A method of, and system for, predicting conductive hearing loss risk in a person. The method includes utilizing, by using a processor, at least the following as inputs to a prediction model: (A) at least one first air conduction value for the person, and (B) any one of (a) a second air conduction value in noise for the person for in-phase binaural stimuli, or (b) a third air conduction value in noise for the person for antiphasic binaural stimuli. The method further includes predicting, by using the processor and an output of the prediction model, whether the person has a risk of conductive hearing loss. The method may be implemented without the need for bone conduction audiometry or any other clinical test to determine conductive hearing loss. The prediction model may be a logistic regression model.

In one embodiment, a health-monitoring system may access a waist-hip measurement of a user. The system may determine one or more stress-related parameters of the user using one or more computing devices. The system may determine one or more correlations between the waist-hip measurement and the one or more stress-related parameters of the user. The system may provide feedback to the user based on one or more of the one or more stress-related parameters or the determined correlations between the waist-hip measurement and the one or more stress-related parameters.

Methods and systems implement a pain assessment regularizing system to autonomously observe pained expressions and physiological measurements of a patient, in order to systematically collect data inputs which may be converted to pain assessment factors. The pain assessment regularizing system, by collecting this data, may combine it with clinical appraisals of pain intensity and patient self-reporting of pain intensity, weighing each factor appropriately in a manner sensitive to the progression of a patient care program, so as to lessen confounding effects of subjective pain assessment. The pain assessment regularizing system may generate a time series of regularized pain assessment factors, and further forecast a regularized pain assessment trend. A clinician may further operate the pain assessment regularizing system to review a visualization of both the time series and the forecast, providing the clinician with rigorously sampled and analytically predicted data which cannot be derived through manual and mental efforts.

A chatbot capable of empathic engagement with a user is disclosed. An identified trend in a user's mood or goals between a first time and a second time can be associated with open input (e.g., open text string input) from the user. As the user's mood or goals continue to be tracked, a subsequent trend can be identified that is the same as, similar to, different from, or opposite to the first identified trend. The user can then be automatically engaged based on the open input associated with the first identified trend. In an example, a user may input thoughts or reasons why they have been having a positively trending mood over a duration of time. The chatbot can then repeat or otherwise use those same thoughts or reasons to engage the user empathically when the chatbot detects that the user is experiencing a negatively trending mood.

A system has a plurality of diagnostic sensors and a first computing device interconnected to the plurality of diagnostic sensors for: commanding the plurality of diagnostic sensors to collect diagnostic data from a patient, determining if each of the plurality of diagnostic sensors are positioned at respectively predefined body locations of the patient, combining the collected diagnostic data for analysis when all of the plurality of diagnostic sensors are positioned at respectively predefined body locations of the patient, and prompting the patient to reposition at least one of the plurality of diagnostic sensors when the at least one sensor is determined as being mis-positioned from the corresponding predefined body location.

A first optical measurement of tissue with a first optical device is initiated. The first optical measurement includes a first shallow optical reading and a first deeper optical reading. A second optical measurement of the tissue with a second optical device spaced is initiated. The second optical device is spaced apart from the first optical device. The second optical measurement includes a second shallow optical reading and a second deeper optical reading. A first difference value between the first shallow optical reading and the first deeper optical reading is determined. A second difference value between the second shallow optical reading and the second deeper optical reading is determined. A large vessel occlusion (LVO) alert is generated when a ratio of the first difference value to the second difference value is larger than a threshold value.

A method includes the step of receiving electrocardiogram (ECG) data associated with a plurality of patients and an electrocardiogram configuration including a plurality of leads and a time interval. The electrocardiogram data includes, for each lead included in the plurality of leads, voltage data associated with at least a portion of the time interval. The method also includes training an artificial intelligence model on the ECG data, tuning the artificial intelligence model using data from a device having fewer leads than the plurality of leads, and evaluating the artificial intelligence model on additional data received from the ECG data.

The present disclosure is directed to methods of predicting overall survival, monitoring, and selecting treatments for a glioblastoma (GBM) patient. The method of the present disclosure includes obtaining at least one morphometric image from the GBM patient, identifying at least one radiomic biomarker based on the at least one morphometric image, and determining an overall survival value based on the at least one radiomic biomarker.

A system includes a wearable sensor device including an accelerometer configured to be worn by a person and to record sensor data during an activity performed by the person; an analysis element configured to receive the sensor data from the wearable sensor, determine sensor orientation data of the wearable sensor during the activity based on the sensor data, translate the sensor orientation data of the wearable sensor to person orientation data of the person during the activity, determine, for the person during the activity, (a) a lift rate, (b) a maximum sagittal flexion, (c) an average twist velocity, (d) a maximum moment, and (e) a maximum lateral velocity, and determine a score representative of an injury risk to the person during the activity based on such data; and a tangible feedback element configured to provide at least one tangible feedback based on the score so as to reduce the injury risk.

Certain aspects of the disclosure are directed to an apparatus including a scale and external circuitry. The scale includes a platform, and data-procurement circuitry for collecting signals indicative of the user's identity and cardio-physiological measurements. The scale includes processing circuitry to process data obtained by the data-procurement circuitry, therefrom generate cardio-related physiologic data, and to send user data to the external circuitry. The external circuitry identifies a risk that the user has a condition based on the reference information and the user data provided by the scale and outputs generic information correlating to the condition to the scale that is tailored based on the identified risk.