Koninklijke Philips Corporation N.v.

North American Philips Corporation

James David Schaffer

Larry James Eshelman

Kaushal Kurapati

Nevenka Dimitrova

Karen Irene Trovato

Lalitha Agnihotri

Robert Randall Entrekin

Yasser Alsafadi

Mark Simpson

Walid Ali

Nicolas Wadih Chbat

Nilanjana Banerjee

Claire Levrier

Claude Cohen-Bacrie

Nicholas Villain

Caitlyn Marie Chiofolo

Jean-Michel LaGrange

Methods and systems for selecting a cohort group or a patient at risk from a population of patients with mild cognitive impairment. The methods include using a computer configured to perform the steps: receiving normalized learning data from a portion of the population of patients; tuning a set of decision trees on the normalized learning data; receiving patient data from one or more patients of the population, wherein the patient data is independent from the learning data; classifying the patient data with the tuned set of decision trees to obtain patient threshold values; and displaying the patient threshold values.

Methods and systems for identifying patients with mild cognitive impairment at risk of converting to alzheimer's

An electronic clinical decision support system (CDSS) () comprises: an inference engine () configured to generate clinical decision recommendations for a patient based on information pertaining to the patient, the inference engine comprising rules () developed by a plurality of medical experts () and codified into software; an electronic outliers detector () configured to detect outlier cases that are probative of a potential flaw in the inference engine; an outliers database () configured to collect information pertaining to the outlier cases detected by the electronic outliers detector; and an outliers report generator () configured to generate a report () on the outlier cases detected by the electronic outliers detector, the generated report containing at least some information collected in the outliers database.

Detection of errors in the inference engine of a clinical decision support system

A medical apparatus () assists clinicians, nurses or other users in choosing an intervention for the treatment of a patent suffering from an acute dynamic disease, e.g. sepsis. The medical apparatus is based on a method where a model of the disease is adapted or personalized to the patient. To ensure that the apparatus remains capable of predicting the health of the patient, the apparatus is continuously provided with new, more recent patient values and the model is continuously adapted to the new patient values. Since the medical apparatus is configured to be continuously adapted to current state of health, the apparatus is able to assist the user by generating disease management information, e.g. suggestions for medications, to an output device ().

Decision support system for acute dynamic diseases

A method for determining the presence or absence of malignant features in medical images, wherein a plurality of base comparison or training images of various types of lesions taken of actual patient is examined by one or more image reading experts to create a first database array. Low-level features of each of the lesions in the same plurality of base comparisons or training images are determined using one or more image processing algorithms to obtain a second database array set. The first and second database array set are combined to create a training database array set which is input to a learning system that discovers/learns a classifier that maps from a subset of the low-level features to the expert's evaluation in the first database array set. The classifier is used to determine the presence of a particular mid-level feature in an image of lesion in a patient based solely on the image.

Method and apparatus for automatically developing a high performance classifier for producing medically meaningful descriptors in medical diagnosis imaging

A method of automatically identifying the microarray chip corners and probes, even if there are no probes at the corners, in a high density and high resolution microarray scanned image having an image space, wherein the method minimizes the error distortions in the image arising in the scanning process by applying to the image a multipass corner finding algorithm comprising: (a) applying a Radon transform to an input microarray image to project the image into an angle and distance space where it is possible to find the orientation of the straight lines; (b) applying a fast Fourier transform to the projected image of (a) to find the optimal tilting angle of the projected image; (c) determining the optimal first and last local maxima for the optimal tilting angle; (d) back projecting the determined first and last local maxima to the image space to find the first approximation of the first and last column lines of the image; (e) rotating the image and repeating steps (a) through (d) to find the first approximation of the top and bottom row lines of the image; (f) determining the first approximation of the four corners of the image from the intersection of the column and row lines; (g) applying a heuristic for determining if the first approximation of step (f) is sufficient; and (h) optionally trimming the scanned image around the first approximation of the four corners and repeating steps (a) through (f).

Method to automatically decode microarray images

An apparatus and method for recommending a schedule of events to a user is disclosed. In the preferred embodiment of the system and method, each channel schedule is broken down into time slices. A novel fuzzy-now recommendation-time value is calculated for each time slice. This fuzzy-now recommendation-time value is a two dimensional value measured in units of recommendation-time, or 'enjoyment minutes'. By means of the calculated fuzzy-now recommendation-time values, recommended schedules may be generated using a wide variety of selection methods.

Evening planner

A user interface for a TV recommender system includes a display screen having a first region for displaying a rating derived from a previously defined TV viewing preference profile contained in the recommender system; and a second region displaying preference settings in the profile which were used to derive the rating. The user interface enables the preference settings to be changed if the rating derived by the profile is incorrect. Additionally, the user interface allows for new features to be added to the profile, which were not previously a part of the profile.

User interface for collecting viewer ratings of media content and facilitating adaption of content recommenders

A genetic algorithm is used to search for optimal configurations of a computer controlled pick and place machine, which places parts on printed circuit boards. Configurations include: assigning grippers to pipettes of the machine; assigning parts, destined for the printed circuit boards, to feeders of the machine; assigning parts to pipettes; and determining time intervals and orders in which parts are to be placed. The genetic algorithm is applied to chromosome strings representing parameters for determining machine configuration. A heuristic layout generator generates machine configurations from the chromosome strings.

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Wappingers Falls, NY, United States of America

James David Schaffer