公开(公告)号：US20190035496A1

公开(公告)日：2019-01-31

申请号：US16073484

申请日：2017-02-27

Applicant: MOR RESEARCH APPLICATIONS LTD

Inventor： Doron NETZER ,  Dan NABRISKI ,  Eytan ROITMAN

IPC: G16H20/10 ,  G16H10/60 ,  G16H50/20

Abstract: Embodiments disclosed herein relate to a computerized method of selecting an optimal medication for a patient, comprising: providing a user with communication access to a central server having in memory a database of a rule set associated with a list of medications available, the rules set numerically ranking the suitability of a medication for a patient; allowing upload of at least a portion of an electronic medical record for an identified patient; allowing input of a patient diagnosis, and receiving a search request for a suitable treatment medication; comparing the rules set, with the uploaded electronic medical record; determining (e.g., scoring and calculating) the suitability of medications from the list of medications available; providing a displayable list of medications suited for the patient, wherein the list is ranked according to the preference of the medications for the patient.

公开(公告)号：US20240105298A1

公开(公告)日：2024-03-28

申请号：US18283236

申请日：2022-03-20

Applicant: Mor Research Applications Ltd.

Inventor： Doron NETZER ,  Ran BALICER ,  Noa DAGAN ,  Eldad KEPTEN ,  Nava LEIBOVICH ,  Yifat MIRON ,  Shlomit BLOOMENTHAL ,  Jacob Gershon WAXMAN ,  Ronit SAFAR ,  Moria MAHANAIMY ,  Ilana ROITMAN KALISHOV

IPC: G16H20/00 ,  G16H10/60 ,  G16H50/20 ,  G16H50/30

CPC classification number: G16H20/00 ,  G16H10/60 ,  G16H50/20 ,  G16H50/30

Abstract: A method comprises: for each specific medical intervention and/or respective clinical outcome: accessing a respective specific priority list of a respective sub-set of subjects scheduled in a prioritized sequence for treatment and/or evaluation, creating a respective specific training dataset that includes data extracted from EMRs of the respective sub-set of subjects labelled with the specific priority list, and training a respective specific machine learning model on the respective specific training dataset for generating an outcome of a respective specific priority list of a sub-set of subjects for prioritized evaluation and/or treatment, in response to an input of data extracted from EMR of the sub-set of subjects, accessing a combined prioritization component for generating an outcome of a combined priority list of subjects for prioritized evaluation and/or treatment in response to an input of outcomes of the specific machine learning models, and providing the specific models and the combined prioritization component.