Siemens Medical Solutions Usa, Inc.

Cerner Innovation, Inc.

Other

International Business Machines Corporation

Siemens Aktiengesellschaft

Mevis Breastcare Gmbh & Co. Kg

Cerner Health Services, Inc.

Balaji Krishnapuram

Faisal Farooq

Glenn Fung

Shipeng Yu

R Bharat Rao

Jan DeHaan

Romer E Rosales

Joseph Marcus Overhage

Bharat R Rao

Farbod Rahmanian

Sriram Krishnan

John Haley

Murat Dundar

Marie Angelopoulos

Vijay Krishnarao Naik

Shilpa N Mahatma

Philippe Lambin

Alok Gupta

Vikram Anand

Carl J G Evertsz

Vikas C Raykar

Bradly G Wouters

Michal Ozery-Flato

Adrian Campbell Begg

Leemor M Yuravlivker

Volkan Vural

Dennis O'Dell

Christoffel Theodorus Anthonius Evelo

Kasper M Rouschop

Michael Gaston Magagnin

Arie Van Erk

Dimitry S A Nuyten

Maud H W Starmans

Renaud G Seigneuric

Markus Bundschus

John Weis

Stanley Chung

Ignazio Palmisano

Nicholas Drummond

Luigi Iannone

Sebastian Philipp Brandt

Brendan Shea

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 mechanism is provided in a data processing system to implement a feature extraction tool for graphical user interface based feature extraction. The feature extraction tool receives selection by a user of a dataset from which features are to be extracted. The feature extraction tool loads a plurality of feature definitions. The feature extraction tool generates a graphical user interface that allows the user to add features from the plurality of features to a feature file. The feature extraction tool presents the graphical user interface to the user and receives user selection of at least one feature to be added to the feature file. The feature extraction tool generates the feature file based on the user selection of the at least one feature.

Graphical user interface based feature extraction application for machine learning and cognitive models

Mapping of semantics in healthcare may involve accessing first transaction data in a first database, the first transaction data corresponding to a collection of a first number of fields defined for a condition using a first semantic system to store information and calculating a first distribution of information in the first transaction data. Mapping may also involve accessing second transaction data in a second database, the second transaction data corresponding to a second semantic system different than the first semantic system and the second database comprising a second number of fields using the second semantic system to store information, and calculating a second distribution of information in the second transaction data. The distributions may then be compared and a map relating the semantic systems may be generated and used to communicate between the first and second semantic systems.

Automated mapping of service codes in healthcare systems

Adaptive medical data collection for medical entities may involve managing content by receiving data indicating a context, identifying at least one application or knowledge base associated with the context, designating the identified application or knowledge base as active, and accessing the active application or knowledge base to provide information at an interface point for a medical professionals and a patient.

Adaptive medical documentation system

Adaptive medical data collection for medical entities may involve triggering an analysis of electronic records in response to information input into an Electronic Medical Record (EMR) of a patient. Determining a potential condition for the patient based on the analysis. Identifying additional information indicated as relevant to the potential condition of the patient, and generating a request for the identified additional information.

Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.

Mapping of semantics in healthcare may involve accessing first transaction data of a first healthcare entity in a first database, the first transaction data corresponding to a collection of a first number of fields defined for a condition using a first semantic system to store information and calculating a first distribution of information in the first transaction data. Mapping may also involve accessing second transaction data of a second healthcare entity in a second database, the second transaction data corresponding to a second semantic system different than the first semantic system and the second database comprising a second number of fields using the second semantic system to store information, the second number of fields larger than the first number of fields and calculating a second distribution of information in the second transaction data. The distributions may then be compared and a map relating the semantic systems may be generated.

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

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 training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of images, each image including one or more candidate regions that have been identified as suspicious by a computer aided diagnosis system. Each image has been manually annotated to identify malignant regions. Multiple instance learning is applied to train a classifier to classify suspicious regions in a new image as malignant or benign by identifying those candidate regions that overlap a same identified malignant region, grouping each candidate region that overlaps the same identified malignant region into a same bag, and maximizing a probability wherein N is a number of bags, pis a probability of bag i containing a candidate region that overlaps with an identified malignant region, and yis a label where a value of 1 indicates malignancy and 0 otherwise.

System and method for multiple-instance learning for computer aided diagnosis

A method of training a classifier for computer aided detection of digitized medical image, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, where each region-of-interest has been labeled as either malignant or healthy. The training uses candidates that are spatially adjacent to each other, modeled by a 'bag', rather than each candidate by itself. A classifier is trained on the plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to a feature sample, is correctly classified according to the label of the associated region-of-interest, rather than a large set of discrete constraints where at least one instance in each bag has to be correctly classified.

System and method for multiple instance learning for computer aided detection

The present invention provides methods and compositions for predicting patient responses to cancer treatment using hypoxia gene signatures. These methods can comprise measuring in a biological sample from a patient the levels of gene expression of a group of the genes designated herein. The present invention also provides for microarrays that can detect expression from a group of genes.

Gene signature of early hypoxia to predict patient survival

A method and system correlate candidate information and provide batch classification of a number of related candidates. The batch of candidates may be identified from a single data set. There may be internal correlations and/or differences among the candidates. The candidates may be classified taking into consideration the internal correlations and/or differences. The locations and descriptive features of a batch of candidates may be determined. In turn, the locations and/or descriptive features determined may used to enhance the accuracy of the classification of some or all of the candidates within the batch. In one embodiment, the single data set analyzed is associated with an internal image of patient and the distance between candidates is accounted for. Two different algorithms may each simultaneously classify all of the samples within a batch, one being based upon probabilistic analysis and the other upon a mathematical programming approach. Alternate algorithms may be used.

Using candidates correlation information during computer aided diagnosis

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King of Prussia, PA, United States of America

Balaji Krishnapuram