Abstract: Systems and methods facilitate the planning and performance of hip and other surgeries. A computer model of a hip may be generated and displayed. A template of a hip component that replaces a native portion of the hip may be superimposed on the model. Changes in leg length, offset, or anterior-posterior (AP) position as well as a virtual distance between a landmark on the model and a location on the template may be determined. During surgery, a physical distance corresponding to the virtual distance may be obtained. The template may be moved relative to the model to match the physical distance, and new change values in leg length, offset, or anterior-posterior (AP) position may be determined. The new change values may be evaluated, and the surgery may proceed, or the process may be repeated using templates corresponding to alternative components.

Abstract: Techniques are disclosed relating to dynamically determining a data load process usable with a decision service for responding to requests to the decision service. The data load process may be selected from either a full data load process that loads data needed for all the rules in the decision service, or a selective data load process that loads data needed for a subset of the rules in the decision service according to embodiments. The decision service may determine the data load process to implement based on an evaluation of a decision blueprint in response to receiving the request, where the decision blueprint includes a set of rules interconnected by flow paths. Evaluation of properties of the rules and flow paths based on input data in the request may be used to determine the data load process to implement. Overall, this results in faster execution times in various circumstances.

Abstract: The disclosed systems and methods manage more notifications between medical devices. A method includes obtaining information corresponding to a clinician and determining whether the information satisfies coordination criteria. The method includes, in accordance with a determination that the clinician is associated with the first medical device and a second medical device, detecting a first and second notification generated by respective medical devices. The method includes identifying, based on detecting the respective notifications, alert information including medical information for a patient receiving medical treatment from the first and second medical device. The method includes determining, based on the identified information, notification priorities between the respective notifications.

Abstract: Techniques and apparatus for generating, performing, and evaluating virtual clinical trials that include clinic modules to represent real-world operational aspects of trial entities are described. In one embodiment, for example, an apparatus may include at least one memory, and logic coupled to the at least one memory. The logic may be configured to generate a plurality of avatars configured to model a health condition associated with a population of patients, and to perform a virtual clinical trial to simulate a course of treatment for the plurality of avatars, the virtual clinical trial to include at least one clinic module associated with at least one event and a probability for the at least one event, the at least one event to model an operational aspect of an entity of the virtual clinical trial. Other embodiments are described.

Abstract: A first data task station which has a first predetermined data content requirement. A data content receiver receives data content. A first activator enables the data content receiver through a data intake of data task station, to associate with a first data content. Movement of the first data content to an automator site of the first data task station, actuates the screener. A second activator of the computer, by operation of the automator, enables, after the first pre-determined data content satisfies the screen against said first pre-determined data content requirement, a second data task station associated with a second predetermined data content requirement. A disabler, by operation of the automator, after the first pre-determined data content satisfies the screen against the first pre-determined data content requirement, disables the further screen of the first data content of said first data station.

Abstract: Methods and systems for improving patient medication adherence by determining when and/or how to intervene with a patient regarding medication adherence are disclosed. An example computer-implemented method for estimating an adherence risk score for a medication taken by a patient includes: receiving, via a network interface from a server configured for determining follow up with patients regarding medication adherence, a request for an adherence risk score for a patient and medication combination, the adherence risk score representing a risk that the patient will not be adherent with taking the medication as prescribed during a time period; obtaining, with one or more processors, pharmacy records for the patient and medication combination; processing the pharmacy records with a machine learning model to determine the adherence risk score; and providing, via the network interface, the adherence risk score to the server.

Abstract: Embodiments described herein provide for recommending radiotherapy treatment attributes. A machine learning model predicts the preference of a medical professional and provides relevant suggestions (or recommendations) of radiotherapy treatment attributes for various categories of radiotherapy treatment. Specifically, the machine learning model predicts field geometry attributes from various field geometry attribute options for various field geometry attribute categories. The machine learning model is conditioned on patient data such as medical images and patient information. The machine learning model is trained in response to cumulative reward information associated with a medical professional accepting the provided/displayed recommendations.

Abstract: An apparatus for generating a user report is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a user profile from a user. The memory instructs the processor to generate a first set of inquiries as a function of the user profile using an inquiry machine learning model. The memory instructs the processor to receive a first set of inquiry responses from the user as a function of the first set of inquiries. The memory instructs the processor to generate a user report as a function of the first set of inquiries and the first set of inquiry responses. The memory instructs the processor to display the user report using a display device.

Abstract: Methods and systems for monitoring of sensor data for processing by machine-learning models to generate event predictions to estimate a risk a medical event are provided. An electronic device or wearable smart device may monitor the output of various sensors to collect data related to a person's activity level, location changes, and communications and may use this information as input to a personalized trained machine-learning model to predict a likelihood of an event.

Abstract: A method of interpreting tabular data includes receiving, at a deep tabular data learning network (TabNet) executing on data processing hardware, a set of features. For each of multiple sequential processing steps, the method also includes: selecting, using a sparse mask of the TabNet, a subset of relevant features of the set of features; processing using a feature transformer of the TabNet, the subset of relevant features to generate a decision step output and information for a next processing step in the multiple sequential processing steps; and providing the information to the next processing step. The method also includes determining a final decision output by aggregating the decision step outputs generated for the multiple sequential processing steps.

Abstract: Technologies and techniques for processing drug data in a specialized medical computer system. Autonomous moving code may be received via a communications interface from a computer network, where the autonomous movable code includes patient data, lab test data, demographic data, and diagnosis data. The autonomous movable code is executed in a system agent manager and processed in a learning module, where the learning module performs predictive processing to determine if an opioid abuse condition exists. New autonomous movable code may be generated that includes information and/or instructions regarding the opioid abuse condition. The new autonomous movable code is then transmitted back to the computer network.

Abstract: A method, system, and computer program product to prevent misinterpretation of communications via artificial intelligence assistants. The method includes monitoring an artificial intelligence enabled communication. The method also includes analyzing messages, participants, and actions within the communication. The method also includes identifying critical data within the communication. The method also includes determining critical sequence data, where the critical sequence data is a sequence of conversation elements that contains the critical data. The method also includes predicting an uncertainty level for the critical sequence data, where the uncertainty level indicates an amount of uncertainty that the artificial intelligence correctly interprets the critical sequence data. The method also includes determining that the uncertainty level for the critical sequence data is above a threshold uncertainty value, resulting in critical sequence data that is subject to misinterpretation.

Abstract: A computer-implemented method is for providing a clinical information. In an embodiment, the computer-implemented method includes receiving input data including a graph representation of a plurality of disease lesions of a patient; applying a trained function to the input data to generate the clinical information, the trained function being based on a graph machine learning model; and providing the clinical information, the clinical information including at least one information for prediction of the disease progression, survival or therapy response of the patient.

Abstract: A system of generating a pre-operative assessment of a patient includes an assessment system configured to communicate with client electronic devices. The system receives patient information pertaining to a patient, uses the patient information to access one or more medical images associated with the patient, performs one or more image processing techniques on the one or more medical images to identify measurements pertaining to an internal bodily structure of the patient, identifies one or more implant components for the patient, determines whether the one or more implant components are currently stocked, in response to determining that the one or more implant components are not currently stocked, automatically generates and placing an order for the one or more implant components that are not stocked, generates a pre-operative assessment for the patient, and causes the assessment to be displayed on a display device of a client electronic device.

Abstract: A system of machine learning (“ML”) models for making actionable predictions regarding low-incidence events, including a generative ML model that produces synthetic minority-class records to form an augmented training data set, a predictive ML model that has been trained on the augmented training data set, a certainty ML model that produces a certainty estimate, and an explanatory model that produces an explanation. A method for producing actionable predictions of a low-incidence event by applying ML models to imbalanced class data by producing a prediction by a predictive ML model that has been trained on a data set comprising synthetic minority-class data records produced by a generative ML model, and producing a certainty estimate and an explanation. At least one of the certainty estimate or explanation determines an effective or appropriate response to the prediction. The low-incidence event may comprise risk of opioid use disorder.

Abstract: A distributed system includes one or more servers configured to provide computing devices access to portals hosted by the one or more servers, where the portals include a patient intake portal accessible by a first computing device located at an office of an intake dentist or orthodontist, and an approving portal accessible by a second computing device remote from the first computing device. The distributed system includes a dental aligner fabrication system including a fabrication computing device and dental aligner fabrication equipment. The patient intake portal is configured to provide a plurality of status updates to the intake dentist or orthodontist regarding a status of the patient, where the plurality of status updates include an indication that the dental aligners have been sent to the patient.

Abstract: Apparatus and associated methods relate to a health record management system having a processing engine configured to collect one or more health records associated with a patient from one or more healthcare providers and collect one or more personal observations identified by the patient, and integrate those collected information to form a comprehensively integrated health record associated with the patient. In an illustrative example, the patient may selectively share all or part of the comprehensively integrated health record with one or more persons or assigned healthcare providers.

Abstract: Described herein are database systems including one or more remote analytical instruments operably connected to one or more servers. The instruments can transmit rich data to the servers, and the one or more servers can compile a database of the rich data. One or more processors associated with the servers can be configured to execute a data analytics program on the database to identify a stochastic phenomenon or to process the data and present in real-time at a location of the one or more instruments comparison information about the instruments.

Abstract: A method for providing a lung disease risk measure in a Computer Aided Diagnosis system is described. The method comprising the steps of: receiving a plurality of inputs for a subject, each input comprising at least one image showing all or part of the lungs of a patient and a time stamp for the image, where the inputs are obtained at varying intervals; analysing the inputs to assess temporal changes in the images using at least one of an input data encoder and a time stamp encoder; inputting the output of at least one of the encoders to a score calculator to calculate a risk score; outputting the risk score indicating the lung disease risk for the subject. A Computer Aided diagnosis system for implementing the method is also described.

Abstract: A method, computer program product, and system are disclosed. The method, when implemented in a computer system, includes obtaining patient information (where the patient information comprises symptom information), and identifying a medication. The medication is identified by searching a medication information database using the symptom information.