Abstract: A method for identifying two or more infections as related or non-related infections based on an estimated genetic relatedness of the two or more infections, comprising: (i) receiving, for each of two or more infected patients, infection-relevant information comprising an antibiotic resistance profile for the patient's infection, a geo-temporal record for the patient, and a caregiver history for the patient; (ii) estimating, using a trained genetic relatedness model, a genetic relatedness of at least two of the two or more infections; (iii) comparing the estimated genetic relatedness between at least two of the two or more infections to a predetermined threshold; (iv) identifying, based on the comparison, the at least two of the two or more infections as a related infection or a non-related infection.

Abstract: In an epidemiology transmission probability analysis for a medical facility, ray origin points are distributed over a medical facility floor map. Rays are cast from the ray origin points. The cast rays stop upon encountering a physical barrier mapped in the medical facility floor map. An infectious transmission probability map is computed from intersections of the cast rays. At least a portion of the medical facility floor map is displayed on a display, overlaid with the infectious transmission probability map. The distributing, the casting, and the computing are suitably performed by one or more electronic processors.

Abstract: A method for performing de-identified location analytics is provided. The method may include generating a de-identified image by removing the identifiable characteristics from a building arrangement image; generating a binary threshold image by processing the de-identified image using a threshold process; generating a segmentation image by removing any segmentation objects of the binary threshold image with an area less than a defined pixel area; extracting room definitions from the segmentation image, wherein each room definition comprises a series of pixels corresponding to an outline of one of the rooms of the building arrangement image; generating a pixilation table, the pixilation table comprising room definition entries corresponding to the room definitions of the segmentation image, wherein each pixel of the segmentation image is mapped to the room definition entry corresponding to the room definition of the outline surrounding the pixel; and assigning a label to each room definition entry.

Abstract: A method (100) for characterizing a sample using a sample characterization system (400), comprising: (i) obtaining (120) sequencing data from the sample; (ii) identifying (130) a genotype of an organism in the sample by comparing the sequencing data to a set of genetic features, comprising genetic features for each of a plurality of different organisms; (iii) selecting (140) which of a plurality of reference genome sets to compare the sequencing data to; (iv) comparing (150) the sequencing data to the selected set of reference genomes; (v) identifying (160) with which reference genome in the selected set of reference genomes the sequencing data most closely aligns, the identification comprising an identification of a species or substrain; and (vi) reporting (170) one or more of the identified genotype of the organism in the sample and the identification of the species or substrain of the organism in the sample.

Abstract: The present system is configured to obtain training information related to patients, and obtain a user input indicating prediction criteria that are to be used by a prediction model for generating patient-related predictions. The prediction criteria include which and how many prediction-contributing features are to be used by the prediction model for generating patient-related predictions. The system is configured to generate the prediction model based on the prediction criteria and the training information; and generate, based on the prediction model and patient information associated with a patient, a prediction related to a health outcome of the patient. The system is also configured to cause display of the prediction and other predictions, wherein the display comprises a scaled display of two or more of the prediction-contributing features.

Abstract: A method for sequence typing using whole-genome sequence data, comprising: receiving a plurality of gene marker sets, each gene marker set comprises sequence data for a plurality of gene markers from an organism, and comprising a plurality of alleles for each gene marker; generating a set of machine learning models for each gene marker in the gene marker set configured to predict an allele value for a gene marker when sequence data for that gene marker is missing or unusable; receiving whole-genome sequence data for the organism, comprising missing or unusable sequence data for a gene marker in the plurality of gene markers; analyzing, using the set of machine learning models, the received whole-genome sequence data to determine one or more probable allele values for that gene maker; and displaying the one or more probable allele values.

Abstract: A method for identifying two or more infections as related or non-related infections based on an estimated genetic relatedness of the two or more infections, comprising: (i) receiving, for each of two or more infected patients, infection-relevant information comprising an antibiotic resistance profile for the patient's infection, a geo-temporal record for the patient, and a caregiver history for the patient; (ii) estimating, using a trained genetic relatedness model, a genetic relatedness of at least two of the two or more infections; (iii) comparing the estimated genetic relatedness between at least two of the two or more infections to a predetermined threshold; (iv) identifying, based on the comparison, the at least two of the two or more infections as a related infection or a non-related infection.

Abstract: A record collection device (201, 400) configured to collect records for an individual (452). The device includes: a user interface (440) configured to receive input from the user, a location module (410) configured to detect a location the device; and a processor (420) configured to: (i) automatically determine, by a processor of the record collection device, that the detected location corresponds to a location where a record may be generated or stored; (ii) receive, from the individual via the user interface, approval to request the record from the determined location; and (iii) send, in response to the individual's input, a request to the determined location for the individual's record.

Abstract: In an epidemiology transmission probability analysis for a medical facility, ray origin points are distributed over a medical facility floor map. Rays are cast from the ray origin points. The cast rays stop upon encountering a physical barrier mapped in the medical facility floor map. An infectious transmission probability map is computed from intersections of the cast rays. At least a portion of the medical facility floor map is displayed on a display, overlaid with the infectious transmission probability map. The distributing, the casting, and the computing are suitably performed by one or more electronic processors.

Abstract: Various embodiments described herein relate to a method, computer readable medium, and device including one or more of the following: receiving at least one newly-available patient feature for the patient; adding the newly-available patient feature to an indication of all available patient features previously established for a previous application of one of a collection of trained models to the patient; comparing the indication of all available patient features to metadata describing input features of respective models of the collection of trained models to determine whether the input features are available for applying the respective trained models to the patient; selecting a selected trained model based on determining that the input features for the selected trained model are available for applying the selected trained model to the patient; and invoking the selected trained model.