Siemens Medical Solutions Usa, Inc.

Cerner Innovation, Inc.

Other

Cerner Health Services, Inc.

Shipeng Yu

Glenn Fung

Balaji Krishnapuram

Faisal Farooq

R Bharat Rao

Romer E Rosales

Bharat R Rao

Jan DeHaan

Philippe Lambin

Joseph Marcus Overhage

Sriram Krishnan

Farbod Rahmanian

Cary Dehing-Oberije

Dirk De Ruysscher

Andreas Lubbertus Aloysius Johannes Dekker

John Haley

Vikram Anand

Stanley Chung

John Weis

Markus Bundschus

Steven Florian Petit

Kartik Jayasurya Komati

Hugo J W L Aerts

Claudia Offermann

Michel Oellers

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

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 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.

Adaptive medical documentation system

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.

Functional imaging information is used to determine a probability of residual disease given a treatment. The functional imaging information shows different characteristic levels for different regions of the tumor. The probability is output for planning use and/or used to automatically determine dose by region. Using the probability, the dose may be distributed by region so that some regions receive a greater dose than other regions. This distribution by region of dose more likely treats the tumor with a same dose, allows a lesser dose to sufficient treat the tumor, and/or allows a greater dose with a lesser or no increase in risk to normal tissue. The dose plan may account for personalized tumors as each patient may have distinct tumors. Probability of dose application accuracy may also be used, so that a combined treatment probability allows efficient dose planning.

Dose distribution modeling by region from functional imaging

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 computer-implemented method for privacy-preserving data mining to determine cancer survival rates includes providing a random matrix B agreed to by a plurality of entities, wherein each entity i possesses a data matrix Aof cancer survival data that is not publicly available, providing a class matrix Dfor each of the data matrices A, providing a kernel K(A, B) by each of said plurality of entities to allow public computation of a full kernel, and computing a binary classifier that incorporates said public full kernel, wherein said classifier is adapted to classify a new data vector according to a sign of said classifier.

System and method for privacy preserving predictive models for lung cancer survival analysis

Knowledge-based interpretable predictive modeling is provided. Expert knowledge is used to seed training of a model by a machine. The expert knowledge may be incorporated as diagram information, which relates known causal relationships between predictive variables. A predictive model is trained. In one embodiment, the model operates even with a missing value for one or more variables by using the relationship between variables. For application, the model outputs a prediction, such as the likelihood of survival for two years of a lung cancer patient. A graphical representation of the model is also output. The graphical representation shows the variables and relationships between variables used to determine the prediction. The graphical representation is interpretable by a physician or other to assist in understanding.

Knowledge-based interpretable predictive model for survival analysis

Modeling of prognosis of survivability, side-effect, or both is provided. For example, RILI is predicted using bullae information. The amount, volume or ratio of Bullae, even alone, may indicate the likelihood of complication, such as the likelihood of significant (e.g., stage 3) pneumonitis. As another example, RILI is predicted using uptake values of an imaging agent. Standardized uptake from a functional image (e.g., FDG uptake from a positron emission image), alone or in combination with other features, may indicate the likelihood of side-effect. In another example, survivability, such as two-year survivability, is predicted using blood biomarkers. The characteristics of a patient's blood may be measured and, alone or in combination with other features, may indicate the likelihood of survival. The modeling may be for survivability, side-effect, or both and may use one or more of the blood biomarker, uptake value, and bullae features.

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Exton, PA, United States of America

Shipeng Yu