Abstract: A system for determining a prioritized instruction set for a user, the system comprising a computing device, wherein the computing device is configured to receive at least a physiological goal and provide a plurality of biological extraction data. Computing device may determine a user baseline profile using training data, wherein training data correlates biological extraction data and physiological goals to baseline profile elements, train a machine-learning model using the training data, and determine the user baseline profile as a function of the machine-learning model. Computing device may generate a differential action as a function of the user baseline profile and the physiological goal, receive a plurality of user preference data, and selecting the differential action from the plurality of candidate differential actions.

Abstract: Techniques for dynamic visualization of data are provided. A plurality of therapies is received, where each of the plurality of therapies is associated with a respective plurality of guidelines. A guideline tree is generated based on the plurality of therapies, where each leaf node in the guideline tree represents a respective therapy, and where each edge in the guideline tree represents a respective guideline. A visual depiction of the guideline tree is generated. Further, a first plurality of attributes associated with a first patient is received, and a first modified visual depiction of the guideline tree is generated based on the first plurality of attributes.

Abstract: A long short-term memory (LSTM) model-based disease prediction method and apparatus, a computer device, and a storage medium are provided. The method includes: obtaining first medical data of a target object and second medical data of an associated object; inputting the first medical data and the second medical data into a first LSTM network in the LSTM model, to obtain a hidden state vector sequence in the first LSTM network; inputting the hidden state vector sequence into a second LSTM network for operation, to obtain a disease prediction result; selecting a predicted disease with an incidence rate higher than a preset threshold, and recording the predicted disease as a designated disease, and obtaining, based on a preset disease association network, an associated disease directly connected to the designated disease; and outputting the disease prediction result and the associated disease, thereby improving the prediction accuracy.

Abstract: The present disclosure relates to methods and systems suitable for use in identifying and providing personalised medicine to a patient. In some aspects, systems and method generate a co-therapy regimen for a patient suffering from a disease or condition. An identification of a co-therapy suitable to treat the disease or condition is received. A desired patient endpoint and a patient position are received, wherein the patient position is defined relative to the desired patient endpoint. A dataset relating to the patient is stored. The dataset comprises one or more patient data based on patient-related measurements. The dataset, the patient position and the desired patient endpoint are processed to generate a regimen for the co-therapy. The regimen is stored in a database.

Abstract: A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

Abstract: A system for computer-aided escalation can include and/or interface with any or all of: a set of user interfaces (equivalently referred to herein as dashboards and/or hubs), a computing system, and a set of models. A method for computer-aided escalation includes any or all of: receiving a set of inputs; and processing the set of inputs to determine a set of outputs; triggering an action based on the set of outputs; and/or any other processes.

Abstract: One embodiment provides a method, including: receiving, from a healthcare enterprise system, a notification indicating a need for at least one medical resource during a predetermined time frame at a predetermined facility within the healthcare enterprise system; determining that the indicated need is unavailable during at least one of: the predetermined time frame and the predetermined facility; identifying, based upon the indicated need being unavailable, at least one action to be performed to supply the at least one medical resource; and performing the at least one action. Other aspects are described and claimed.

Abstract: A system is provided to monitor, over time, one or more physical variables related to a severity or progression of a movement disorder and/or of symptoms thereof. The monitored physical variables can include speech sounds; keyboard outputs; or accelerations, rotations, or other properties of the motion of one or more body parts. The system operates to detect, based on the monitored one or more physical variables, potential changes in the degree and/or character of the movement disorder symptoms. In response to detecting such a potential change, the system provides the user with one or more tasks that the user can perform. The system detects one or more properties of the user's performance and, based on that detection, determines the severity or progression of the movement disorder. The tasks can include stepping, turning, standing, sitting, reaching, typing, pointing, manipulating an object, speaking into a microphone, or other tasks.

Abstract: A medical information processing apparatus according to an embodiment includes a processing circuitry. The processing circuitry is configured: to evaluate each of a plurality of machine learning models on a basis of an output result from each of the machine learning models obtained by inputting mutually the same medical data to the machine learning models and success/failure judgment results on the output results, the plurality of machine learning models being created from a plurality of pieces of medical data and either created from at least partially mutually-different medical data or created under mutually-different parameter conditions; and to cause results of the evaluation on the machine learning models to be displayed in such a manner that comparison is possible.

Abstract: Systems and methods that analyze blood-based cancer diagnostic tests using multiple classes of molecules are described. The system uses machine learning (ML) to analyze multiple analytes, for example cell-free DNA, cell-free microRNA, and circulating proteins, from a biological sample. The system can use multiple assays, e.g., whole-genome sequencing, whole-genome bisulfite sequencing or EM-seq, small-RNA sequencing, and quantitative immunoassay. This can increase the sensitivity and specificity of diagnostics by exploiting independent information between signals. During operation, the system receives a biological sample, and separates a plurality of molecule classes from the sample. For a plurality of assays, the system identifies feature sets to input to a machine learning model. The system performs an assay on each molecule class and forms a feature vector from the measured values.

Abstract: Methods, systems, and devices are disclosed for an efficient hardware architecture to implement gradient boosted trees for detecting biological conditions. For example, a method of detecting a biological condition includes receiving, by a device, a plurality of physiological signals from a plurality of input channels of the device, selecting, based on a trained prediction model, one or more input channels from the plurality of input channels, converting the one or more physiological signals received from the one or more input channels to one or more digital physiological signals, identifying, by using the plurality of gradient boosted decision trees, the selected characteristic in the one or more digital physiological signals, and determining a presence of a physiological condition based on an addition of the output values obtained from the plurality of gradient boosted decision trees.

Abstract: An example method includes identifying training data indicating features of a sample population and clinical outcomes of the sample population. The clinical outcomes are associated with a heterogeneous condition. The method further includes generating decision trees in a Random Forest (RF) based on the training data, each one of the decision trees being configured to divide the sample population into multiple categories based on the features of the sample population. In response to generating the decision trees, a proximity matrix comprising multiple entries is generated using the RF. One of the entries indicates a proportion of the decision trees that categorize a first individual among the sample population and a second individual among the sample population into the same categories among the multiple categories. The method further includes identifying subgroups of the heterogeneous condition by detecting communities of the proximity matrix.

Abstract: Systems and methods for intraocular lens selection include obtaining one or more pre-operative measurements of an eye; selecting, from a plurality of historical IOL implantation records, a subset of historical IOL implantation records for evaluating a first plurality of prediction model candidates; evaluating the first plurality of prediction model candidates; selecting a first prediction model from the first plurality of prediction model candidates based on the evaluating; calculating, using the selected first prediction model, a plurality of estimated post-operative MRSE values based on a set of IOL powers and the one or more pre-operative measurements of the eye; determining a first IOL power corresponding to a first estimated post-operative MRSE value that matches a predetermined post-operative MRSE value; and providing the determined first IOL power to a user to aid in selection of an IOL for implantation in the eye.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predicting the time to vaginal delivery of an infant. In one aspect, a method comprises: obtaining patient data characterizing a patient, comprising automatically querying a database storing one or more electronic medical records of the patient; generating a model input to a delivery time machine learning model based on the patient data characterizing the patient; processing the model input using the delivery time machine learning model, in accordance with values of a set of model parameters of the delivery time machine learning model, to generate a prediction for the time to vaginal delivery of the infant; and generating a notification that indicates the prediction for the time to vaginal delivery of the infant.

Abstract: In some instances, the disclosure provides a method for managing and updating contextual intelligent processes using artificial intelligence algorithms. The method comprises obtaining, from a user device, health information indicating a health triggering event associated with a user, obtaining event information associated with the user, determining, based on the health triggering event, one or more contextual intelligent processes for the health triggering event, retrieving one or more contextual artificial intelligence datasets, updating the one or more contextual intelligent processes with at least one new service based on inputting the health triggering event, the event information, and the one or more contextual intelligent processes into the one or more contextual artificial intelligence datasets, and performing the one or more updated contextual intelligent processes.

Abstract: An imaging and tracking device used for real-time health care inventory intelligence includes an image sensor and a processor. The image sensor captures images of inventory items stored within a furniture unit to which at least a portion of the imaging and tracking device is coupled. The processor processes the images captured using the image sensor to detect a physical retrieval of one of the inventory items from the furniture unit and to generate a signal including data associated with the retrieved inventory item. A software application running on a server device uses the signal to automatically update a database record associated with the retrieved inventory item within a database. Information associated with the updated database record is then transmitted to a client device in communication with the server device. The information may include instructions for rendering a graphical user interface of the software application at the client device.

Abstract: Systems and methods for managing medical information storage and transmission are discussed. A data management system may include a receiver circuit to receive information about a physiological event sensed from a patient, and an event prioritizer circuit to assign a priority to the received information. A control circuit may perform data reduction of the received information according to the assigned priority. Data reduction at a higher reduction rate is performed on the received information if a lower priority is assigned than if a higher priority is assigned. The system may include an output circuit to output the received information to a user or a process, or to transmit the received information to an external device, according to the assigned priority.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining data for a set of patients that each have a certain condition. A first and second sequence of data is determined based on the obtained data. A scoring model is generated by processing the first and second sequence of data to train a neural network. The scoring model determines a confidence that an individual has the particular healthcare condition. Patient scoring data is provided to the scoring model to determine the confidence that the individual has the healthcare condition. A confidence score is received as an output of the scoring model in response to providing the patient scoring data. The confidence score represents a determined confidence that the individual has the healthcare condition. An indication that represents the confidence that the individual has the healthcare condition is provided based on the received confidence score.

Abstract: There is a need for more effective and efficient predictive data analysis solutions for processing genetic sequencing data. This need can be addressed by, for example, techniques for performing predictive data analysis based on genetic sequences that utilize at least one of cross-variant polygenic risk modeling using genetic risk profiles, cross-variant polygenic risk modeling using functional genetic risk profiles, per-condition polygenic clustering operations, cross-condition polygenic predictive inferences, and cross-condition polygenic diagnoses.

Abstract: The present disclosure relates to providing personalized prediction and prevention of various types of medical events (e.g., emergency department visits, hospital admissions, complications) using machine-learning algorithms. An exemplary method comprises: obtaining a plurality of feature values of the patient; providing the plurality of feature values to a set of one or more trained machine-learning models to obtain: a first probabilistic value indicating a likelihood of a future medical event, a second probabilistic value indicating a likelihood of a reason for the future medical event, a third probabilistic value indicating a likelihood that the future medical event can be prevented, displaying, on the display, a risk value of the future medical event based on the first probabilistic value, a reason of the future medical event based on the second probabilistic value, an interceptability value of the future medical event based on the third probabilistic value.