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    METHOD FOR DETERMINING A DISEASE PROGRESSION AND SURVIVAL PROGNOSIS FOR PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS
    36.
    发明公开
    METHOD FOR DETERMINING A DISEASE PROGRESSION AND SURVIVAL PROGNOSIS FOR PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS 审中-公开

    公开(公告)号：US20230290513A1

    公开(公告)日：2023-09-14

    申请号：US18017196

    申请日：2020-07-22

    Applicant: UNIVERSITA’ DEGLI STUDI DI PADOVA ,  UNIVERSITA’ DEGLI STUDI DI TORINO ,  AZIENDO OSPEDALIERA UNIVERSITARIA MODENA ,  NEMO LAB SRL ,  THE MEDICAL RESEARCH, INFRASTRUCTURE AND HEALTH SERVICES FUND OF THE TEL-AVIV MEDICAL CENTER ,  ISTITUTI CLINICI SCIENTIFICl MAUGERI ,  HADASIT MEDICAL RESEARCH SERVICES AND DEVELOPMENT LTD.

    Inventor： Barbara DI CAMILLO ,  Alessandro ZANDONA' ,  Sebastian DABERDAKU ,  Erica TAVAZZI ,  Adriano CHIO' ,  Rosario VASTA ,  Andrea CALVO ,  Cristina MOGLIA ,  Federico CASALE ,  Fabrizio D'OVIDIO ,  Jessica MANDRIOLI ,  Christian LUNETTA ,  Vivian DRORY ,  Gabriele MORA ,  Marc GOTKINE

    IPC: G16H50/30 ,  G16B20/20 ,  G06N7/01

    CPC classification number: G16H50/30 ,  G16B20/20 ,  G06N7/01

    Abstract: A method is described for determining a disease progression and survival prognosis, at a succession of prediction times, for patients suffering from amyotrophic lateral sclerosis (ALS). The method comprises a step of defining a set of variables associated with the onset and progression of amyotrophic lateral sclerosis, comprising a first group of variables associated with the onset of amyotrophic lateral sclerosis (comprising at least the variables “patient sex”, “disease onset age”, “disease onset site”), a second group of dynamic time variables (comprising at least the variable “time elapsed since disease onset”), a third group of dynamic functional variables (comprising at least one of the variables breathing, swallowing, communicating, walking/self-care or at least one variable of a functional progression and/or severity scale of amyotrophic lateral sclerosis), and further at least one variable associated with survival. The method further provides for encoding by means of a Dynamic Bayesian Network, using at least one trained algorithm, a plurality of probabilistic conditional dependence relationships, in which each relationship is a probabilistic conditional dependence relationship between two of the aforesaid variables. The aforesaid prediction times are defined so that each prediction time belongs to a respective time interval in which the conditional dependence relationships between the variables are stationary. The method further involves describing the Dynamic Bayesian Network, using at least one trained algorithm, by means of a corresponding graph, comprising said variables as nodes and comprising topological connections oriented between nodes corresponding to variables among which a probabilistic conditional dependence is identified. In the graph, given a node, the connections entering it show a conditional probability of the value assumed by the variable associated with such node, in a given prediction time, depending on the values assumed, in a prior prediction time, from the variables associated with the nodes from which such connections originate. The method further comprises the steps of entering, for each of the defined variables, data acquired at a given acquisition time relating to the situation of a specific patient; and calculating, by electronic processing and/or calculating means, on the basis of the Dynamic Bayesian Network and the graph, and starting from the aforesaid acquired data, the values of each of the defined variables, at one or more prediction times following the acquisition time. Finally, the method involves obtaining, in a given prediction time, disease progression prognosis results on the basis of the values of one or more of the variables of the third group calculated in such prediction time; and the survival prognosis results on the basis of the value of at least one variable associated with survival, calculated at such prediction time.

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