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公开(公告)号：US11107589B2
公开(公告)日：2021-08-31
申请号：US15855314
申请日：2017-12-27
发明人： Sanjoy Dey , Ping Zhang
摘要： A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a drug response estimation engine. The drug response estimation engine receives real-world evidence for a plurality of patients. A patient similarity network builder component executing within the drug response estimation engine builds a patient similarity network. A regression analysis component executing within the drug response estimation engine builds a network localized regression analysis approach. A patient clustering component executing within the drug response estimation engine groups patients based on demographics and comorbidities to form a plurality of patient clusters. The drug response estimation engine estimates drug responses for a given patient based on the patient similarity network, the network localized regression analysis approach, and the plurality of patient clusters. The drug response estimation engine outputs the drug responses for the given patient.
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公开(公告)号：US20210202055A1
公开(公告)日：2021-07-01
申请号：US16730107
申请日：2019-12-30
发明人： Cao Xiao , Zachary Shahn , Daby M. Sow , Mohamed Ghalwash , Sanjoy Dey
摘要： A mechanism computes a discounted health variable with a penalty for deviating from clinical guidelines based on a distance function representing an allowed deviation from the clinical guidelines, applies reinforcement learning techniques on the discounted health variable to generate a reinforcement learning (RL) model for generating dynamic treatment regimes, and determines, for a patient for a plurality of times, a next action in a treatment regime using the RL model with no distance function, an optimal next action in the treatment regime with allowed deviation from the guidelines, and a next action in the treatment regime that adheres to the guidelines. The mechanism generates an outcome output display based on the determined next action in a treatment regime using the RL model with no distance function, optimal next action in the treatment regime with allowed deviation from the guidelines, and next action in the treatment regime that adheres to the guidelines.
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3.发明申请公开(公告)号：US20190279774A1
公开(公告)日：2019-09-12
申请号：US15913221
申请日：2018-03-06
发明人： Sanjoy Dey , Mohamed Ghalwash , Ping Zhang
摘要： Mechanisms are provided for implementing a framework to learn multiple drug-adverse drug reaction associations. The mechanisms receive and analyze patient electronic medical record data and adverse drug reaction data to identify co-occurrences of references to drugs with references to adverse drug reactions (ADRs) to thereby generate candidate rules specifying multiple drug-ADR relationships. The mechanisms filter the candidate rules to remove a subset of one or more rules having confounder drugs specified in the subset of one or more candidate rules, and thereby generate a filtered set of candidate rules. The mechanisms further generate a causal model based on the filtered set of candidate rules. The causal model comprises, for each ADR in a set of ADRs, a corresponding set of one or more rules, each rule specifying a combination of drugs having a causal relationship with the ADR.
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公开(公告)号：US11276494B2
公开(公告)日：2022-03-15
申请号：US15976970
申请日：2018-05-11
摘要： Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and disease interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more disease similarity measures between one or more diseases. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more diseases, then calculates one or more drug-disease feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more disease similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first disease of the one or more diseases based on a model, wherein the model is trained based on the one or more drug-disease feature vectors.
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公开(公告)号：US20220059244A1
公开(公告)日：2022-02-24
申请号：US17517998
申请日：2021-11-03
发明人： Sanjoy Dey , MOHAMED GHALWASH , PING ZHANG
摘要： Mechanisms are provided for implementing a framework to learn multiple drug-adverse drug reaction associations. The mechanisms receive and analyze patient electronic medical record data and adverse drug reaction data to identify co-occurrences of references to drugs with references to adverse drug reactions (ADRs) to thereby generate candidate rules specifying multiple drug-ADR relationships. The mechanisms filter the candidate rules to remove a subset of one or more rules having confounder drugs specified in the subset of one or more candidate rules, and thereby generate a filtered set of candidate rules. The mechanisms further generate a causal model based on the filtered set of candidate rules. The causal model comprises, for each ADR in a set of ADRs, a corresponding set of one or more rules, each rule specifying a combination of drugs having a causal relationship with the ADR.
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6.发明申请公开(公告)号：US20190228865A1
公开(公告)日：2019-07-25
申请号：US16178734
申请日：2018-11-02
摘要： Mechanisms are provided that implement a plurality of heterogeneous causality models and a metaclassifier for predicting a likelihood of causality between a drug and an adverse event (AE). The plurality of heterogenous causality models process drug information for the drug to generate a plurality of risk predictions for a drug and AE pair. The risk predictions include at least one of a risk score or a risk label indicating a probability of the AE occurring with use of the drug. The plurality of heterogenous causality models provide the risk predictions, associated with the drug and AE pair, to a metaclassifier which generates a single causality score value indicative of a probability of causality between the drug and the AE, of the drug and AE pair, based on an aggregation of the risk predictions from the plurality of heterogenous causality models. The metaclassifier outputs the single causality score value in association with information identifying the drug and AE pair.
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公开(公告)号：US10902943B2
公开(公告)日：2021-01-26
申请号：US15982281
申请日：2018-05-17
摘要： Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and food interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more food similarity measures between one or more foods. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more foods, then calculates one or more drug-food feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more food similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first food of the one or more foods based on a model, wherein the model is trained based on the one or more drug-food feature vectors.
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公开(公告)号：US20190355458A1
公开(公告)日：2019-11-21
申请号：US15982281
申请日：2018-05-17
摘要： Embodiments of the present invention disclose a method, a computer program product, and a computer system for predicting drug and food interactions. A computer identifies one or more drug similarity measures between one or more drugs and one or more food similarity measures between one or more foods. In addition, the computer identifies one or more interactions between the one or more drugs and the one or more foods, then calculates one or more drug-food feature vectors based on the one or more interactions, the one or more drug similarity measures, and the one or more food similarity measures. Furthermore, the computer calculates a first probability indicating whether a first drug of the one or more drugs will interact with a first food of the one or more foods based on a model, wherein the model is trained based on the one or more drug-food feature vectors.
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9.发明申请公开(公告)号：US20190279775A1
公开(公告)日：2019-09-12
申请号：US16176022
申请日：2018-10-31
发明人： Sanjoy Dey , Mohamed Ghalwash , Ping Zhang
摘要： Mechanisms are provided for implementing a framework to learn multiple drug-adverse drug reaction associations. The mechanisms receive and analyze patient electronic medical record data and adverse drug reaction data to identify co-occurrences of references to drugs with references to adverse drug reactions (ADRs) to thereby generate candidate rules specifying multiple drug-ADR relationships. The mechanisms filter the candidate rules to remove a subset of one or more rules having confounder drugs specified in the subset of one or more candidate rules, and thereby generate a filtered set of candidate rules. The mechanisms further generate a causal model based on the filtered set of candidate rules. The causal model comprises, for each ADR in a set of ADRs, a corresponding set of one or more rules, each rule specifying a combination of drugs having a causal relationship with the ADR.
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公开(公告)号：US20190198179A1
公开(公告)日：2019-06-27
申请号：US16209196
申请日：2018-12-04
发明人： Sanjoy Dey , Ping Zhang
摘要： A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a drug response estimation engine. The drug response estimation engine receives real-world evidence for a plurality of patients. A patient similarity network builder component executing within the drug response estimation engine builds a patient similarity network. A regression analysis component executing within the drug response estimation engine builds a network localized regression analysis approach. A patient clustering component executing within the drug response estimation engine groups patients based on demographics and comorbidities to form a plurality of patient clusters. The drug response estimation engine estimates drug responses for a given patient based on the patient similarity network, the network localized regression analysis approach, and the plurality of patient clusters The drug response estimation engine outputs the drug responses for the given patient.
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