Abstract: A computer implemented method for processing a plurality of medical indication conditions on a computer that includes a processor communicatively coupled to a memory includes obtaining a plurality of predetermined indication conditions which relate to a plurality of parameters, and forming a plurality of conditional segments based on respective values of the plurality of parameters defined in the plurality of predetermined indication conditions. Each conditional segment of the plurality of conditional segments corresponds to a combination of different value ranges of respective parameters of the plurality of parameters. The plurality of predetermined indication conditions includes a measurement item, a reference standard, a time period, a measurement condition, and a measurement manner.

Abstract: Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

Abstract: The present disclosure provides a method, apparatus and system for processing a case management model (CMM). According to an embodiment, there is provided a method for processing a CMM, the method includes: obtaining an existing CMM having a plurality of elements; obtaining a new CMM having at least one element; aligning an element of the new CMM to an element of the existing CMM according to match costs between the element of the new CMM and the plurality of elements of the existing CMM; and fusing the new CMM into the existing CMM based on the match cost between the aligned elements.

Abstract: The present invention discloses a method for transferring a thin film from a first substrate to a second substrate comprising the steps of: providing a transfer structure and a thin film provided on a surface of a first substrate, the transfer structure comprising a support layer and a film contact layer, wherein the transfer structure contacts the thin film; removing the first substrate to obtain the transfer structure with the thin film in contact with the film contact layer; contacting the transfer structure obtained with a surface of a second substrate; and removing the film contact layer, thereby transferring the thin film onto the surface of the second substrate.

Abstract: A computer-implemented method for predicting non-communicable diseases with infectious risk factors using artificial intelligence includes detecting one or more risk factors associated with a non-communicable disease based on a graph associated with person-to-person links, generating a data structure for compactly representing the graph to compute at least one person-to-person distance, and performing a machine learning technique with regularization of the at least one person-to-person distance.

Abstract: The disclosure provides a method for data instance processing. The method includes obtaining a set of data instances collected from a plurality of organizations. Each of the data instances includes at least one record formed in an organization that stores values of a plurality of attributes of the data instance. The method also includes dividing the set of data instances into groups, wherein data instances with conflicting values for the same attribute are divided into different groups. The method further includes subdividing data instances in each of the groups into clusters.

Abstract: A method and system of imputing corrupted sequential data is provided. A plurality of input data vectors of a sequential data is received. For each input data vector of the sequential data, the input data vector is corrupted. The corrupted input data vector is mapped to a staging hidden layer to create a staging vector. The input data vector is reconstructed based on the staging vector, to provide an output data vector. adjusted parameter of the staging hidden layer is iteratively trained until it is within a predetermined tolerance of a loss function. A next input data vector of the sequential data is predicted based on the staging vector. The predicted next input data vector is stored.

Abstract: The present invention discloses a preparation method for a surface molding film of a PVC-based stone plastic composite board, including: surface activation treatment of the PVC-based stone plastic composite board: preparation of an activated putty, coarse roughening of a substrate surface, application and solidification of the activated putty, and fine roughening of the substrate surface; preparation of a PMMA slurry; and surface film forming of the PVC-based stone plastic composite board. The PVC-based stone plastic composite board coated with a PMMA film is obtained by cold pressing and shaping in a mold, tightening up a clamp, solidifying at low temperature, treating at high temperature, cooling and demolding.

Abstract: A method for automatically generating a semantic mapping for a relational database RDB includes obtaining a first semantic mapping from a first RDB to an ontology of linked data; obtaining a schema mapping from the first RDB to a second RDB; and generating a second semantic mapping from the second RDB to the ontology of the linked data based on the first semantic mapping and the schema mapping.

Abstract: A method for automatically generating a semantic mapping for a relational database RDB includes obtaining a first semantic mapping from a first RDB to an ontology of linked data; obtaining a schema mapping from the first RDB to a second RDB; and generating a second semantic mapping from the second RDB to the ontology of the linked data based on the first semantic mapping and the schema mapping.

Abstract: A method for using a plurality of decision engines to produce a single decision comprises: receiving heterogeneous data from the plurality of decision engines comprising a set of class labels from each of the plurality of decision engines, wherein the set of class labels from at least a first decision engine differs from the set of class labels from at least a second decision engine; generating a single set of unified class labels from the heterogeneous data; calculating at least one value corresponding to each of at least a subset of the unified class labels; and performing decision fusion on at least the subset of the unified class labels and corresponding values to produce the single decision.

Abstract: Systems, computer-implemented methods and/or computer program products that facilitate predicting personalized risks based on intrinsic factors and extrinsic factors are provided. In one example, a computer-implemented method comprises: collecting, by a system operatively coupled to a processor, intrinsic factors and extrinsic factors associated with infectious diseases; generating, by the system, a probabilistic model based on the intrinsic factors and extrinsic factors, wherein the model incorporates node characteristics into infection probability; and refining, by the system, the model through concurrently learning respective node thresholds and hidden infection network structure of the model.

Abstract: A method and system of imputing corrupted sequential data is provided. A plurality of input data vectors of a sequential data is received. For each input data vector of the sequential data, the input data vector is corrupted. The corrupted input data vector is mapped to a staging hidden layer to create a staging vector. The input data vector is reconstructed based on the staging vector, to provide an output data vector. adjusted parameter of the staging hidden layer is iteratively trained until it is within a predetermined tolerance of a loss function. A next input data vector of the sequential data is predicted based on the staging vector. The predicted next input data vector is stored.

Abstract: Techniques are provided for training and/or executing, by a system operatively coupled to a processor, a modified random forest model using a process that employs significance of data fields in performing imputation, filtering data records out of sample datasets for generating subtrees, and filtering out subtrees for making predictions.

Abstract: Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

Abstract: Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

Abstract: A computer implemented method for processing a plurality of medical indication conditions on a computer that includes a processor communicatively coupled to a memory includes obtaining a plurality of predetermined indication conditions which relate to a plurality of parameters, and forming a plurality of conditional segments based on respective values of the plurality of parameters defined in the plurality of predetermined indication conditions. Each conditional segment of the plurality of conditional segments corresponds to a combination of different value ranges of respective parameters of the plurality of parameters. The plurality of predetermined indication conditions includes a measurement item, a reference standard, a time period, a measurement condition, and a measurement manner.

Abstract: A computer implemented method identifies guidelines through use of a neural network by a remote guideline server. A client computer transmits instructions to the remote guideline server to retrieve and evaluate multiple candidate guidelines. The remote guideline server utilizes a neural network to identify a string of terms found in each of the multiple candidate guidelines that match one or more strings of terms from a model guideline; to identify a semantic concept of each of the multiple candidate guidelines that matches one or more semantic concepts from the model guideline; and to identify a structural pattern of each of the multiple candidate guidelines that matches one or more structural patterns of the model guideline. The candidate guidelines that match the model guideline are then sent from the remote guideline server to the client computer.

Abstract: Method and apparatus for processing medical data. The method for processing indication conditions includes obtaining a plurality of predetermined indication conditions which relate to a plurality of parameters and forming a plurality of conditional segments based on respective values of the plurality of parameters, which respectively correspond to a plurality of combinations of value ranges of the plurality of parameters. The method for processing patient data includes obtaining distribution information of the patient data in the plurality of conditional segments formed above and determining a matching relationship of patient data with at least one indication condition. The apparatuses correspond to the methods.

Abstract: Disclosed are a computer-implemented method for converting a procedural process model for a process to a hybrid process model, a system and a computer program product. In this method, a plurality of steps of the process which are included in the procedural process model may be clustered selectively according to historical execution information of the plurality of steps, to generate a plurality of candidate cluster set. One candidate cluster set satisfying a first condition may be selected from the plurality of candidate cluster sets. Then, the procedural process model may be converted into the hybrid process model according to the selected candidate cluster set.