Siemens Medical Solutions Usa, Inc.

Other

Cerner Innovation, Inc.

Romer E Rosales

R Bharat Rao

Balaji Krishnapuram

Shipeng Yu

Sriram Krishnan

Faisal Farooq

Harald Steck

Glenn Fung

Bharat R Rao

Radu Stefan Niculescu

Farbod Rahmanian

Abhinay Mahesh Pandya

Marianne Larissa Mueller

William Alexander Landi

Lucian Vlad Lita

Praveen Krishnamurthy

Jennifer Dy

Markus Bundschus

Oksana Yakhnenko

Mark Schmidt

Stanley Chung

Yan Yan

John Weis

Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Healthcare information technology system for predicting or preventing readmissions

A method, including receiving a data source selection from a user or software application, the data source including medical information of a plurality of patients, receiving, from the user or software application, a data pattern that is related to a concept to be explored in the data source, querying the data source to find information that approximately matches the data pattern; and receiving the information from the data source, wherein the information includes unstructured data, assigning a classification to individual parts of the information based on the part's relationship to the data pattern, and outputting the classified information to the user or software application.

Automated patient/document identification and categorization for medical data

Medical related quality of care information is extracted and edited for reporting. Patient records are mined. The mining may include mining unstructured data to create structured information. Measures are derived automatically from the structured information. A user may then edit the measures, data points used to derive the measures, or other quality metric based on expert review. The editing may allow for a better quality report. Tools may be provided to configure reports, allowing generation of new or different reports.

Quality metric extraction and editing for medical data

A system and method for diagnosis and treatment decisions based on information maximization is disclosed. Utilizing patient information as well as clinical records from other patients can reduce the uncertainty in both diagnosis and treatment options. The information maximization may consider additional data such as risk, cost, and comfort in making a proper medical decision.

Guiding differential diagnosis through information maximization

Quality improvement factors in patient care are ranked. Hospital performance is measured, such as a CMS measure. The variables and/or values relative contribution to quality of care is determined using medical records of the hospital. The variables and/or values are ranked according influence of the quality of care result. The ranking is performed by a given medical institution at a desired time rather than based on a broad study. The medical institution may regularly determine variables (e.g., admitting doctor) and/or values (e.g., doctor X) that are relevant to a decreased quality of care. Quality may be regularly improved using a software product.

System and method for ranking quality improvement factors in patient care

A method for sequence tagging medical patient records includes providing a labeled corpus of sentences taken from a set of medical records, initializing generative parameters &#x3b8; and discriminative parameters {tilde over (&#x3b8;)}, providing a functional LL&#x2212;C&#xd7;Penalty, where LL is a log-likelihood function where em=1&#x2212;&#x3a3;p(x|y), e{tilde over (m)}=1&#x2212;&#x3a3;{tilde over (p)}(x|y) are emission probability constraints, tr=1&#x2212;&#x3a3;p(y|y), t{tilde over (r)}=1&#x2212;&#x3a3;{tilde over (p)}(y|y) are transition probability constraints, and extracting gradients of LL&#x2212;C&#xd7;Penalty with respect to the transition and emission probabilities and solving &#x3b8;*,{tilde over (&#x3b8;)}*that maximize LL&#x2212;C&#xd7;Penalty, initializing a new iteration with &#x3b8;*,{tilde over (&#x3b8;)}* and incrementing C and repeating until solutions have converged, where parameters &#x3b8;,{tilde over (&#x3b8;)} are the probabilities that a new sentence X' is labeled as Y&#x2032;.

System and method for text tagging and segmentation using a generative/discriminative hybrid hidden markov model

A method for multiple-label data analysis includes: obtaining labeled data points from more than one labeler; building a classifier that maximizes a measure relating the data points, labels on the data points and a predicted output label; and assigning an output label to an input data point by using the classifier.

Learning classifiers for multiple-label data analysis

A method, including receiving multi-labeler data that includes data points labeled by a plurality of labelers; building a model from the multi-labeler data, wherein the model includes an input variable that corresponds to the data points, a label variable that corresponds to true labels for the data points, and variables for the labels given by the labelers; and executing the model, in response to receiving new data points, to determine a level of expertise of the labelers for the new data points.

Automatic labeler assignment using a model built from multi-labeler data

Computer instructions for guiding differential diagnosis through information maximization

A medical concept is learned about or inferred from a medical transcript. A probabilistic model is trained from medical transcripts. For example, the problem is treated as a graphical model. Discrimitive or generative learning is used to train the probabilistic model. A mutual information criterion can be employed to identify a discrete set of words or phrases to be used in the probabilistic model. The model is based on the types of medical transcripts, focusing on this source of data to output the most probable state of a patient in the medical field or domain. The learned model may be used to infer a state of a medical concept for a patient.

Learning or inferring medical concepts from medical transcripts using probabilistic models with words or phrases identification

Medical ontology information is used for mining and/or probabilistic modeling. A domain knowledge base may be automatically or semi-automatically created by a processor from a medical ontology. The domain knowledge base, such as a list of disease associated terms, is used to mine for corresponding information from a medical record. The relationship of different terms with respect to a disease may be used to train a probabilistic model. Probabilities of a disease or chance of indicating the disease are determined based on the terms from a medical ontology. This probabilistic reasoning is learned with a machine from ontology information and a training data set.

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Downingtown, PA, United States of America

Romer E Rosales