Abstract: A computer implemented method determines skill shares for skills from job advertisements. A skill share for a skill identifies a number of times a skill has appeared in job advertisements during a given period of time. The computer implemented method creates a time series of skill demand using the skill shares. The computer implemented method extracts embeddings from job advertisements for an occupation using natural language processing. The computer implemented method clusters the skills using the embeddings to form skill clusters. The computer implemented method defines a training dataset using as a time series of skill demand for all skills within a cluster containing a selected skill to be predicted. The computer implemented method trains a time series prediction model using the training dataset.

Abstract: A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.

Abstract: A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.

Abstract: A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.

Abstract: A reaming apparatus for reaming pipe may include a body having a planar front surface, an annular groove defined in the planar front surface of the body and concentric with a center of the body, and a straight groove defined in the planar front surface of the body, radially aligned with the center of the body, and intersecting the annular groove. The straight groove may pass through the center of the body. The reaming apparatus may include a reaming blade having a planar front surface and at least one notch defined in the planar front surface of the reaming blade, the at least one notch being positioned on the reaming blade to substantially align with the annular groove when the reaming blade is disposed within the straight groove. The reaming apparatus can be removably mounted to a pulley of a bandsaw to ream pipe under the power of the bandsaw.

Abstract: A mechanism is provided for implement a discrepancy detection mechanism for detecting discrepancies between clinical notes and administrative records. Clinical concepts are extracted from the clinical notes and the administrative records in a patient's electronic medical records (EMRs). The extracted clinical concepts are filtered based on semantic type information to identify concepts that reference diseases or syndromes while also removing negated instances. Utilizing the positive mentions of diseases in clinical notes, the positive mentions of diseases or syndromes in the clinical notes are compared against each positive entry in the administrative records. A discrepancy summary is then generated for diseases or syndromes that failed to translate correctly from clinical notes to the administrative records in the patient's EMRs.

Abstract: A reaming apparatus for reaming pipe may include a body having a planar front surface, an annular groove defined in the planar front surface of the body and concentric with a center of the body, and a straight groove defined in the planar front surface of the body, radially aligned with the center of the body, and intersecting the annular groove. The straight groove may pass through the center of the body. The reaming apparatus may include a reaming blade having a planar front surface and at least one notch defined in the planar front surface of the reaming blade, the at least one notch being positioned on the reaming blade to substantially align with the annular groove when the reaming blade is disposed within the straight groove. The reaming apparatus can be removably mounted to a pulley of a bandsaw to ream pipe under the power of the bandsaw.

Abstract: Mechanisms are provided to implement a natural language request processing engine (NLRPE). The NRLPE performs natural language processing on a portion of unstructured text in an electronic data structure to generate textual characteristics of the portion of unstructured text. The NRLPE annotates at least one phrase in the portion of unstructured text at least by linking the at least one phrase to one or more concepts specified in at least one ontological data structure based on the textual characteristics of the portion of unstructured text. The NRLPE generates a model of the portion of unstructured text based on the one or more concepts linked to the at least one phrase. The NRLPE processes a request for information specifying a concept of interest based on the model of the portion of unstructured text by retrieving the at least one phrase or the at least one merged phrase as a response.

Abstract: Mechanisms are provided to implement a natural language request processing engine (NLRPE). The NRLPE performs natural language processing on a portion of unstructured text in an electronic data structure to generate textual characteristics of the portion of unstructured text. The NRLPE annotates at least one phrase in the portion of unstructured text at least by linking the at least one phrase to one or more concepts specified in at least one ontological data structure based on the textual characteristics of the portion of unstructured text. The NRLPE generates a model of the portion of unstructured text based on the one or more concepts linked to the at least one phrase. The NRLPE processes a request for information specifying a concept of interest based on the model of the portion of unstructured text by retrieving the at least one phrase as a response.

Abstract: Mechanisms are provided to implement a natural language request processing engine (NLRPE). The NRLPE performs natural language processing on a portion of unstructured text in an electronic data structure to generate textual characteristics of the portion of unstructured text. The NRLPE annotates at least one phrase in the portion of unstructured text at least by linking the at least one phrase to one or more concepts specified in at least one ontological data structure based on the textual characteristics of the portion of unstructured text. The NRLPE generates a model of the portion of unstructured text based on the one or more concepts linked to the at least one phrase. The NRLPE processes a request for information specifying a concept of interest based on the model of the portion of unstructured text by retrieving the at least one phrase as a response.

Abstract: Mechanisms are provided to implement a natural language request processing engine (NLRPE). The NRLPE performs natural language processing on a portion of unstructured text in an electronic data structure to generate textual characteristics of the portion of unstructured text. The NRLPE annotates at least one phrase in the portion of unstructured text at least by linking the at least one phrase to one or more concepts specified in at least one ontological data structure based on the textual characteristics of the portion of unstructured text. The NRLPE generates a model of the portion of unstructured text based on the one or more concepts linked to the at least one phrase. The NRLPE processes a request for information specifying a concept of interest based on the model of the portion of unstructured text by retrieving the at least one phrase or the at least one merged phrase as a response.

Abstract: Mechanisms are provided to implement a medical information summarization engine (MISE). The MISE receives input specifying a summarization template, wherein the summarization template specifies terms or concepts of interest to a medical professional when making a medical decision regarding a patient. The MISE maps the terms or concepts of interest to medical concepts in a medical knowledge base. The MISE processes electronic medical records (EMR) of the patient based on the mapping of the medical concepts in the medical knowledge base to the terms or concepts of interest in the summarization template to extract patient information from the patient EMR that matches at least one of the medical concepts from the mapping. The MIE generates and outputs a holistic summary of the patient's EMRs that summarizes the most salient portions of the patient EMR for use by the medical professional in making the medical decision regarding the patient.

Abstract: Mechanisms are provided to implement a medical information summarization engine (MISE). The MISE receives input specifying a summarization template, wherein the summarization template specifies terms or concepts of interest to a medical professional when making a medical decision regarding a patient. The MISE expands the summarization template based on related concepts or related terms related to the terms or concepts of interest specified in the summarization template. The MISE processes an EMR of the patient based on the expanded summarization template to extract patient information corresponding to the terms or concepts of interest and the related concepts or related terms. The MISE generates and outputs a holistic summary of the EMR of the patient that summarizes the most salient portions of the patient EMR for use by the medical professional in making the medical decision regarding the patient, based on extracted patient information obtained from processing the patient EMR.

Abstract: According to one embodiment, a rotorcraft yoke comprises a plurality of arms. The plurality of arms, in combination, comprise a plurality of fibrous plies extending continuously throughout all of the plurality of arms.

Abstract: According to one embodiment, a rotorcraft yoke comprises a plurality of arms. The plurality of arms, in combination, comprise a plurality of fibrous plies extending continuously throughout all of the plurality of arms.

Abstract: Techniques for analyzing a social graph of a social networking service to derive a social proximity score representing a measure of how socially connected a member is to a target entity are described. With some embodiments, an aggregate social proximity score is derived to reflect a measure of how well socially connected a person is to a set of organizations representing customer accounts of a business organization. Accordingly, the aggregate social proximity score can be used to compare candidates in a hiring decision, or to identify potential candidates during recruiting.

Abstract: Techniques for analyzing a social graph of a social networking service to derive a social proximity score representing a measure of how socially connected a member is to a target entity are described. With some embodiments, an aggregate social proximity score is derived to reflect a measure of how well socially connected a person is to a set of organizations representing customer accounts of a business organization. Accordingly, the aggregate social proximity score can be used to compare candidates in a hiring decision, or to identify potential candidates during recruiting.

Abstract: Techniques for analyzing a social graph of a social networking service to derive a social proximity score representing a measure of how socially connected a member is to a target entity are described. With some embodiments, for each member of the social networking service in a pre-defined group of members, a social proximity score is derived to reflect a measure of how socially connected the member is to a target entity (e.g., company, school, government institution, or some other organization). The social proximity score for each member-target entity pairing may be based on a variety of factors derived from analyzing the social graph, member profile information, and in some instances, the activity and behavior of the members.

Abstract: Techniques for allocating sales representatives to potential new and/or existing customer accounts are described. With sonic embodiments, for each member of the social networking service in a pre-defined group of members (e.g., team of sales representatives), a social proximity score is derived to reflect a measure of how socially connected the member is to a target entity (e.g., company, school, government institution, or some other organization). The new or existing accounts are then allocated to various members in the pre-defined group, based at least in part on the social proximity scores.