Abstract: From metadata corresponding to a narrative text, a first encoding is constructed, the first encoding comprising a standardized text string, the first encoding formed according to an encoding scheme. A specified portion of the standardized text string of the first encoding is marked as an anchor term. A correspondence between the first encoding and a second encoding is tested using the encoding scheme and a Natural Language Processing engine, responsive to finding the anchor term within the narrative text. The second encoding corresponds to a text window. The text window comprises a portion of the narrative text comprising an instance of the anchor term and a word within a predetermined distance from the instance. Responsive to the second encoding being identical to the first encoding, the narrative text is annotated, the annotating creating new data linking the narrative text with the second encoding.

Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.

Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.

Abstract: Mechanisms are provided to implement a medical coding engine to perform medical coding using a neural network architecture that leverages hierarchical semantics between medical concepts. The medical coding engine configures a medical coding neural network to comprise an first layer of nodes comprising preferred terminology (PT) nodes, a second layer comprising lowest level terminology (LLT) nodes, and a third layer comprising weighted values for each connection between each PT node and each LLT node forming a PT node/LLT node connection. Responsive to receiving an adverse event from a cognitive system, a PT node is identified in the first layer associated with a citation from the adverse event. One or more LLT nodes are identified from the second layer based on the identification PT node and a weight associated with the PT node/LLT node connection. A medical code associated with each the one or more LLT nodes is then output.

Abstract: Mechanisms are provided to implement a medical coding engine to perform medical coding using a neural network architecture that leverages hierarchical semantics between medical concepts. The medical coding engine configures a medical coding neural network to comprise an first layer of nodes comprising preferred terminology (PT) nodes, a second layer comprising lowest level terminology (LLT) nodes, and a third layer comprising weighted values for each connection between each PT node and each LLT node forming a PT node/LLT node connection. Responsive to receiving an adverse event from a cognitive system, a PT node is identified in the first layer associated with a citation from the adverse event. One or more nodes are identified from the second layer based on the identification PT node and a weight associated with the PT node/LLT node connection. A medical code associated with each the one or more LLT nodes is then output.

Abstract: Mechanisms are provided that implement a drug-adverse event causality evaluation engine to identify human resource selections based on a readability of unstructured text within an individual case safety report (ICSR) and a confidence value of the ICSR. The drug-adverse event causality evaluation engine receives the ICSR from a cognitive system. The drug-adverse event causality evaluation engine analyzes the ICSR to determine a readability value of the ICSR. The drug-adverse event causality evaluation engine determines whether or not an assessment, by a human reviewer, of the ICSR is required based on a combination of the readability value of the ICSR and the confidence value. The drug-adverse event causality evaluation engine outputs an indication of whether human reviewer assessment is required.

Abstract: Mechanisms are provided that implement a drug-adverse event causality evaluation engine to identify human resource selections based on a readability of unstructured text within an individual case safety report (ICSR) and a confidence value of the ICSR. The drug-adverse event causality evaluation engine receives the ICSR from a cognitive system. The drug-adverse event causality evaluation engine analyzes the ICSR to determine a readability value of the ICSR. The drug-adverse event causality evaluation engine determines whether or not an assessment, by a human reviewer, of the ICSR is required based on a combination of the readability value of the ICSR and the confidence value. The drug-adverse event causality evaluation engine outputs an indication of whether human reviewer assessment is required.

Abstract: From metadata corresponding to a narrative text, a first encoding is constructed, the first encoding comprising a standardized text string, the first encoding formed according to an encoding scheme. A specified portion of the standardized text string of the first encoding is marked as an anchor term. A correspondence between the first encoding and a second encoding is tested using the encoding scheme and a Natural Language Processing engine, responsive to finding the anchor term within the narrative text. The second encoding corresponds to a text window. The text window comprises a portion of the narrative text comprising an instance of the anchor term and a word within a predetermined distance from the instance. Responsive to the second encoding being identical to the first encoding, the narrative text is annotated, the annotating creating new data linking the narrative text with the second encoding.

Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.

Abstract: A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical ontology data structure. A surface finding component executing within the training system selects a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The surface finding component determines a plurality of adjacent surfaces that differ from the selected component by one node. The surface finding component selects an optimal surface, based on the selected surface and the plurality of adjacent surfaces, that maximizes accuracy and coverage. The training system trains a classifier model for a cognitive system using the optimal surface and the training data set.

Abstract: Mechanisms are provided to implement a medical coding engine to perform medical coding using a neural network architecture that leverages hierarchical semantics between medical concepts. The medical coding engine configures a medical coding neural network to comprise an first layer of nodes comprising preferred terminology (PT) nodes, a second layer comprising lowest level terminology (LLT) nodes, and a third layer comprising weighted values for each connection between each PT node and each LLT node forming a PT node/LLT node connection. Responsive to receiving an adverse event from a cognitive system, a PT node is identified in the first layer associated with a citation from the adverse event. One or more nodes are identified from the second layer based on the identification PT node and a weight associated with the PT node/LLT node connection. A medical code associated with each the one or more LLT nodes is then output.

Abstract: Mechanisms are provided that implement a drug-adverse event causality evaluation engine to identify human resource selections based on a readability of unstructured text within an individual case safety report (ICSR) and a confidence value of the ICSR. The drug-adverse event causality evaluation engine receives the ICSR from a cognitive system. The drug-adverse event causality evaluation engine analyzes the ICSR to determine a readability value of the ICSR. The drug-adverse event causality evaluation engine determines whether or not an assessment, by a human reviewer, of the ICSR is required based on a combination of the readability value of the ICSR and the confidence value. The drug-adverse event causality evaluation engine outputs an indication of whether human reviewer assessment is required.

Abstract: Mechanisms are provided that implement a drug-adverse event causality evaluation engine to identify human resource selections based on a readability of unstructured text within an individual case safety report (ICSR) and a confidence value of the ICSR. The drug-adverse event causality evaluation engine receives the ICSR from a cognitive system. The drug-adverse event causality evaluation engine analyzes the ICSR to determine a readability value of the ICSR. The drug-adverse event causality evaluation engine determines whether or not an assessment, by a human reviewer, of the ICSR is required based on a combination of the readability value of the ICSR and the confidence value. The drug-adverse event causality evaluation engine outputs an indication of whether human reviewer assessment is required.

Abstract: Evaluation of textual annotation models is provided. In various embodiments, an annotation model is applied to textual training data to derive a plurality of automatic annotations. The plurality of automatic annotations is compared to ground truth annotations of the textual data to determine overlapping tokens between the plurality of automatic annotations and the ground truth annotations. Weights are assigned to the overlapping tokens. Based on the weights of the overlapping tokens, scores are determined for the automatic annotations. The scores indicate the correctness of the automatic annotations relative to the ground truth annotations. Based on the scores of for the automatic annotations, an accuracy of the annotation model is determined.