Abstract: Medical records may be analyzed to identify important items in the text of the medical record. Actionable content may be identified and may be emphasized or extracted from the medical record. Actionable content may be categorized into one or more categories. Identification may include processing using trained models that use contextual information and position information to determine sentence labels.

Abstract: A computer implemented method for managing medical information includes displaying a number of user interface elements within a graphical user interface, receiving medical information for a patient in a first user interface element of the user interface elements, processing the medical information to identify one or more semantic items, the one or more semantic items including a first semantic item, processing a medical record for the patient according to first semantic item, the processing including identifying a number of medical information items related to the first semantic item, and presenting at least some medical information items of the number of medical information items in a second user interface element configured to display medical information items related to the first semantic item.

Abstract: Electronic health records may be organized into problem-oriented medical records. Generating problem-oriented medical record may be based on problem and target relations. Problem and target relations may be determined from a knowledge base. An initial knowledge base may be determined from medical data sets and annotated problem and target relations. The initial knowledge base may be completed to establish new problem target relations using a trained model and/or site embeddings, data statistics, and combined embeddings. The completed knowledge base may be used in generation of problem-oriented medical records.

Abstract: Medical records may be analyzed to identify important items in the text of the medical record. Actionable content may be identified and may be emphasized or extracted from the medical record. Actionable content may be categorized into one or more categories. Identification may include processing using trained models that use contextual information and position information to determine sentence labels.

Abstract: Techniques for responding to a healthcare inquiry from a user are disclosed. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; identifying the intent as a need to obtain a dermatological diagnosis or treatment; soliciting, from the user and via the computing device, an image of the user's skin area to be diagnosed or treated; instantiating an image classification system to identify a dermatological disease from the image; and presenting one or more medical recommendations to the user based on the identified dermatological disease.

Abstract: Techniques for responding to a healthcare inquiry from a user are disclosed. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; identifying the intent as a need to obtain a dermatological diagnosis or treatment; soliciting, from the user and via the computing device, an image of the user's skin area to be diagnosed or treated; instantiating an image classification system to identify a dermatological disease from the image; and presenting one or more medical recommendations to the user based on the identified dermatological disease.

Abstract: In one embodiment, computer implemented method identifies a risk of developing a condition for a particular patient. First, an initial variable set is developed by utilizing one or more patient databases. Second, an enhanced model predictive of a selected condition is created using machine learning. With the enhanced model developed, patient features vectors are created from a patient health information database for the initial variable set. The enhanced model is applied to these patient feature vectors to predict development of the condition. Patients predicted to have the condition can be enrolled in an appropriate intervention program.

Abstract: A query processing system is described herein for personalizing results for a particular user. The query processing system operates by receiving a query from a particular user u who intends to find results that satisfy the query with respect to a topic Tu, the user being characterized by user information ?u. In one implementation, the query processing system then produces a generic topic distribution Prr(T|q) associated with the query that is germane to a population of generic users, as well as a user-specific query-dependent topic distribution Pr(Tu|q,?u) for the particular user. The query processing system then produces personalized results for the particular user based on Prr(T|q) and Pr(Tu|q,?u). The query processing system can use multiple techniques to produce Pr(Tu|q,?u), such as, in one approach, a discriminative learning approach.

Abstract: A query processing system is described herein for personalizing results for a particular user. The query processing system operates by receiving a query from a particular user u who intends to find results that satisfy the query with respect to a topic Tu, the user being characterized by user information ?u. In one implementation, the query processing system then produces a generic topic distribution Prr(T|q) associated with the query that is germane to a population of generic users, as well as a user-specific query-dependent topic distribution Pr(Tu|q,?u) for the particular user. The query processing system then produces personalized results for the particular user based on Prr(T|q) and Pr(Tu|q,?u). The query processing system can use multiple techniques to produce Pr(Tu|q,?u), such as, in one approach, a discriminative learning approach.