response of a user from a microphone of a mobile computing device in response to one or more open ended questions. A detection module is configured to provide a machine learning assessment for a user of a medical condition based on a machine learning analysis of a received conversational verbal response of the user.

The present disclosure provides systems and methods for assessing a mental state of a subject in a single session or over multiple different sessions, using for example an automated module to present and/or formulate at least one query based in part on one or more target mental states to be assessed. The query may be configured to elicit at least one response from the subject. The query may be transmitted in an audio, visual, and/or textual format to the subject to elicit the response. Data comprising the response from the subject can be received. The data can be processed using one or more individual, joint, or fused models. One or more assessments of the mental state associated with the subject can be generated for the single session, for each of the multiple different sessions, or upon completion of one or more sessions of the multiple different sessions.

A method and system for generating a classifier to classify facial images for cognitive disorder in humans. The system comprises receiving a labeled dataset including set of facial images, wherein each of the facial image is labeled depending on whether it represents a cognitive disorder condition; extracting, from each facial image in the set of facial images, at least one learning facial feature indicative of a cognitive disorder; and feeding the extracted facial features into a to produce a machine learning trained model to generate a classifier, wherein the classifier is generated and ready when the trained model includes enough facial features processed by a machine learning model.

The present invention provides a method of computer analysis of a data set representing an image to extract a texture based measure therefrom, said image including a multiplicative bias in intensity within the image of unknown magnitude, the method comprising applying to said data set a bank of texture extracting filters, such that said filters are chosen from filters that are invariant to the presence of a multiplicative bias field. By providing such a method, rather than attempting to correct the bias field before extraction of a texture based-biomarker, a texture-based biomarker that is bias field invariant is extracted. This makes correction of the bias field unnecessary.

Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

A biological classification device and a method for Alzheimer's disease using a brain image are disclosed. The biological classification device includes an image receiving unit which receives a plurality of images obtained by capturing images of a brain of a subject; an image processing unit which acquires neurodegeneration feature related to the brain of the subject and standardized uptake value ratio (SUVR) information from the plurality of images; an image analysis unit which performs first determination of a presence or absence of cranial nerve abnormality based on the neurodegeneration feature(s) and second determination and third determination of a presence or absence of abnormality of beta amyloid protein and tau protein, respectively, based on the SUVR information; and a classifying unit which performs biological classification of the subject related to Alzheimer's disease using the first, the second, and the third determinations together.