Abstract: A medical device for assessing clinical patient deterioration in an in-patient hospital environment is provided. The medical device includes a processor and sensors that couple externally to a skin of the patient to acquire electrocardiogram (ECG) and other physiologic signals. The processor is configured to receive a medical history of the patient; generate physiologic data, including ECG data, over a period of time based on one or more physiologic signals; and execute a risk assessment process associated with a clinical condition of the patient. The risk assessment process analyzes the physiologic data and the medical history of the patient to generate a risk estimate of deterioration of the patient's clinical condition.

Abstract: Disclosed are a device, a computer-readable medium, and techniques that provide an onboarding process and an adaptivity process for a drug delivery device. A processor executing an onboarding process determines whether a history of delivered insulin to a user meets certain sufficiency requirements. The onboarding process enables a processor to cause the drug delivery device to administer doses of insulin to a user according to an initial total daily insulin dose calculation that is determined based on the sufficiency of the insulin delivery history. The initial total daily insulin may be adapted according to the adaptivity process as new insulin delivery is collected. The insulin delivery history, when sufficient, may be used to set total daily insulin dosages that enable automated insulin delivery upon replacement of a drug delivery device. The adaptivity process may be implemented to modify an initial insulin delivery doses to provide adapted insulin delivery doses.

Abstract: The present disclosure is generally directed to an apparatus for outputting at least a recipe to a user is described. The apparatus may include at least a processor, and a memory communicatively connected to the processor. The memory may contain instructions for configuring the at least a processor to receive user data from a user. The processor may be configured to classify the user data to a one or more phenotypic clusters and assign the user one or more cohort labels as a function of the one or more phenotypic clusters. Further, the processor may be configured to generate alimentary data as a function of the one or more phenotypic cluster and the one or more cohort labels. Moreover, the processor may be configured to output an alimentary program to the user.

Abstract: Some embodiments include a system and computer-implemented method for aggregating and tracking medical delivery to a patient including a non-transitory computer-readable medium in data communication with at least one processor, where the non-transitory computer-readable medium includes software instructions for a medical services tracking system and method. Upon execution of the software instructions, information from a patient database or server can be received and displayed a medical record dashboard. A user can view and edit access to the information, and a user selectable link can display medical record information. The system and method enable auto-population of medical data entry fields based at least one part on at least one claim made or billing signed off by a physician for at least one medical service or procedure previously provided to or performed on at least one patient.

Abstract: A surgical hub may obtain a hub connectivity mode based on a hub connectivity control parameter. For example, the hub connectivity mode may be selected from multiple connectivity modes that may be preconfigured, dynamically updated, semi-dynamically updated, periodically updated, or preset. The hub connectivity modes may control inter-device connectivity within a network associated with a hospital, and/or communication with an external network associated with a different hospital. The surgical hub may determine whether to provide instructional information to at least one smart surgical instrument based on the hub connectivity mode. On a condition that the hub connectivity mode does not support provisioning instructional information to surgical devices, provisioning instructional information to surgical devices may be disabled.

Abstract: A virtual health-care electronic commerce community includes a network to communicate information relating to the community; one or more patients coupled to the network; one or more treating professionals coupled to the network; and a server coupled to the network, the server storing data for each patient and performing patient data visualization in response to a user request.

Abstract: Systems and methods are described for utilizing machine learning techniques to analyze data associated with one or more dental practices to identify missed treatment opportunities, future treatment opportunities, or provider performance metrics. The treatment opportunities or performance metrics may be determined or identified based at least in part on a comparison of patient data, such as data stored in association with a dental office's practice management system, with the output of one or more machine learning models' processing of associated radiograph images of the dental office's patients. The one or more machine learning models may include models that identify, from image data of a radiograph, a dental condition depicted in the radiograph, which may be mapped by a computer system to a corresponding dental treatment recommended for the identified dental condition.

Abstract: A secure artificial intelligence (AI) enabled wearable medical sensor platform is used for adaptive operation according to features and techniques described herein. Operational parameters are modified based on data inputs thereto that provide feedback to the AI systems of the wearable sensor platform. The described technology can facilitate adaptive optimizations provided by AI machine learning algorithms in a manner that can beneficially assist in the monitoring and treatment of a patient. For example, the system described herein may be used for the continuous monitoring of the physiological parameters and health of a patient's vascular access point (for example, the fistula) and may provide, among other things, early warnings of possible infection at the vascular access location.

Abstract: An example method comprises: performing a first intraoral scan of an oral cavity, the oral cavity including a dentition of a patient; identifying a first digital representation of dental anatomy associated with the dentition of the patient; causing the first digital representation of dental anatomy to be stored at an external storage location; appending, to a current data block of a main blockchain, a reference to a first data block of an auxiliary blockchain; appending, to the first data block of the auxiliary blockchain, an identifier of the external storage location; appending, to the current data block of the main blockchain, a first data item of the first digital representation of dental anatomy; appending, to the current data block of the main blockchain, a reference to a preceding data block of the main blockchain; and broadcasting the current data block of the main blockchain to a plurality of blockchain nodes.

Abstract: A system for associating a patient with a device may include a memory and a processor. The memory may be configured to store a plurality of orders and a set of parameters, wherein each of the plurality of orders comprises order attributes and each of the plurality of orders identifies one of a plurality of patients. The processor may be configured to receive the plurality of orders from an order entry system, receive the set of parameters from a device, correlate the set of parameters with an order of the plurality of orders based on the order attributes of the plurality of orders, associate the device with the patient identified by the order that correlates with the set of parameters, and provide, to the device, an indication of the patient associated with the device.

Abstract: A method for determining at least one competency relationship to be used as a basis for selection of a dose-related recording parameter for an examination process, in particular the recording of a timed series of x-ray images as an x-ray image dataset, of a patient at an x-ray device, between at least one competency value describing a user-specific or user-group specific user competency in respect of the evaluation of recorded x-ray images and at least the at least one recording parameter. The method includes displaying a plurality of x-ray image datasets corresponding to different recording parameters and/or having different image contents described by image content parameters; recording, for each x-ray image dataset, at least one measured value describing the competency value based on an interaction of a user with an operating device assigned to a display device; and determining, based on the measured values, the competency relationship describing the recording parameter-dependent competency value.

Abstract: An example method of managing medical records using multi-dimensional cryptographically-secured datastores comprises: storing digital representations of dental anatomy utilizing a two-dimensional blockchain structure includes: causing, by a computer system, a digital representation of dental anatomy to be stored at an external storage location; appending, to a first data block of an auxiliary blockchain, an identifier of the external storage location; appending, to a current data block of a main blockchain, a reference to a first data block of the auxiliary blockchain; appending, to the current data block of the main blockchain, a data item of the digital representation of dental anatomy; appending, to the current data block of the main blockchain, a reference to a preceding data block of the main blockchain; and broadcasting the current data block of the main blockchain to a plurality of blockchain nodes.

Abstract: A method of generating a classifier includes a step of classifying each member of a development set of samples with a class label in a binary classification scheme with a first classifier; and generating a second classifier using a classifier development process with an input classifier development set being the members of the development set assigned one of the two class labels in the binary classification scheme by the first classifier. The second classifier stratifies the members of the set with an early label into two further sub-groups. We also describe identifying a plurality of different clinical sub-groups within the development set based on the clinical data and for each of the different clinical sub-groups, conducting a classifier generation process for each of the clinical sub-groups thereby generating clinical subgroup classifiers.

Abstract: A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

Abstract: A device, such as a data network device for a medical data network, controls an operating state of a second medical device in such a way that by sending a request message, the second medical device is prompted to change over into the operating state of the combined therapy and hence into an operating state in which its actuator is controlled as a function of an information signal of the first medical device. The information signal is based on physiological measured values. As a result, a clinician does not have to configure the second medical device himself/herself directly on site at the medical device by inputting an input signal, but this can be carried out by the device.

Abstract: A system and method to receive a request for a patient-specific surgical plan, create the requested plan, and provide it to a surgeon is disclosed. A Planning server may receive information regarding a patient, such as one or more files or objects, and an order from a surgeon for a surgical plan. A Plan Development client may include a surgical planning module that accesses the one or more files or objects, and creates a computer-generated, three-dimensional model of the patient anatomy. The planning module of the Plan Development client also may create an electronic surgical plan, and may transmit the plan to the Planning server. The ordering surgeon may be notified of the availability of the plan, and may access the plan stored at the Planning server through a surgeon client. The ordering surgeon may utilize the remotely generated surgical plan during the surgical procedure on the patient.

Abstract: A portable therapeutic platform for use in a therapeutic procedure including: (a) at least one compartment for holding at least one therapeutically effective item required to conduct the therapeutic procedure; and (b) at least one electronic communications device for receiving and transmitting information about an environment surrounding and including the platform. The use of the platform is conveniently managed by a system which transfers information about the environment surrounding and including the platform between the electronic communications device and a computer system including a processor which processes the information; and initiates a control response to the processed information. The platform and system are suitable for use to address a range of public health issues and as a countermeasure to biosecurity threats.

Abstract: Congestive heart failure (CHF) is a leading cause of death in the United States. WANDA is a wireless health project that leverages sensor technology and wireless communication to monitor the health status of patients with CHF. The first pilot study of WANDA showed the system's effectiveness for patients with CHF. However, WANDA experienced a considerable amount of missing data due to system misuse, nonuse, and failure. Missing data is highly undesirable as automated alarms may fail to notify healthcare professionals of potentially dangerous patient conditions. Embodiments of the present disclosure may utilize machine learning techniques including projection adjustment by contribution estimation regression (PACE), Bayesian methods, and voting feature interval (VFI) algorithms to predict both non-binomial and binomial data. The experimental results show that the aforementioned algorithms are superior to other methods with high accuracy and recall.

Abstract: A system and method based on the use of a mobile application running on a handheld mobile device that can be manipulated by a user/patient with the result that the data gathered by the application is communicated via a secure link to a computer service in order for evaluation of the user/patient's neurological and physical functioning for purposes of detecting changes in the progression of certain functions to aid in the diagnosis of certain diseases, such as multiple sclerosis and related neurological diseases.

Abstract: A system for modifying a nutrition requirement includes a computing device configured to obtain a user attribute, identify a nutrition requirement as a function of the user attribute, wherein identifying further comprises, receiving a nutrition training set, wherein the nutrition training set relates an aliment to a user attribute, and identifying the nutrition requirement as a function of a nutrition machine-learning process, receive a monitoring element from a monitoring device, modify the nutrition requirement, wherein modifying further comprises, receiving a modification training set, wherein the modification training set relates a monitoring element to a nutrition outcome, and modifying the nutrition requirement as a function of a modification machine-learning process, identify an aliment that fulfills the modified user nutrition requirement, and present an aliment on a display device.