Abstract: A method, a computing system and a computer program product are provided. A computing system identifies elements within a collection of medical documents. The elements include patients, adverse events and medical drugs. The medical documents are analyzed by the computer system to determine associations between the identified medical drugs and corresponding identified adverse events. The identified elements and the determined associations may be encoded as features by the computing system. The computing system identifies portions of the medical documents as containing the identified elements and the determined associations. The computing system generates a classification model based at least on the encoded features associated with the identified portions for identifying medical case safety reports within medical documents. The classification model is applied to a new document to determine a classification of the new document with respect to a medical case safety report.

Abstract: Embodiments herein include a NLP application used to coach a participant who violates a social norm during a conversation. For example, the NLP application can evaluate the textual representation of the conversation to determine if the participant is exhibiting a characteristic of autism or other medical disorder which violates a social norm, and if so, inform the participant. Once a characteristic of autism is identified, a coaching application may output text that informs the participant what particular characteristic he is exhibiting—e.g., the participant is ignoring an attempt by another participant to change the topic of the conversation. In addition to providing notice, in one embodiment, the coaching application suggests a corrective action to the participant. For example, if the participant fails to provide an appropriate response to an emotional statement, the coaching action may suggest a sympathetic statement.

Abstract: Embodiments herein include a NLP application used to coach a participant who violates a social norm during a conversation. For example, the NLP application can evaluate the textual representation of the conversation to determine if the participant is exhibiting a characteristic of autism or other medical disorder which violates a social norm, and if so, inform the participant. Once a characteristic of autism is identified, a coaching application may output text that informs the participant what particular characteristic he is exhibiting—e.g., the participant is ignoring an attempt by another participant to change the topic of the conversation. In addition to providing notice, in one embodiment, the coaching application suggests a corrective action to the participant. For example, if the participant fails to provide an appropriate response to an emotional statement, the coaching action may suggest a sympathetic statement.

Abstract: A method, a computing system and a computer program product are provided. A computing system identifies elements within a collection of medical documents. The elements include patients, adverse events and medical drugs. The medical documents are analyzed by the computer system to determine associations between the identified medical drugs and corresponding identified adverse events. The identified elements and the determined associations may be encoded as features by the computing system. The computing system identifies portions of the medical documents as containing the identified elements and the determined associations. The computing system generates a classification model based at least on the encoded features associated with the identified portions for identifying medical case safety reports within medical documents. The classification model is applied to a new document to determine a classification of the new document with respect to a medical case safety report.

Abstract: Embodiments herein include a natural language computing system that provides a diagnosis for a participant in the conversation which indicates the likelihood that the participant exhibited a symptom of autism. To provide the diagnosis, the computing system includes a diagnosis system that performs a training process to generate a machine learning model which is then used to evaluate a textual representation of the conversation. For example, the diagnosis system may receive one or more examples of baseline conversations that exhibit symptoms of autisms and those that do not. The diagnosis system may annotate and the baseline conversations and identify features that are used to identify the symptoms of autism. The system generates a machine learning model that weights the features according to whether the identified features are, or are not, an indicator of autism.

Abstract: Embodiments herein include a natural language computing system that provides a diagnosis for a participant in the conversation which indicates the likelihood that the participant exhibited a symptom of autism. To provide the diagnosis, the computing system includes a diagnosis system that performs a training process to generate a machine learning model which is then used to evaluate a textual representation of the conversation. For example, the diagnosis system may receive one or more examples of baseline conversations that exhibit symptoms of autisms and those that do not. The diagnosis system may annotate and the baseline conversations and identify features that are used to identify the symptoms of autism. The system generates a machine learning model that weights the features according to whether the identified features are, or are not, an indicator of autism.

Abstract: Embodiments herein include a NLP application used to coach a participant who violates a social norm during a conversation. For example, the NLP application can evaluate the textual representation of the conversation to determine if the participant is exhibiting a characteristic of autism or other medical disorder which violates a social norm, and if so, inform the participant. Once a characteristic of autism is identified, a coaching application may output text that informs the participant what particular characteristic he is exhibiting—e.g., the participant is ignoring an attempt by another participant to change the topic of the conversation. In addition to providing notice, in one embodiment, the coaching application suggests a corrective action to the participant. For example, if the participant fails to provide an appropriate response to an emotional statement, the coaching action may suggest a sympathetic statement.

Abstract: Embodiments herein include a NLP application used to coach a participant who violates a social norm during a conversation. For example, the NLP application can evaluate the textual representation of the conversation to determine if the participant is exhibiting a characteristic of autism or other medical disorder which violates a social norm, and if so, inform the participant. Once a characteristic of autism is identified, a coaching application may output text that informs the participant what particular characteristic he is exhibiting—e.g., the participant is ignoring an attempt by another participant to change the topic of the conversation. In addition to providing notice, in one embodiment, the coaching application suggests a corrective action to the participant. For example, if the participant fails to provide an appropriate response to an emotional statement, the coaching action may suggest a sympathetic statement.

Abstract: Embodiments herein include a natural language computing system that provides a diagnosis for a participant in the conversation which indicates the likelihood that the participant exhibited a symptom of autism. To provide the diagnosis, the computing system includes a diagnosis system that performs a training process to generate a machine learning model which is then used to evaluate a textual representation of the conversation. For example, the diagnosis system may receive one or more examples of baseline conversations that exhibit symptoms of autisms and those that do not. The diagnosis system may annotate and the baseline conversations and identify features that are used to identify the symptoms of autism. The system generates a machine learning model that weights the features according to whether the identified features are, or are not, an indicator of autism.

Abstract: Embodiments herein include a natural language computing system that provides a diagnosis for a participant in the conversation which indicates the likelihood that the participant exhibited a symptom of autism. To provide the diagnosis, the computing system includes a diagnosis system that performs a training process to generate a machine learning model which is then used to evaluate a textual representation of the conversation. For example, the diagnosis system may receive one or more examples of baseline conversations that exhibit symptoms of autisms and those that do not. The diagnosis system may annotate and the baseline conversations and identify features that are used to identify the symptoms of autism. The system generates a machine learning model that weights the features according to whether the identified features are, or are not, an indicator of autism.