Abstract: There is provided a radiotherapy system, a data processing method and a storage medium. The radiotherapy system includes a calibration database, a training module and a data processing module. The calibration database can acquire a plurality of the sample data generated in a clinical therapy process. The training module can perform deep learning on the plurality of sample data stored in the calibration database to obtain a therapy algorithm model. The data processing module can process the detection data received in the clinical therapy process according to the therapy algorithm model to generate preliminary therapy data. As the therapy algorithm model is obtained by performing deep learning on a large amount of clinical data, the reliability of the therapy algorithm model is high. Accordingly, the accuracy of the preliminary therapy data generated by the therapy algorithm model is also high.

Abstract: A machine learning model is generated for drug efficacy prediction in treatment of genetic disease from a dataset correlating gene expression data for disease-cell samples with drug efficacy values for the samples. Bias weights are stored that correspond to respective genes in the samples. Each bias weight is dependent on predetermined relevance of the respective gene to drug efficacy. The model is generated by processing the dataset via a tree ensemble method wherein decision trees are grown with splits corresponding to respective genes in the samples. The gene for each split is chosen from a respective subset of the genes, and genes are selected for inclusion in this subset with respective probabilities dependent on the corresponding bias weights. The model is stored, and can be applied to gene expression data measured for a patient to obtain a personalized drug efficacy prediction for devising a personalized course of treatment.

Abstract: Systems and methods for treating a patient. The methods comprise: producing, by a radiotherapy system, a fused model for a superficial portion of the patient's skin by combining structural imaging data acquired using high frequency ultrasound and functional imaging data acquired using optical imaging; generating a treatment plan for the patient based on the fused model; storing the treatment plan; detecting, by a processor, an arrival of the patient at a medical facility using first information acquired from a first mobile device coupled to the patient; obtaining the treatment plan using the first information; and causing a state of medical equipment to be transitioned from a first configurable operational state in which a radiotherapy treatment head has a first position to a second configurable operational state in which the radiotherapy treatment head has a second different position, in accordance with the treatment plan.

Abstract: A device may obtain identification information concerning a clinical trial, and may obtain, based on the identification information, selection information concerning the clinical trial and input information concerning the clinical trial. The device may select at least one machine learning model, of a plurality of machine learning models, based on the selection information, and may process, using the at least one machine learning model, the input information to determine predicted rate of recruitment (RoR) information concerning the clinical trial.

Abstract: A method for checking the quality of data content in a structured clinical record is disclosed. The method may include the steps of providing a data quality test that checks the content of at least a portion of the data content in the structured clinical record, applying the data quality test to the portion of the data content, and returning the results of the data quality test.

Abstract: A system for generating a parasitic infection nutrition program including a computing device configured to receive at least a parasitic biomarker, generate a parasitic disease assessment referring to a first parasitic infection as a function of the at least a parasitic biomarker, determine a current position of the user, identify, using the current position and the parasitic disease assessment, a parasitic infection intervention, receive a geophysical indicator relating to the user, identify, using the geophysical indicator, a parasitic prevention strategy regarding a second parasitic infection, and generate a parasitic infection nutrition program, using the parasitic infection intervention and the parasitic prevention strategy.

Abstract: Systems and methods for utilizing machine learning to generate a trans-diagnostic classifier that is operative to concurrently diagnose a plurality of different mental health disorders using a single trans-diagnostic questionnaire that includes a plurality of questions (e.g., 17 questions). Machine learning techniques are used to process labeled training data to build statistical models that include trans-diagnostic item-level questions as features to create a screen to classify groups of subjects as either healthy or as possibly having a mental health disorder. A subset of questions are selected from the multiple self-administered mental health questionnaires and used to autonomously screen subjects across multiple mental health disorders without physician involvement, optionally remotely and repeatedly, in a short amount of time.

Abstract: Information about a medical provider's locations, brands, and gateways can be received (e.g., via an online portal). This information can be used to generate a data object that represents relationships between the locations, the brands, and the gateways. This data object can be maintained in a database base that is searchable by a user device. For example, a user of the user device may search the database to identify a medical provider where the user obtains care. Once identified, other systems can be used to connect the user device to a gateway associated with the medical provider in order to download electronic health record data associated with the user.

Abstract: A method comprises displaying, via an interactive interface, a medical scan and a plurality of prompts of each prompt decision tree of a plurality of prompt decision trees in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. Labeling data indicating the ultimately selected leaf node of each prompt decision tree is determined for the medical scan.

Abstract: A computer-implemented method for executing patient management workflows includes acquiring a pre-test dataset of clinically relevant information related to a patient and using a first intelligent agent to identify a diagnostic test for the patient based on the pre-test dataset. Following performance of the diagnostic test, a second intelligent agent is used to select a processing technique to be applied to data collected from the diagnostic test to obtain a diagnostic marker. Following application of the processing technique to the data collected from the diagnostic test, a third intelligent agent is used to generate an optimal patient management plan based on the pre-test dataset, the data collected from the diagnostic test, and the diagnostic marker.

Abstract: A system and method are provided for collection and testing of a biologic sample in a self-diagnostic test. The system and method comprise collecting by a user of a testing device a biologic sample for use with the testing device, assigning correlative values as test results, and receiving the test results at a server disposed on a network. Some aspects include a mobile application operating on a mobile device with which the user interacts. These aspects allow advertisements and other messages to be presented to the user through the mobile application. Some aspects present different messages to the user based on the type of self-diagnostic test the user is conducting.

Abstract: A system and method are provided for collection and testing of a biologic sample. The system and method comprise collecting by a user of a testing device a biologic sample for use with the testing device, assigning correlative values as test results, and receiving the test results at a server disposed on a network. Some aspects further include presenting advertisements and other messages to users through a mobile application operating on a mobile device. These aspects take into account the results of the self-diagnostic test and present different advertisements to the user based on the results of the test.

Abstract: A method of building a machine learning pipeline for predicting the efficacy of anti-epilepsy drug treatment regimens is provided.

Abstract: A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features and weighting the common features based on frequency of occurrence in the data set(s) to generate mutual information. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Abstract: A method, a system, and a computer program product are provided. A machine learning model is generated to process adverse event information and produce multiple corresponding medical codes associated with the adverse event information, wherein the multiple medical codes are semantically and hierarchically related in a medical taxonomy. The machine learning model includes multiple parallel output layers, each of which is associated with a corresponding medical code. The machine learning model is trained with training data elements, each of which includes adverse event information mapped to respective multiple medical codes, wherein results from each of the output layers adjusts the machine learning model. After completing the training, information pertaining to an adverse event is applied to the machine learning model to determine the corresponding multiple medical codes within the medical taxonomy.

Abstract: A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features and weighting the common features based on frequency of occurrence in the data set(s) to generate mutual information. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Abstract: A method, a computing system and a computer program product are provided. A model is generated and trained. The model is based on clinical data with outcomes from clinically-defined hierarchical metadata in a selected level of clinically-defined hierarchical metadata serving as an initial set of prediction targets. A score is determined for each of the prediction targets based on the generated model and the set of evaluation factors. The set of prediction targets, the generated model, and the scores for the set of prediction targets are updated until the updated scores for the updated set of prediction targets satisfy acceptance criteria. The updated generated model, using the updated set of prediction targets, is applied to predict one of a set of updated prediction targets of mutually exclusive outcome categories.

Abstract: A medical scan hierarchical labeling system stores labeling application data that includes application operational instructions and a plurality of prompt decision trees. A medical scan and the labeling application data are sent to a client device for storage. The client device executes the application operational instructions of the labeling application data, causing the client device to display, via an interactive interface, the medical scan and a plurality of prompts of each prompt decision tree in succession, beginning with automatically determined starting prompts of each prompt decision tree, in accordance with corresponding nodes of each prompt decision tree until a leaf node of each prompt decision tree is ultimately selected. The client device transmits labeling data indicating the ultimately selected leaf node of each prompt decision tree. A medical scan entry of the medical scan in a medical scan database is populated based on the set of labels.

Abstract: A method, computer program product, and system identifying a probability of a medical condition in a patient. The method includes a processor obtaining data set(s) related to a patient population diagnosed with a medical condition and based on a frequency of features in the data set(s), identifying common features. The processor generates pattern(s) including a portion of the common features to generate a machine learning algorithm(s). The processor compiles a training set of data to use to tune the machine learning algorithm(s). The processor dynamically adjusts common features in the pattern(s) such that the machine learning algorithm(s) can distinguish patient data indicating the medical condition from patient data not indicating the medical condition. The processor applies the machine learning algorithm(s) to data related to the undiagnosed patient, to determine the probability.

Abstract: Provided are mechanisms and processes for a lighting system for medical schedule management. According to various examples, an apparatus is provided which comprises a lighting interface configured to connect to a lighting element for illuminating a medical examination room. The apparatus further comprises a power interface coupled to a power source. The apparatus further comprises a transceiver configured to connect to a device corresponding to a physician. The duration of the connection is used to track the presence of the physician in the medical examination room. The transceiver is tuned to transmit a signal strength corresponding to the size and characteristics of the medical examination room. The apparatus is located in a lighting fixture in the medical examination room. The lighting fixture may be centrally located in the medical examination room.