SYSTEMS AND METHODS FOR OPTIMIZING MEDICAL INTERVENTIONS USING PREDICTIVE MODELS

    公开(公告)号:WO2022261622A2

    公开(公告)日:2022-12-15

    申请号:PCT/US2022/072784

    申请日:2022-06-06

    摘要: A computer implemented method for prescribing optimized medical interventions includes retrieving a patient's updated electronic medical record (EMR) and mapping the diagnosis of the patient to a medical treatment database to select a plurality of likely medical intervention choices based on a score exceeding a defined threshold score. The method includes determining a rank order of the selected plurality of medical intervention choices by comparing simulation outcomes for each choice executed by the medical predictive algorithm, on respective choices among each of the selected likely medical interventions. The method also includes receiving by the patient's physician or patient's electronic medical record database, a rank order of recommended medical intervention choices including possible options and associated metrics based on an accepted level of simulated outcome.

    SYSTEMS AND METHODS FOR CONTINUOUS CANCER TREATMENT AND PROGNOSTICS

    公开(公告)号:WO2022232850A1

    公开(公告)日:2022-11-03

    申请号:PCT/US2022/072068

    申请日:2022-05-02

    发明人: MORIN, Olivier

    摘要: Oncology faces a digital chasm in its quest for personalized treatments. Despite the adoption of electronic health records (EHR), most hospitals are ill-equipped for data science research. Embodiments herein describe a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence to address unmet clinical needs. Embodiments describe systems and methods for improved cancer prognostics, including by obtaining electronic medical records and performing natural language processing thereon. Additional embodiments apply term frequency inverse document frequency to identify terms that are predictive for cancer survival. Additional embodiments are capable of performing in silico clinical trials based on information comprised in a collection of health records.

    临床路径的挖掘方法、装置、设备及存储介质

    公开(公告)号:WO2022134476A1

    公开(公告)日:2022-06-30

    申请号:PCT/CN2021/097546

    申请日:2021-05-31

    IPC分类号: G16H70/20 G16H50/70 G06K9/62

    摘要: 一种临床路径的挖掘方法、装置、设备及存储介质,其中方法包括:获取目标病种的待分析的临床数据,待分析的临床数据是根据目标病种对应的多个患者的历史临床数据得到的临床数据(S1);对待分析的临床数据进行矩阵构建,得到待分析的临床数据矩阵(S2);对待分析的临床数据矩阵按收费项目进行患者行为的聚类,得到待分析的临床数据对应的患者行为集合(S3);采用患者行为集合对待分析的临床数据的收费项目进行患者行为的匹配和替换,得到待分析的临床数据对应的临床患者行为序列(S4);根据临床患者行为序列进行时序频繁集挖掘,得到目标病种对应的多个目标临床路径(S5)。从而提升了挖掘得到的临床路径的灵活性和可解释性。

    VIABLE PATIENT HEALTH SYSTEMS
    6.
    发明申请

    公开(公告)号:WO2022053853A1

    公开(公告)日:2022-03-17

    申请号:PCT/IB2020/058480

    申请日:2020-09-11

    申请人: TREMBLAY, Laura

    发明人: TREMBLAY, Laura

    摘要: Attempts to create health system sustainability have failed to offset continually rising fiscal pressures, let alone create health system viability. Viable patient health systems comprise sustained, evenly counterbalanced upstream and downstream health system components and operations within an entire primary health care continuum; where patient wellness is a powerful link between that built upstream and used sparingly, downstream. Standardized quantification of patient wellness, informs accurate health measurement in economic evaluations of health interventions, including appropriate technology interventions. Equal patient access upstream and downstream, enables equal access to wellness reserves in both directions, supporting sustained maintenance of patient health system viability. Coordinated, linked networks upstream and downstream, facilitate improved social determinants of health, particularly within vulnerable, marginalized populations, a crucial common factor in jurisdictions that use viable patient health systems; wherein economic patterns in health and non health sectors are regularly monitored and evaluated, informing partnered economies.

    MULTILINGUAL INTERFACE FOR THREE-STEP PROCESS FOR MIMICKING PLASTIC SURGERY RESULTS

    公开(公告)号:WO2022015334A1

    公开(公告)日:2022-01-20

    申请号:PCT/US2020/042662

    申请日:2020-07-17

    申请人: HAYMAN, Hillary

    发明人: HAYMAN, Hillary

    摘要: A computer program product comprises a non-transitory computer readable storage that has a computer readable program stored thereon. When executed on a computer, the computer readable program causes the computer to select, with a processor, a multilingual graphical user interface template corresponding to a three-step skincare treatment process. The multilingual graphical user interface template has a layout of all of the objects present in a multilingual graphical user interface without any content displaying a human-spoken language. Furthermore, the computer is caused to determine, with the processor, a preferred spoken language of a user operating a computing device. Additionally, the computer is caused to automatically select, from a multilingual object database, one or more audiovisual objects composed in the preferred spoken language and one or more menu objects composed in the preferred spoken language.

    PROCEDE ET SYSTEME INFORMATISE DE RECHERCHE BIBLIOGRAPHIQUE

    公开(公告)号:WO2021209581A1

    公开(公告)日:2021-10-21

    申请号:PCT/EP2021/059852

    申请日:2021-04-16

    申请人: ODROA

    发明人: PAYSAN, Patrick

    摘要: Un module interface (20) d'un serveur (19) reçoit des données relatives à un test biologique. Le serveur élabore une requête à partir des données relatives à un test biologique. Un module informatisé de recherche (26) reçoit la requête. Le module informatisé de recherche (26) recherche dans une base de données de publications labellisées (23) des publications pertinentes correspondant à la requête. Une identification des publications pertinentes identifiées est envoyé au module d'interface.

    跨医疗数据源的网络表示学习算法

    公开(公告)号:WO2021197491A1

    公开(公告)日:2021-10-07

    申请号:PCT/CN2021/085611

    申请日:2021-04-06

    IPC分类号: G16H50/70 G16H70/20 G06N3/02

    摘要: 一种跨医疗数据源的网络表示学习算法,包括:S1.生成包括源网络和目标网络的医疗网络数据;S2.分别从源网络和目标网络随机采样设定数量的节点,采集节点的数量与所述医疗网络的度数相关;S3.得到一个L层的神经网络,并对每一层分别计算源网络和目标网络的结构特征和表达特征,计算源网络和目标网络的网络特征之间的距离损失;S4.得到源网络在L层神经网络的输出,并根据分类损失和距离损失计算损失值,根据反向传播算法更新算法的参数;S5重复步骤S2-S4,直至整个算法收敛,使得算法对于疾病分类的准确率在多个迭代内不再上升。上述方法考虑了不同医院数据源之间的数据分布不一致问题,通过提取网络的结构信息及节点属性信息、最小化特征距离弥补信息损失,有着广阔的应用空间。