International Business Machines Corporation

Italo Buleje

Shilpa N Mahatma

Rachita Chandra

Fang Lu

Piyush Madan

Gigi Y C Yuen-Reed

Kevin N Tran

Jingwei Yang

Sundar Saranathan

Sharon M Hensley Alford

Embodiments of the present invention disclose a method, a computer program product, and a computer system for medication decision support. A computer generates one or more patient profiles detailing one or more patient health conditions and one or more medication profiles detailing one or more medication side effects. The computer then determines an association between the one or more patient health conditions and one or more medication side effects and quantifies the association as a conflict score. In addition, the computer determines whether the conflict score exceeds a threshold and, if so, the computer identifies and recommends an alternative medication having a lower conflict score.

Medication decision support

Embodiments of the present invention disclose a method, a computer program product, and a computer system for mapping components of non-distributed environments to distributed environments. A computer receives a data pipeline configured for a non-distributed environment and identifies one or more bottleneck components of the data pipeline. In addition, the computer converts data used in the pipeline to a format compatible with a distributed environment and installs the necessary computing libraries necessary for operating the pipeline within the distributed environment. The computer further converts the code of the pipeline to a code that is compatible with the distributed environment and optimizes components of the pipeline for use in the distributed environment.

Mapping components of a non-distributed environment to a distributed environment

Methods and apparatus for identifying health trends via the identification of ambiguous food items via container identification may be provided by: receiving an image captured from a user device that includes a food item; identifying a food container present in the image; identifying a utensil present in the image; determining a cluster of food items from a food recognition database corresponding to the food container and to the utensil; selecting a candidate food item from the cluster based on a confidence score of the candidate food item matching the food item; and adding the candidate food item to a dietary log associated with the user device.

Health trends identification

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Orlando, FL, United States of America

Italo Buleje