Conduent Business Services, LLC

Xerox Corporation

Vaibhav Rajan

Sakyajit Bhattacharya

Vijay Huddar

Prathosh Aragulla Prasad

Bhupendra Singh Solanki

Stephen Craig Fullerton

Lavanya Sita Tekumalla

Abhishek Sengupta

Katerina Sinclair

Koustuv Dasgupta

Abhishek Tripathi

Harsh Shrivastava

Satya Narayan Shukla

Shourya Roy

Nischal Murthy Piratla

Narayanan Unny Edakunni

Kovendhan Ponnavaikko

Atul Singh

Karanam Saraschandra

Ranjan Shetty K

Amith Sitaram

Vivek G Raman

James D Kirkendall

According to embodiments illustrated herein, there is provided a system for predicting a health condition of a patient. The system further includes one or more processors configured to separately cluster data points from a set of medical records associated with a first class of patients and a second class of patients. A similarity value of each of the clustered data points with respect to a pre-selected subset of data points that represents landmark points may be determined, using a parameterized similarity measure. One or more classifiers are trained using the determined similarity value of each data point. The trained one or more classifiers are adapted to learn one or more parameters of the parameterized similarity measure during the training. An occurrence of the health condition of the patient may be predicted based on the trained one or more classifiers and one or more medical records of the patient.

System and method for predicting health condition of a patient

A method, non-transitory computer readable medium and apparatus for predicting mortality of a current patient are disclosed. For example, the method includes receiving data associated with a plurality of different patients with known mortality outcomes, wherein the data includes a subset of data for each one of a plurality of different measurement timepoints for each one of the plurality of different patients, calculating n number of classifiers, wherein n is equal to a number of the plurality of different measurement timepoints, receiving data associated with the current patient at an i-th measurement timepoint, predicting the current patient has a high mortality risk based on an output of the i-th classifier of the n number of classifiers and transmitting a signal to a health administration server to cause an alarm to be generated in response to the high mortality risk that is predicted.

Method and apparatus for predicting mortality of a patient using trained classifiers

According to embodiments illustrated herein, there is provided a system for predicting a health condition of a first patient. The system includes a document processor configured to extract one or more headings from one or more medical records of the first patient based on one or more predefined rules. The document processor is further configured to extract one or more words from one or more phrases written under each of the extracted one or more headings, wherein the one or more phrases correspond to documentation of the observation of the first patient by a medical attender. The system further includes one or more processors configured to predict the health condition of the first patient based on a count of the one or more words in historical medical records and the one or more medical records.

A method and a system are provided for processing electronic medical records for predicting a health condition of a patient. The method may determine a first set of datasets of a first patient based on one or more first electronic medical records. The method may extract one or more second sets of datasets of one or more second patients from a database server based on the first set of datasets. The method may generate one or more bipartite graphs based on the first set of datasets and the one or more second sets of datasets. The method may determine a set of edges from the one or more edges based on a matching score in each bipartite graph. The method may further predict the health condition of the first patient based on at least the matching score associated with each of the one or more bipartite graphs.

Method and system to process electronic medical records for predicting health conditions of patients

A method and a system for predicting admission of a human subject to a first ward in a medical center are disclosed. A patient dataset is generated based on at least a measure of one or more physiological parameters associated with one or more first human subjects and a first information pertaining to the admission of each of the one or more first human subjects to the first ward. For a first human subject of the one or more first human subjects, a first score at each of the one or more first time instants is determined. Further, one or more second time instants from the one or more first time instants are identified. Further, a second score at each of the one or more second time instants is determined. In an embodiment, the first classifier is trained based on at least the second score, and the first information.

Method and system for predicting admission of a human subject to a ward in a medical center

What is disclosed is a system and method for forecasting and imputing an unknown vital measurement of a patient. Temporally successive patient vital measurements are received which comprise irregularly sampled observations {y, . . . , y}, where ydenotes the jobservation at time t, and N is the number of samples. The vital measurements are then provided to a model trained using historical data of patient vital measurements. The model generates a parameter set &#x3b8;=(A,B,C), where A is a state transition matrix, B is a control matrix, and C is a matrix which maps state-space variables to observation variables. The parameters are used to obtain state-space variable zwhich, in turn, is used to forecast an unknown observation yor to impute an unknown observation y, where 1&#x3c;t&#x3c;N. The historical data is then updated with the forecasted observation yor the imputed unknown observation y.

Forecasting a patient vital measurement for healthcare analytics

Disclosed are embodiments of method and system to predict health condition of a human subject. The method comprises receiving historical human-subject related data including records corresponding to multiple data views. The method estimates one or more latent variables based on: a first value indicative of count of records in a cluster, a second value indicative of count of records, and a third value indicative of a parameter utilizable to predict a fourth value. The fourth value corresponds to selection probability of a D-vine pair copula family, of a D-vine mixture model, utilizable to model a cluster. The method generates the D-vine mixture model based on the estimated one or more latent variables. The method further comprises receiving multi-view data of a second human subject and predicting health condition of the second human subject based on the multi-view data using a classifier trained based on the estimated latent variables.

Method and system for data processing to predict health condition of a human subject

Disclosed are embodiments of methods and systems for predicting mortality of a first patient. The method comprises categorizing a historical data into a first category and a second category. The method further comprises determining a first test parameter and a second test parameter based on at least one of a sample data of a first patient and the historical data corresponding to at least one of the first category and the second category. The method further comprises determining a probability score based on a cumulative distribution of at least one of the first test parameter and the second test parameter. The method further comprises categorizing the sample data in one of the first category and the second category based on the probability score. Further, the method comprises predicting the mortality of the first patient based on at least the categorization of the sample data of the first patient.

Methods and systems for predicting mortality of a patient

Disclosed are embodiments of methods and systems for predicting a health condition of a first human subject. The method comprises receiving a measure of one or more physiological parameters associated with the first human subject. The method estimates one or more latent variables based on a first count indicative of a number of the plurality of d-vines, a second count indicative of a number of the one or more records, a first value that is representative of a number of the one or more records clustered into a d-vine from the plurality of d-vines, and a second value that is representative of a parameter utilizable to predict a third value. The method generates the plurality of d-vines based on the estimated one or more latent variables. The method predicts health condition of the first human subject by utilizing a trained classifier based on the estimated one or more latent variables.

Methods and systems for predicting a health condition of a human subject

Disclosed are embodiments of methods and systems for predicting a health condition of a first human subject. The method comprises extracting a historical data including physiological parameters of second human subjects. Thereafter, a first distribution of a first physiological parameter is determined based on a marginal cumulative distribution of a rank transformed historical data. Further, a second distribution of a second physiological parameter is determined based on the first distribution and a first conditional cumulative distribution of the rank transformed historical data. Further, a latent variable is determined based on the first and the second distributions. Thereafter, one or more parameters of at least one bivariate distribution, corresponding to a D-vine copula, are estimated based on the latent variable. Further, a classifier is trained based on the D-vine copula. The classifier is utilizable to predict the health condition of the first human subject based on his/her physiological parameters.

Systems and methods of modeling irregularly sampled time series signals with unknown temporal dynamics are disclosed wherein a temporal difference variable (TDV) is introduced to model irregular time differences between subsequent measurements. A hierarchical model is designed comprising two linear dynamical systems that model the effects of evolving TDV on temporal observations. All the parameters of the model, including the temporal dynamics, are statistically estimated using historical data.

System and method of modeling irregularly sampled temporal data using Kalman filters

Disclosed are the embodiments for creating a model capable of identifying one or more clusters in a healthcare dataset. An input is received pertaining to a range of numbers. Each number in the range of numbers is representative of a number of clusters in the healthcare dataset. For a cluster, one or more first parameters of a distribution associated with the cluster are estimated. Thereafter, a threshold value is determined based on the one or more first parameters. An inverse cumulative distribution of each of one or more n-dimensional variables in the healthcare dataset is determined. The one or more first parameters are updated to generate one or more second parameters based on the estimated inverse cumulative distribution. A model is created for each number in the range of numbers based on the one or more second parameters.

Methods and systems for analyzing healthcare data

Disclosed are methods and systems for classifying one or more human subjects in one or more categories indicative of a health condition of the one or more human subjects. The method includes categorizing one or more parameters of each of the one or more human subjects in one or more data views based on a data type of each of the one or more parameters. A data view corresponds to a first data structure storing a set of parameters categorized in the data view, associated with each of the one or more human subjects. The one or more data views are transformed to a second data structure representative of the set of parameters across the one or more data views. Thereafter, a classifier is trained based on the second data structure, wherein the classifier classifies the one or more human subjects in the one or more categories.

Methods and systems for predicting health condition of human subjects

A method and system for recommending one or more crowdsourcing platforms from a plurality of crowdsourcing platforms to a requester is disclosed. The method includes receiving values corresponding to one or more parameters of one or more tasks from the requester. In response to the received values recommending the one or more crowdsourcing platforms to the requester based on the values and one or more statistical models maintained for the one or more crowdsourcing platforms, wherein the one or more statistical models corresponds to mathematical models representing performances of the one or more crowdsourcing platforms over a period of time.

Method and system for recommending crowdsourcing platforms

The disclosed embodiments illustrate methods and systems for crowdsourcing a software development project. The method includes segregating the software development project into one or more modules based on at least one configuration file. The at least one configuration file is deterministic of at least a set of dependencies between the one or more modules. Further, a task corresponding to at least one module from the one or more modules is created. The task is crowdsourced to one or more crowdworkers. Thereafter, a source code of the at least one module, received as a response for the task, is integrated with one or more source codes of remaining of the one or more modules to generate an integrated software package based on said at least one configuration file. Further, the integrated software package is validated by performing integration testing of the integrated software package.

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Bengaluru, India

Vaibhav Rajan