Abstract: Aspects of the present disclosure provide a computer-implemented method that includes obtaining the medical condition of the individual based at least in part on a personal identifier associated with the individual, the medical condition being determined by performing natural language processing of medical information associated with the individual. The method further includes obtaining characteristics of the plurality of items. The method further includes comparing the characteristics of the plurality of items to a restriction associated with the medical condition of the individual. The method further includes, responsive to determining that at least one of the characteristics of the plurality of items violates the restriction associated with the medical condition of the individual, preventing the individual from obtaining the item of the plurality of items associated with the violation.

Abstract: Embodiments include methods, systems and computer program products for performing named entity recognition. Aspects include obtaining a text having a plurality of words and comparing each of the plurality of words to a dictionary. Aspects also include creating, based on the comparison, an annotation for at least one of the plurality of words that the least one of the plurality of words refers to a named entity. Aspects further include parsing the text to identify a part of speech for each of the plurality of words and removing the annotations from each of the at least one of the plurality of words that has a part of speech that is not one or a noun and a noun supporting adjective.

Abstract: A computer-implemented method includes receiving, by a natural language processing (NLP) annotator, an input text that is to be annotated. The method further includes determining, by the NLP annotator, a user setting that indicates an aggressiveness level of annotation to be used to annotate the input text. The method further includes selecting, by the NLP annotator, from a plurality of dictionaries, a first dictionary based at least in part on the aggressiveness level. The method further includes generating, by the NLP annotator, annotated text of the input text based at least in part on the first dictionary. The method further includes outputting, by the NLP annotator, the annotated text.

Abstract: Aspects of the present disclosure include receiving, by a processor, a procedure code for a patient, wherein the procedure code is associated with a medical device, obtaining, by the processor, patient medical data for the patient, analyzing the patient medical data to identify one or more patient medical conditions and one or more other procedure codes, determining a risk score for the procedure code based on the one or more patient medical conditions and the one or more other procedure codes, and enacting an action based at least in part on the risk score for the procedure code

Abstract: A computer-implemented method includes receiving, by a natural language processing (NLP) annotator, an input text that is to be annotated. The method further includes determining, by the NLP annotator, a user setting that indicates an aggressiveness level of annotation to be used to annotate the input text. The method further includes selecting, by the NLP annotator, from a plurality of dictionaries, a first dictionary based at least in part on the aggressiveness level. The method further includes generating, by the NLP annotator, annotated text of the input text based at least in part on the first dictionary. The method further includes outputting, by the NLP annotator, the annotated text.

Abstract: Systems and methods for generating alerts based on potential health conflicts are described. The described systems and methods may provide for identifying unstructured text from a physical environment, performing natural language processing on the unstructured text to produce annotated text, comparing the annotated text to patient medical information to find a potential conflict, and generating an alert based on the potential conflict.

Abstract: A method, apparatus, and non-transitory computer readable medium for transportation services using text analytics are described. The method, apparatus, and non-transitory computer readable medium may provide for inputting a text corpus comprising patient medical information, performing text analytics on the text corpus, determining if the patient has a need for transportation assistance based on the performed text analytics, and notifying a transportation service of the need for transportation assistance based on the determination.

Abstract: Systems and methods for generating alerts based on potential health conflicts are described. The described systems and methods may provide for identifying unstructured text from a physical environment, performing natural language processing on the unstructured text to produce annotated text, comparing the annotated text to patient medical information to find a potential conflict, and generating an alert based on the potential conflict.

Abstract: Aspects of the present disclosure provide a computer-implemented method that includes obtaining the medical condition of the individual based at least in part on a personal identifier associated with the individual, the medical condition being determined by performing natural language processing of medical information associated with the individual. The method further includes obtaining characteristics of the plurality of items. The method further includes comparing the characteristics of the plurality of items to a restriction associated with the medical condition of the individual. The method further includes, responsive to determining that at least one of the characteristics of the plurality of items violates the restriction associated with the medical condition of the individual, preventing the individual from obtaining the item of the plurality of items associated with the violation.

Abstract: Embodiments include methods, system and computer program products for identifying adverse effects of medications. Aspects include obtaining a plurality of medical records for an individual, at least one of the plurality of medical records including a medication prescribed to the individual. Aspects also include creating annotations for each medical record by performing natural language processing on the medical record, wherein the annotations include a date associated with the medical record. Aspects further include grouping medications prescribed to the individual into two groups based on the annotations, the groups including a first group of medications that the individual is currently prescribed and the second group of medications that the individual does not have a current prescription for. Aspects also include identifying a known adverse interaction between one of the first group of medications and one of the second group of medications and notifying the individual of the known adverse interaction.

Abstract: Embodiments include methods, systems and computer program products for performing named entity recognition. Aspects include obtaining a text having a plurality of words and comparing each of the plurality of words to a dictionary. Aspects also include creating, based on the comparison, an annotation for at least one of the plurality of words that the least one of the plurality of words refers to a named entity. Aspects further include parsing the text to identify a part of speech for each of the plurality of words and removing the annotations from each of the at least one of the plurality of words that has a part of speech that is not one or a noun and a noun supporting adjective.