Abstract: Finding care plan(s) matching desired aspects includes receiving a selection of a model care plan from a user; generating a feature hash vector representing the model care plan, resulting in a model feature hash vector; comparing feature hash vectors of existing care plans to the model feature hash vector to find care plan(s) best matching the desired aspect(s) of a user, resulting in best match(es); and providing one or more care plan recommendation(s) to the user based on the best match(es).

Abstract: A computer system manages multi-stage transactions. A plurality of response time values of transaction components for a plurality of transactions are received. Two or more transactions from the plurality of transactions are selected, wherein a quantity of the selected transactions is equal to a number of the transaction components in the plurality of transactions. Eigenvalues are calculated from the response time values for the selected transactions. The selected transactions are determined to have timed out by processing the eigenvalues using a machine learning classifier. Embodiments of the present invention further include a method and program product for managing multi-stage transactions in substantially the same manner described above.

Abstract: A computer system manages multi-stage transactions. A plurality of response time values of transaction components for a plurality of transactions are received. Two or more transactions from the plurality of transactions are selected, wherein a quantity of the selected transactions is equal to a number of the transaction components in the plurality of transactions. Eigenvalues are calculated from the response time values for the selected transactions. The selected transactions are determined to have timed out by processing the eigenvalues using a machine learning classifier. Embodiments of the present invention further include a method and program product for managing multi-stage transactions in substantially the same manner described above.