Abstract: Proposed are concepts for predicting the likelihood of a brain injury of a subject resulting from a fall event experienced by the subject. Such concepts may therefore be useful for determining a need for further medical assessment or treatment of a fall by the subject. According to an exemplary concept, data from one or more sensors is used to determine information about a fall of a subject. Such information is then used in conjunction with a prediction algorithm to derive a probability of a brain injury.

Abstract: According to an aspect, there is provided an apparatus (500; FIG. 5) for determining an action to be taken in response to an emergency call, the apparatus comprising: a processor (502; FIG. 5) configured to receive an indication that an emergency call has been initiated; extract, from speech transmitted as part of the emergency call, features indicative of a medical condition or event relating to a subject; obtain medical data relating to the subject; predict, based on the extracted features and the medical data, an acuity level of the subject; determine, based on the acuity level, an action to be taken in respect of the subject; and provide an indication of the determined action for presentation to a recipient.

Abstract: A risk prediction system for predicting a risk of a negative event occurring for a monitored subject. The system comprises an input interface configured to obtain case data containing a history of events for the monitored subject and a predictive model based on deep learning networks. The predictive model comprises embedding layers for embedding the case data to generate embedded data, one or more sequence learning layers configured to learn patterns from sequences of the embedded data and generate sequenced data for each layer and one or more output layers each configured to map the sequenced data of each layer to a risk prediction for a negative event occurring.

Abstract: According to an aspect, there is provided an apparatus (500; FIG. 5) for determining an action to be taken in response to an emergency call, the apparatus comprising: a processor (502; FIG. 5) configured to receive an indication that an emergency call has been initiated; extract, from speech transmitted as part of the emergency call, features indicative of a medical condition or event relating to a subject; obtain medical data relating to the subject; predict, based on the extracted features and the medical data, an acuity level of the subject; determine, based on the acuity level, an action to be taken in respect of the subject; and provide an indication of the determined action for presentation to a recipient.

Abstract: According to an aspect, there is provided a computer implemented method of displaying to a user a risk score associated with a risk of a patient requiring a medical intervention. The method comprises obtaining the risk score for the patient; determining a format in which to display the risk score to the user based on a numerical literacy of the user; and sending an instruction to a user display to instruct the user display to display the risk score to the user in the determined format.

Abstract: Proposed are concepts for predicting the likelihood of a brain injury of a subject resulting from a fall event experienced by the subject. Such concepts may therefore be useful for determining a need for further medical assessment or treatment of a fall by the subject. According to an exemplary concept, data from one or more sensors is used to determine information about a fall of a subject. Such information is then used in conjunction with a prediction algorithm to derive a probability of a brain injury.

Abstract: A computer implemented method for detecting a fall of a user within an internal space of a building, the method comprising receiving a first air-pressure sensor signal indicative of change in air-pressure measured at a device worn by the user; receiving a measurement indicative of change in air-pressure within the internal space of the building; and determining the fall of the user based on the change in air-pressure measured at the device and the measurement indicative of change in air-pressure within the internal space of the building.

Abstract: The present disclosure relates to a conversation facilitation system and method configured for capturing and presenting life moment information for a subject with cognitive impairment. The system and method comprises obtaining life moment information from one or more life moment capturing devices. The life moment information comprises information on daily activities experienced by the subject, including locations of daily activities and/or co-participants in the daily activities. The system and method comprises storing the life moment information for later review by the subject and/or a caregiver of the subject. The system and method comprises obtaining external data relating to the life moment information from one or more external data sources. The system and method comprises utilizing the life moment information and the external data to generate a life moment timeline.

Abstract: A computing system (204) includes a memory device (208) configured to store missing outcome instructions (218) and a processor (206) configured to execute the missing outcome instructions. The instructions cause the processor to: evaluate records stored on a storage device (114) of a call center (106) of a system (102), wherein the storage device is configured to store electronic records of calls from a user site (104) to the call center, and each record includes at least a situation attribute field and an outcome attribute field, identify records of the stored records which include, at least one of, a value in the situation attribute or case note text in a case note database, but no value in the outcome attribute, and impute an outcome to the outcome attribute field of each identified record using at least one of the situation attribute and the case note text to predict the missing outcome.

Abstract: In a Personal Emergency Response System (PERS) service, activation of a wearable call device by a PERS client causes a speakerphone console to connect with a PERS call center to enable the PERS client to converse with a call center operator while a PERS client profile is retrieved from a PERS database and displayed on a display component at the PERS call center. From the profile, values of a set of features are generated for the PERS client. An emergency dispatch risk prediction or other risk prediction is computed over a future time horizon for the PERS client based on the generated values of the set of features, and is displayed on the display component. The risk prediction may be wirelessly sent to a mobile device for display on the mobile device.

Abstract: A computer implemented method for detecting a fall of a user within an internal space of a building, the method comprising receiving a first air-pressure sensor signal indicative of change in air-pressure measured at a device worn by the user; receiving a measurement indicative of change in air-pressure within the internal space of the building; and determining the fall of the user based on the change in air-pressure measured at the device and the measurement indicative of change in air-pressure within the internal space of the building.

Abstract: The present disclosure pertains to a system for providing prediction models for predicting a health determinant category contribution in savings generated by a clinical program. In some embodiments, the system obtains healthcare data including (i) historical and financial data corresponding to one or more clinical programs, (ii) demographic, clinical, and behavioral data of one or more patients, and (iii) environmental factors associated with the one or more clinical programs; defines one or more health determinant categories; generates prediction models related to a contribution of one or more constituents of the one or more health determinant categories to savings generated by the one or more clinical programs; generates one or more predictions related to a contribution of the one or more health determinant categories to the savings generated by the one or more clinical programs; and effectuates, via a user interface, presentation of the one or more predictions.

Abstract: A threshold value or range of values for results of a test used to assign patients to a particular level of treatment for a clinical condition is determined based on historical information on a plurality of patients having the clinical condition. The historical information may include values for the test performed on the patients, information on the treatment level for the clinical condition provided to the patients, information on the outcome of the clinical condition for the patients, and information on the cost associated with providing each of the treatment levels to the patients. The threshold value or range of values for results of the test is determined from the historical information, with the threshold value or range of values indicating the most cost effective treatment level for a given test result.

Abstract: In some embodiments, a patient dataset including digital medical images and other patient data may be obtained. The other patient data may include specific patient health data associated with a patient and historical patient data derived from a population related to the patient. The historical patient data may indicate medical inventions provided to patients of the related population, health effects of the medical interventions, and costs of the medical interventions. In some embodiments, a neural network specific to the patient may be configured for a user application using at least part of the patient dataset. As an example, the user application may include neural network. Based on the specific patient health data, health effects and intervention costs related to individual interventions for the patient may be predict via the neural network of the user application.

Abstract: The present system is configured to generate an ensemble prediction model to provide a care services combination for a user. The ensemble prediction model is configured to predict the effectiveness of individual care services for users. The ensemble prediction model accounts for effects of feature combinations on outcomes for the users. The present system is configured such that output from the ensemble prediction model is used during a single agent search to determine optimal combinations of services that minimize the risk of emergency re-hospitalization and/or other negative patient outcomes.

Abstract: There is provided a computer-implemented method and apparatus for determining a likelihood of occurrence of a medical event for a subject. A risk profile for the subject is acquired and a plurality of risk profiles for other subjects are obtained from a database. The acquired subject risk profile is compared to the obtained plurality of other subject risk profiles. At least one risk profile is selected from the obtained plurality of other subject risk profiles that most closely matches the acquired subject risk profile. The likelihood of occurrence of a medical event for the subject is determined based on the selected at least one risk profile. A signal indicative of the determined likelihood of occurrence of the medical event for the subject is output.

Abstract: In a transient ischemic attack (TIA) alerting device, a motion sensor (24) is configured to attach to or be worn by a subject. A wearable electronic data processing device (10) includes a speaker (14), a microphone (16), a radio transmitter (26), and an electronic processor (40). The processor is programmed to perform a TIA alerting method including: detecting a TIA-symptomatic gait type by processing subject motion data acquired by the motion sensor; responsive to detecting the TIA-symptomatic gait type, executing a TIA assessment questionnaire (70) by outputting questions using the speaker and receiving spoken responses using the microphone; determining a TIA assessment based at least on the spoken responses to the questions of the TIA assessment questionnaire; and conditional on the TIA assessment indicating a possible TIA, outputting a TIA alert radio signal using the radio transmitter.

Abstract: In a Personal Emergency Response System (PERS) service, activation of a wearable call device by a PERS client causes a speakerphone console to connect with a PERS call center to enable the PERS client to converse with a call center operator while a PERS client profile is retrieved from a PERS database and displayed on a display component at the PERS call center. From the profile, values of a set of features are generated for the PERS client. An emergency dispatch risk prediction or other risk prediction is computed over a future time horizon for the PERS client based on the generated values of the set of features, and is displayed on the display component. The risk prediction may be wirelessly sent to a mobile device for display on the mobile device.

Abstract: In a Personal Emergency Response System (PERS) service, activation of a wearable call device (10) by a PERS client causes a speakerphone console (12) to connect with a PERS call center (18) to enable the PERS client to converse with a call center operator while a PERS client profile is retrieved from a PERS database (52) and displayed on a display component (44) at the PERS call center. From the profile, values of a set of features are generated for the PERS client. An emergency dispatch risk prediction or other risk prediction is computed over a future time horizon for the PERS client based on the generated values of the set of features, and is displayed on the display component. The risk prediction may be wirelessly sent to a mobile device (80) for display on the mobile device.

Abstract: There is provided a computer-implemented method and apparatus for determining a likelihood of occurrence of a medical event for a subject. A risk profile for the subject is acquired and a plurality of risk profiles for other subjects are obtained from a database. The acquired subject risk profile is compared to the obtained plurality of other subject risk profiles. At least one risk profile is selected from the obtained plurality of other subject risk profiles that most closely matches the acquired subject risk profile. The likelihood of occurrence of a medical event for the subject is determined based on the selected at least one risk profile. A signal indicative of the determined likelihood of occurrence of the medical event for the subject is output.