Siemens Medical Solutions Usa, Inc.

Harald Steck

R Bharat Rao

Romer E Rosales

Sriram Krishnan

Abhinay Mahesh Pandya

Marcos Salganicoff

Glenn Fung

Radu Stefan Niculescu

Philippe Lambin

Anna Jerebko

William Alexander Landi

Maneesh Dewan

Farbod Rahmanian

Cary Dehing-Oberije

Praveen Krishnamurthy

Phan Hong Giang

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 method for predicting survival rates of medical patients includes providing a set D of survival data for a plurality of medical patients, providing a regression model having an associated parameter vector &#x3b2;, providing an example xof a medical patient whose survival probability is to be classified, calculating a parameter vector {circumflex over (&#x3b2;)} that maximizes a log-likelihood function of &#x3b2; over the set of survival data, l(&#x3b2;|D), wherein the log likelihood l(&#x3b2;|D) is a strictly concave function of &#x3b2; and is a function of the scalar x&#x3b2;, calculating a weight wfor example x, calculating an updated parameter vector &#x3b2;* that maximizes a function l(&#x3b2;|D&#x222a;{(y,x,w)}), wherein data points (y,x,w) augment set D, calculating a fair log likelihood ratio &#x3bb;from {circumflex over (&#x3b2;)} and &#x3b2;* using &#x3bb;=&#x3bb;(&#x3b2;*|x)+sign(&#x3bb;({circumflex over (&#x3b2;)}|x)){l({circumflex over (&#x3b2;)}|D)&#x2212;l(&#x3b2;*|D)}, and mapping the fair log likelihood ratio &#x3bb;to a fair price y, wherein said fair price is a probability that class label yfor example xhas a value of 1.

System and method for medical predictive models using likelihood gamble pricing

According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a na&#xef;ve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the na&#xef;ve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio.

System and method for lesion detection using locally adjustable priors

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.

Medical ontologies for computer assisted clinical decision support

A predictor of medical treatment outcome is developed and applied. A prognosis model is developed from literature. The model is determined by reverse engineering the literature reported quantities. A relationship of a given variable to a treatment outcome is derived from the literature. A processor may then use individual patient values for one or more variables to predict outcome. The accuracy may be increased by including a data driven model in combination with the literature driven model.

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Phoenixville, PA, United States of America

Harald Steck