Microsoft Technology Licensing, LLC

Michael Todd Gillam

Jonathan Alan Handler

Craig Frederick Feied

Renato Reder Cazangi

Bryan Dove

Uri Kartoun

Alexey P Kontsevoy

John Christopher Gillotte

Zhengyou Zhang

Cha Zhang

Yan Xu

Rajesh Hegde

David Fusari

Eric I-Chao Chang

Hao Fang Wu

Kuo Zhang

Yan Yu

Xiaopei Zhang

Michael J Bortnick

Yushun Wang

Dobroslav K Kolev

Kurt Arley Luke Thorne

Described is a technology by which medical patient facial images are acquired and maintained for associating with a patient's records and/or other items. A video camera may provide video frames, such as captured when a patient is being admitted to a hospital. Face detection may be employed to clip the facial part from the frame. Multiple images of a patient's face may be displayed on a user interface to allow selection of a representative image. Also described is obtaining the patient images by processing electronic documents (e.g., patient records) to look for a face pictured therein.

Automated acquisition of facial images

Some implementations provide a user interface for generating an electronic document. An external component may be launched within the user interface for accessing external data, and context data of the current document may be provided to the external component. The external data that corresponds to the context data for the document may be displayed within the user interface, such as for enabling a user to interact with the data and/or include a portion of the data in the document.

Generating and managing electronic documentation

A method may use a genetic algorithm to varying prediction parameters in forecasting software to obtain optimal predictions is disclosed. The method identifies parameters that can be varied and by modifying the parameters, the predictions of the forecasting software improve. The method uses sample data to train and validate the forecast and the optimal forecasting parameters are determined.

Medical data prediction method using genetic algorithms

The described concepts relate to automated patient disposition. One example can receive a clinician's disposition for a patient. This implementation can perform parameter-based cluster analysis on the patient and a set of patients to identify a sub-set of the patients with which the patient has a high similarity. This example can also cause a graphical user interface to be generated that conveys parameters from the sub-set of the patients and the patient.

Automated clustering for patient disposition

A de-identification system is described herein for converting original messages into de-identified messages. The de-identification system leverages original message-inception-functionality which operates as a gateway for providing original messages for use by a production environment. Namely, the de-identification system includes a transformation module that receives the original messages from the original message-inception functionality. The transformation module then converts instances of sensitive information contained in the original messages into non-sensitive information, to produce the de-identified messages. A de-identified environment can consume the de-identified messages with high confidence that the messages have been properly sanitized. This is because the de-identification work has been performed at a well-contained quarantine level of the message processing functionality.

Functionality for providing de-identified data

An information management system (IMS) is described herein that provides supplemental information to a user who is interacting with an application module. In one implementation, the IMS includes a collection system, an information extraction module, and a notification system. The collection system provides collected information from one or more information sources. The information extraction module extracts application information from the application module, even though the application module may lack a native interface for providing the application information. The analysis engine determines supplemental information to present to the user based on the collected information and the application information. And the notification information provides notification information which alerts the user to the existence of the supplemental information. In one scenario, the IMS can be applied in a healthcare-related domain.

Functionality for providing clinical decision support

The described implementations relate to patient identification. One implementation can acquire binary biometric data and structured data from a patient. This implementation can compare the acquired patient binary biometric data and structured data to binary biometric data and structured data associated with patient files in an electronic master patient index.

Patient identification

One or more techniques and/or systems are disclosed that provide for document retrieval where a user can identify key attributes of potential target documents that are desirable (e.g., have a particular semantic content for the user). Further, relevant documents that comprise the desired semantic content can be retrieved. Additionally, the user can provide feedback on the retrieved documents, for example, based on key semantic concepts found in the documents, and the input can be used to update the classification. For example, this process can be iterated to improve the retrieval and precision of documents found through machine learning techniques.

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Silver Spring, MD, United States of America

Michael Todd Gillam