Abstract: A method for predicting simulated patient admissions, comprising: receiving healthcare records for a plurality of patients; adapting the received healthcare records to a common data format; parameterizing the adapted healthcare records to generate a plurality of patient parameters comprising for each patient a day of the week admission parameter, a time of day admission parameter, and a patient type parameter; generating a length of stay parameter for each of the plurality of different patient types; generating a transition probability for each of the plurality of different patient types; predicting, for a time period in the healthcare environment, patient admissions; predicting a care pathway for some or all of the predicted patient admissions during the time period; and reporting, via a user interface, the predicted patient admissions and predicted care pathways.

Abstract: According to an aspect, there is provided a computer-implemented method (100) for assessing a subject's adherence to a treatment for a condition, the method comprising receiving (102) adherence data indicative of the subject's past adherence to the treatment; receiving (104) medical data indicative of physiological details and a medical history of the subject; determining (106), based on the received adherence data, a non-adherence risk score indicative of a likelihood that the subject will not adhere to the treatment within a defined time period in the future; determining (108), based on the medical data, an adverse event risk score indicative of a likelihood that the subject will experience an adverse medical event; determining (110), based on the non-adherence risk score and the adverse event risk score, a priority classification to be assigned to the subject; and generating (122), based on the priority classification, an instruction signal to be delivered to a recipient

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: The present invention relates to a method for screening cardiac conditions of a patient, the method comprising the following steps: calculating (S1) a cardiac risk value based on therapy sensor data by means of a first level of a multi-level procedure, wherein the therapy sensor data is provided from a first data source as sensor data during a therapy of the patient; and refining (S2) the calculated cardiac risk value based on survey data and/or device data by means of a second level of a multi-level procedure to provide a refined cardiac risk value, wherein the survey data and/or the device data is provided from a second data source by incremental data gathering.

Abstract: A method (100) for generating and presenting a patient risk score, comprising: (i) receiving (104) a plurality of features about the patient comprising at least a plurality of vital signs obtained for the patient; (ii) characterizing (106), using a trained risk model, an importance of each of the received plurality of features to a risk score analysis; (iii) calculating (108) an initial risk score; (iv) identifying (110) one or more missing features; (v) calculating (110) a risk score confidence interval comprising an effect of the identified one or more missing features on a confidence range of the initial risk score; (vi) calculating (112), from the initial risk score and the calculated risk score confidence interval, a risk score range; and (vii) presenting (118) the risk score range comprising the initial the score plus and minus the calculated risk score confidence interval.

Abstract: A non-transitory computer readable medium (26) stores instructions executable by at least one electronic processor (20) to perform a method (100) of acuity monitoring of a patient. The method includes: generating a set of diagnosis-specific acuity scores (32) for a plurality of diagnoses using diagnosis-specific acuity scoring modules (28) for the respective diagnoses of the plurality of diagnoses applied to clinical metrics of the patient; determining at least one primary diagnosis (34) of the patient using a computer-aided diagnosis (CAD) module (30) applied to the clinical metrics of the patient; selecting an acuity score for the at least one primary diagnosis from the set of diagnosis-specific acuity scores; and displaying an indication of the at least one primary diagnosis and the acuity score for the at least one primary diagnosis.

Abstract: A non-transitory computer readable medium (26) stores instructions executable by at least one electronic processor (20) to perform a method (100) for staging a disease having a predefined ordered set of S discrete stages where S is an integer having a value greater than or equal to two.

Abstract: A system and method for visualizing and annotating temporal trends of an abnormal condition in patient data. A classification and visualization module detects one or more conditions in one or more images, e.g. X-ray images, and visualizes the condition on the image. A temporal disease state extraction module analyzes text, e.g. radiology reports, for indications of a change in the condition. A multimodal disease state comparison module fuses the extracted data into a compact representation of the condition changes over time.

Abstract: A system and method for unsupervised training of a text report identification machine learning model, including: labeling a first set of unlabeled text reports using a seed dictionary to identify concepts in the unlabeled text reports; inputting images associated with the first set of seed-labeled text reports into an auto-encoder to obtain an encoded first set of images; calculating a set of first correlation matrices as a dot product of the first encoded images with their associated text report features; determining a distance between the set of first correlation matrices and a filter bank value associated with the auto-encoder; identifying a first set of validated images as the images in the first set of images that have a distance less than a threshold value; and training the text report machine learning model using the labeled text reports associated with the set of first validated images.

Abstract: A system and method for training a text report identification machine learning model and an image identification machine learning model, including: initially training a text report machine learning model, using a labeled set of text reports including text pre-processing the text report and extracting features from the pre-processed text report, wherein the extracted features are input into the text report machine learning model; initially training an image machine learning model, using a labeled set of images; applying the initially trained text report machine learning model to a first set of unlabeled text reports with associated images to label the associated images; selecting a first portion of labeled associated images; re-training the image machine learning model using the selected first portion of labeled associated images; applying the initially trained image machine learning model to a first set of unlabeled images with associated text reports to label the associated text reports; selecting a first po

Abstract: A system and for interpreting a risk score and comparing a decision trajectory are described. The system includes: a memory adapted to store: input features comprising clinical measurements of a patient; engineered features; a trained risk score computational model comprising instructions; a defined subgroup of patients; and a processor.

Abstract: A method for processing medical information includes identifying a first patient in a first state, identifying a second patient in a second state, calculating a first risk score for the first patient, calculating a first risk score for the second patient, and determining a risk prone area in a medical facility based on the first risk score for the first patient and the first risk score for the second patient. The first state is an infected state and the second state is different from the first state. The first risk score of the first patient provides an indication of a severity of the infected state of the first patient, and the first risk score of the second patient provides an indication of the second patient being infected by the first patient.

Abstract: Methods and systems for managing alerts. The methods and systems described herein receive a classification decision related to a patient. If the classification decision is a borderline classification decision, the systems and methods described herein apply one or more alert filters to patient data to determine an alert filter condition. Upon determining the alert filter condition contradicts the borderline classification, the systems and methods may issue a contextual data alert to a clinician to prompt the clinician to consider contextual data related to the patient.

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.