There is provided a method for determining dementia risk factors by a server using deep learning. In this instance, the method for determining dementia risk factors includes acquiring biometric information from each subject corresponding to a first control group through a wearable device, acquiring measurement information for each subject corresponding to the first control group, deriving a first dementia risk factor based on the biometric information and the measurement information for each subject, and deriving a second dementia risk factor related to the first dementia risk factor via deep learning performed based on the biometric information related to the first dementia risk factor and control group information.

The invention provides a method for searching a database of ECG data. The method includes, obtaining a reference set of ECG data, wherein the reference set of ECG data comprises ECG data for a plurality of subjects comprising, for each of said subjects, data values for a plurality of characteristic ECG features, and grouping the reference set of ECG data into a plurality of subsets of ECG data, wherein each of the plurality of subsets of ECG data is associated with one of a plurality of characteristic ECG features. A search feature extraction algorithm is applied to the reference set of ECG data to extract at least one characteristic ECG feature as a search feature wherein a search feature is a characteristic ECG feature which enables ECG data of interest to be identified within the database of ECG data and a search criterion is generated based on the search feature. The database is then searched using the search criterion to obtain ECG data of interest.

A method (100) of generating and using one or more radiology analysis tools comprising: providing a labeling user interface (28, 40) on the workstation via which a user creates a labeled dataset by defining label types; receiving a user selection of desired output as at least one of the defined label types; identifying a proposed machine learning (ML) model based on the defined label types and the desired output; providing one or more GUI dialogs (40) presenting the proposed ML model and allowing the user to generate a user-designed proposed ML model (38) from the proposed ML model; training the user-designed ML model using training data comprising at least a portion of the labeled dataset, thereby generating a trained ML model (44); and deploying the trained ML model for an analysis process applied to at least a portion of radiology images and/or radiology reports in.

A method (100) of generating and using one or more radiology analysis tools comprising: providing a labeling user interface (28, 40) on the workstation via which a user creates a labeled dataset by defining label types; receiving a user selection of desired output as at least one of the defined label types; identifying a proposed machine learning (ML) model based on the defined label types and the desired output; providing one or more GUI dialogs (40) presenting the proposed ML model and allowing the user to generate a user-designed proposed ML model (38) from the proposed ML model; training the user-designed ML model using training data comprising at least a portion of the labeled dataset, thereby generating a trained ML model (44); and deploying the trained ML model for an analysis process applied to at least a portion of radiology images and/or radiology reports in.

Systems and methods of recording patient's medical documents and training programs for improving medical document recordation. The system includes a computer readable medium capable of storing medical data obtained from patients, including disease codes, a computer with software capable of evaluating the data stored on the computer readable medium for completeness, and a notification system capable of presenting to the user of the system a warning if any of the data is found to be incomplete or incorrect. The training includes evaluating the healthcare provider's current medical documentation process, training the healthcare provider in methods of recording medical documents, providing a system for recording medical documents, and training the healthcare provider in use of the system.

Systems and methods of recording patient's medical documents and training programs for improving medical document recordation. The system includes a computer readable medium capable of storing medical data obtained from patients, including disease codes, a computer with software capable of evaluating the data stored on the computer readable medium for completeness, and a notification system capable of presenting to the user of the system a warning if any of the data is found to be incomplete or incorrect. The training includes evaluating the healthcare provider's current medical documentation process, training the healthcare provider in methods of recording medical documents, providing a system for recording medical documents, and training the healthcare provider in use of the system.

Methods, systems, and computer-storage media are provided for using a generic aliasing scheme to facilitate electronic transcription of groups of clinical event data extracted from an electronic medical record to case report forms associated with clinical studies. The generic aliasing scheme is also used to electronically transcribe documentation of task completion to case report forms associated with the clinical studies.

Methods, systems, and computer-storage media are provided for using a generic aliasing scheme to facilitate electronic transcription of groups of clinical event data extracted from an electronic medical record to case report forms associated with clinical studies. The generic aliasing scheme is also used to electronically transcribe documentation of task completion to case report forms associated with the clinical studies.

An ultrasound imaging system includes a cine buffer in which image frames produced during an examination are stored. A processor is programmed to select one or more image frames from the cine buffer for presentation to an operator for approval and inclusion in a patient record or other report. The operator can accept the proposed image frames or can select one or more other image frames from the cine buffer. The processor may select image frames at spaced intervals in the cine buffer for presentation. Alternatively, the processor compares image frames in the cine buffer with one or more target image frames. Image frames that are similar to the target image frames are presented to the operator to confirm. Alternatively, image frames can be selected by the processor that contain a specific feature or that are similar to image frames that were previously selected by the operator when performing a particular type of examination.

A system (100) includes a feature extraction engine (130), a finding code comparison engine (140), and a mapping interface (160). The feature extraction engine (130) extracts features of a statement of a finding code in a source vocabulary (110) and features of a second statement of a second finding code in a target vocabulary (112). The finding code comparison engine (140) determines a mapping between the statement of the source vocabulary and the second statement of the target vocabulary by comparing the extracted features based on at least one identified concept that comprises the extracted features. The mapping interface (160) presents the determined mapping on a display device (162).