Conduent Business Services, LLC

Vijay Huddar

Vaibhav Rajan

Sakyajit Bhattacharya

Bhupendra Singh Solanki

Shourya Roy

Prathosh Aragulla Prasad

Stephen Craig Fullerton

Abhishek Sengupta

Katerina Sinclair

Harsh Shrivastava

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.

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Bijapur, India

Vijay Huddar