An approach for improving determining a significant slice associated with a tumor from a volume of medical images is disclosed. The approach is based on the annotation of tumor range and the slice index in which the tumor appears to have the largest area. The approach infer a tumor growth classifier on sliding window of the volume slices and creates a discrete integral function out of the classifier predictions. The approach applies post processing on the discrete integral function which can include a smoothing function and a bias correction. The approach selects the slice index of maximum value from the post processing step.

An automated laboratory system for processing biological samples in a batch type manner is disclosed. In one embodiment, the system may receive assay instructions for biological samples processing among a plurality of devices. The devices may include a pre-analytical instrument and one or more analysis systems. The system may include an orchestration core application for determining an order of performance for the assays ordered for the samples.

An automated laboratory system for processing biological samples in a batch type manner is disclosed. In one embodiment, the system may receive assay instructions for biological samples processing among a plurality of devices. The devices may include a pre-analytical instrument and one or more analysis systems. The system may include an orchestration core application for determining an order of performance for the assays ordered for the samples.

Systems and methods for intelligently routing a member of an organization to a single point-of-contact within an optimized, secure network to address all the member's healthcare needs are described. The disclosed intelligent routing configurations transform and process, in real-time, vast amounts of member data to generate aggregated diagnoses and a member score specific to each member's household. The scores, among other things, are used to determine an identification of special needs and an appropriate advocate within the organization to route the member, and its account file containing real-time member and household level data.

Systems and methods for intelligently routing a member of an organization to a single point-of-contact within an optimized, secure network to address all the member's healthcare needs are described. The disclosed intelligent routing configurations transform and process, in real-time, vast amounts of member data to generate aggregated diagnoses and a member score specific to each member's household. The scores, among other things, are used to determine an identification of special needs and an appropriate advocate within the organization to route the member, and its account file containing real-time member and household level data.

A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.

Systems and methods of the present invention provide for: receiving a digital image data; modifying the digital image data to reduce a width of a feature within the digital image data; executing a dimension reduction process on the feature; storing a feature vector comprising: at least one feature for each of the received digital image data, and a correct or incorrect label associated with each feature vector; selecting the feature vector from a data store; training a classification software engine to classify each feature vector according to the label; classifying the image data as correct or incorrect according to a classification software engine; and generating an output labeling a second digital image data as correct or incorrect.

System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.

System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.