Siemens Medical Solutions USA, Inc.

Cerner Innovation, Inc.

Siemens Corporation

Siemens Corporate Research, Inc.

Radu Stefan Niculescu

R Bharat Rao

Sathyakama Sandilya

Arun Kumar Goel

William Alexander Landi

Farbod Rahmanian

Thomas R Warrick

Christopher Jude Amies

William Michael Hasling

Todd W Fritsche

Vikram Anand

Brian Thornton

Bette Jane Reese

Lucian Vlad Lita

Muthu Premananth

Daniel Antohe

Camille Anne Egan

Romer E Rosales

Sriram Krishnan

Harald Steck

Bharat R Rao

Brian Berenbach

Gilberto Augusto Matos

Harm Jacob Scherpbier

Maleeha Qazi

Vasco Calais Pedro

Oksana Yakhnenko

By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Gap in care determination using a generic repository for healthcare

The present invention provides a data mining framework for mining high-quality structured clinical information. The data mining framework includes a data miner that mines medical information from a computerized patient record (CPR) based on domain-specific knowledge contained in a knowledge base. The data miner includes components for extracting information from the CPR, combining all available evidence in a principled fashion over time, and drawing inferences from this combination process. The mined medical information is stored in a structured CPR which can be a data warehouse.

Patient data mining

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 web-based system for visual construction of logical rules includes a server, a network, and client operatively connected to the server via the network. The server includes a database and a search engine. The client includes a web-based visual rule building application including selectable windows for displaying and visually editing terms, logical operators, logical rules for storage in the database. The logical rules are generated by visually selecting at least one of the terms and logical operators from the windows. The server may further include a search engine configured to perform at least one of a direct search or a contextual search for an entered query string in records stored in the database and the client may include a visual interface for displaying results of the searches. The search results generated by the search engine may be stored as terms in the database for subsequent rule generation.

Contextual searching of electronic records and visual rule construction

A method for creating and searching medical ontologies includes providing a semi-structured information source comprising a plurality of articles linked to each other, each article having one or more sections and each article is associated with a concept, creating a directed unlabeled graph representative of the information source, providing a plurality of labels, labeling a subset of edges, and assigning each unlabeled edge an equal probability of being assigned one of the labels. For each node, the probability of each outgoing edge is updated by smoothing each probability by an overall probability distribution of labels over all outgoing edges of each node, and the probability of each incoming edge is updated the same way. A label with a maximum probability is assigned to an edge if said maximum probability is greater than a predetermined threshold to create a labeled graph.

System and method for creating and searching medical ontologies

The present invention provides a graphical user interface for presentation, exploration and verification of patient information. In various embodiments, a method is provided for browsing mined patient information. The method includes selecting patient information to view, at least some of the patient information being probabilistic, presenting the selected patient information on a screen, the selected patient information including links to related information. The selected patient information may include elements, factoids, and/or conclusions. The selected patient information may include an element linked to unstructured information. For example, an element linked to a note with highlighted information may be presented. Additionally, the unstructured information may include medical images and waveform information.

Patient data mining, presentation, exploration, and verification

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 technique is provided for automatically generating performance measurement information. At least some of the obtained performance measurement information may be derived from unstructured data sources, such as free text physician notes, medical images, and waveforms. The performance measurement may be sent to a health care accreditation organization. The health care accreditation organization can use the performance measurement to evaluate a health care provider for its quality of patient care. Alternatively, performance measurement information can be provided directly to consumers.

Patient data mining for automated compliance

Systems and methods for automated processing of medical information in electronic patient medical record databases, wherein billing information (e.g., diagnosis codes, procedural codes) is automatically extracted from electronic patient medical records through comprehensive analysis of clinical information included in the patient medical records using a knowledge base of domain-specific criteria. The extracted billing information can be automatically processed for purposes of, e.g., medical claims correction, medical claims billing, quality assurance of recorded billing information, or claim reimbursement tracking.

Systems and methods for automated extraction and processing of billing information in patient records

A method and system for determining patient states is provided. The method includes the steps of data mining a patient record using a domain knowledge base relating to a disease of interest; inputting the mined data into a model of the disease of interest; and determining a state of the patient based on the model. The system includes a data miner for mining information from a patient record using a domain knowledge base relating to a disease of interest; and a processor for creating a patient model of the disease of interest, processing the mined data in the model to determine a current state of the patient and future states for different courses of treatment, and recommending a therapy based on the determined future state.

Patient data mining for diagnosis and projections of patient states

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Malvern, PA, United States of America

Radu Stefan Niculescu