-
公开(公告)号：US10978208B2
公开(公告)日：2021-04-13
申请号：US14097995
申请日：2013-12-05
发明人： Jianying Hu , Buyue Qian , Fei Wang , Jun Wang , Xiang Wang
IPC分类号： G16H50/30
摘要： A system and method for patient stratification include determining a first set of patient groups from patients in a patient similarity graph based on a similarity structure of the patient similarity graph. A second set of patient groups is identified based on expert domain knowledge associated with the patients. Patients in the first set and the second set are aligned using a processor to stratify patients.
-
公开(公告)号：US10452961B2
公开(公告)日：2019-10-22
申请号：US14921365
申请日：2015-10-23
发明人： Jianying Hu , Yajuan Wang , Fei Wang , Chuanren Liu
摘要： In one embodiment, a computer-implemented method includes transforming a plurality of electronic health records into a plurality of temporal graphs indicating an order in which events observed in the plurality of electronic health records occur and learning a temporal pattern from the plurality of temporal graphs, wherein the temporal pattern indicates an order of events that is observed to occur repeatedly across the plurality of temporal graphs.
-
3.发明申请公开(公告)号：US20190130070A1
公开(公告)日：2019-05-02
申请号：US15799664
申请日：2017-10-31
发明人： Yu Cheng , Soumya Ghosh , Jianying Hu , Ying Li , Zhaonan Sun
摘要： A system (or method) for generation and employment of disease progression model(s) that facilitates identifying and indexing discriminative features for disease progression in observational data. The disease progression prediction system comprises a processor that executes computer executable components stored in memory. A receiving component receives and learns observational patient data. A model generation component builds a preliminary disease progression model. An identification component identifies discriminative clinical features for different disease stages. A ranking component ranks discriminative powers of clinical features for respective pairs of disease stages; wherein the model generation component employs the ranked features to generate a final disease progression model.
-
公开(公告)号：US20180307804A1
公开(公告)日：2018-10-25
申请号：US15494027
申请日：2017-04-21
发明人： Sanjoy Dey , Achille Belly Fokoue-Nkoutche , Jianying Hu , Heng Luo , Ping Zhang
CPC分类号： G06F19/707 , G06N3/04 , G06N3/08
摘要： Embodiments of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.
-
5.发明授权公开(公告)号：US09996889B2
公开(公告)日：2018-06-12
申请号：US13632659
申请日：2012-10-01
发明人： David H. Gotz , Pei-Yun S. Hsueh , Jianying Hu , Jimeng Sun
CPC分类号： G06Q50/22 , G06F19/00 , G06Q10/0635 , G06Q10/0637 , G16H50/30 , G16H50/70
摘要： Systems and methods for individual risk factor identification include identifying common risk factors for one or more risk targets from population data. Individuals are stratified into clusters based upon the common risk factors. A discriminability of each of the common risk factors is determined, using a processor, for a target cluster using individual data of the target cluster to provide re-ranked common risk factors as individual risk factors for the target cluster, such that the discriminability is a measure of how a risk factor discriminates its cluster from other clusters.
-
6.发明申请公开(公告)号：US20170124469A1
公开(公告)日：2017-05-04
申请号：US14929995
申请日：2015-11-02
发明人： Jianying Hu , Zhaonan Sun , Fei Wang , Ping Zhang
摘要： An embodiment of the invention receives input including a list of drugs, drug characteristics of each drug, and known drug-disease associations including a disease and a drug having a threshold efficacy for treating the disease. For each drug in the list of drugs, a processor predicts whether the drug meets a threshold efficacy for treating a first disease based on the drug characteristics and the drug-disease associations. For each drug in the list of drugs, the processor predicts whether the drug meets a threshold efficacy for treating a second disease based on the drug characteristics and the predicting of whether the drug meets the threshold efficacy for treating the first disease. Output is generated output based on the predictions, the output including an identified drug-disease association, an identified disease-disease association, an identified chemical fingerprint for the first disease, and an identified chemical fingerprint for the second disease.
-
7.发明申请公开(公告)号：US20170098063A1
公开(公告)日：2017-04-06
申请号：US15289077
申请日：2016-10-07
发明人： Nan Cao , Jianying Hu , Robert K. Sorrentino , Fei Wang , Ping Zhang
摘要： A system and method for analyzing chemical data including a processor and one or more classifiers, stored in memory and coupled to the processor, which further includes an indication predictive module configured to predict whether a given chemical treats a particular indication or not and a side effect predictive module configured to predict whether a given chemical causes a side-effect or not. A correlation engine is configured to determine one or more correlations between one or more indications and one or more side effects for the given chemical and a visualization tool is configured to analyze the one or more correlations and to output results of the analysis.
-
公开(公告)号：US20140236544A1
公开(公告)日：2014-08-21
申请号：US13770627
申请日：2013-02-19
发明人： Shahram Ebadollahi , Jianying Hu , Jimeng Sun , Fei Wang , Jiayu Zhou
IPC分类号： G06F19/12
摘要： A system and method for providing a temporally dynamic model parameter include building a model parameter by minimizing a loss function based on patient measurements taken at a plurality of time points. Temporally related values of the model parameter are identified, using a processor, having a same type of patient measurement taken at different time points. At least one value of the model parameter and temporally related values of the at least one value are selected to provide a temporally dynamic model parameter.
-
公开(公告)号：US20140058986A1
公开(公告)日：2014-02-27
申请号：US14067416
申请日：2013-10-30
发明人： Gregory Jensen Boss , Ching-Hua Chen-Ritzo , Rick Allen Hamilton, II , Jianying Hu , Clifford Alan Pickover
CPC分类号： G06N5/00 , G06F17/30976 , G06F19/00 , G06F19/325 , G06F19/3456 , G06K9/62 , G06N5/02 , G06N99/005 , G06Q10/10 , G06Q50/00 , G16H50/20
摘要： A DeepQA engine is enhanced to provide a digital medical investigation tool which assists a medical professional in researching potential causes of a set of patient conditions, including clues, facts and factoids about the patient. The DeepQA engine provides one or more answers to a natural language question with confidence levels for each answer. If a confidence level falls below a threshold, the enhanced DeepQA engine performs a crowd sourcing operation to gather additional information from one or more domain experts. The domain expert responses are provided to the medical professional, and are learned by the enhanced DeepQA system to provide for better research of similar patient conditions in future queries.
摘要翻译： DeepQA引擎被增强以提供数字医疗调查工具，其帮助医学专业人员研究一组病人状况的潜在原因，包括关于患者的线索，事实和事实。 DeepQA引擎为自然语言问题提供一个或多个答案，每个答案的置信水平。 如果置信水平低于阈值，则增强型DeepQA引擎执行群众采购操作，从一个或多个域专家收集附加信息。 领域专家的回答提供给医疗专业人员，并由增强型DeepQA系统学习，以提供对未来查询中类似患者状况的更好的研究。
-
10.发明申请公开(公告)号：US20220415524A1
公开(公告)日：2022-12-29
申请号：US17361717
申请日：2021-06-29
发明人： Sarah Kefayati , PRITHWISH CHAKRABORTY , Ajay Ashok Deshpande , Vishrawas Gopalakrishnan , Jianying Hu , Hu Trombley Huang , Gretchen Jackson , Xuan Liu , SAYALI NAVALEKAR , Raman Srinivasan
摘要： In an approach for building a machine learning model with a flexible prediction horizon, a processor gathers statistical data related to a disease from one or more regional sources. A processor clusters the statistical data according to a plurality of localized regional source similarity criteria and a plurality of region criteria. A processor builds a plurality of training models based on the clustered statistical data. A processor builds a plurality of feature vectors based on the plurality of localized regional source similarity criteria and the plurality of region criteria. A processor trains the plurality of training models separately against the plurality of feature vectors. A processor selects a best performing training model for each of the plurality of localized regional source similarity criteria and the plurality of region criteria based on a performance criterion. A processor tests the best performing training model to predict one or more future outcomes.
-
-
-
-
-
-
-
-
-
搜索结果
61 条记录 (耗时 0.031 秒)
