Abstract: A method (100) for determining a baseline creatinine value for a subject, comprising: obtaining (130) a set of features about the subject; analyzing (140), using a trained baseline creatinine determination model, the obtained set of features to generate a baseline creatinine value for the subject; reporting (150), via a user interface, the generated baseline creatinine value for the subject.

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: In various embodiments, a first classification assigned to a periodic component of an electrical waveform that represents electrical activity in a patient's heart may be identified. A corresponding periodic component of a hemodynamic waveform that represents hemodynamic activity in the patient's cardiovascular system may be analyzed. The corresponding periodic component may be causally related to the periodic component of the electrical waveform. Based on the analysis, the previously-assigned classification may be assigned to the corresponding periodic component of the hemodynamic waveform in response to a determination, based on the analyzing, that the previously-assigned classification also applies to the corresponding periodic component. In a database of hemodynamic templates, a hemodynamic template associated with the previously-assigned classification may be updated to include one or more features of the corresponding periodic component of the hemodynamic waveform.

Abstract: A machine learning model, including: a categorical input feature, having a defined set of values; a plurality of non-categorical input features; a word embedding layer configured to convert the categorical input feature into an output in a word space having two dimensions; and a machine learning network configured to receive the output of the word embedding layer and the plurality of non-categorical input features and to produce a machine learning model output.

Abstract: Systems, apparatuses, and methods provide for the monitoring and/or management of acute kidney injury (AKI). For example, an apparatus (100) is configured to determine whether a baseline AKI risk prediction is above a baseline threshold based on patient demographic data and patient medical history data, and perform a continuous AKI risk prediction. The continuous AKI risk prediction includes determining whether an any risk of AKI prediction is above an any AKI threshold based on dynamic intervention data and/or dynamic patient condition data, and determining an AKI stage prediction in response to a determination that the any risk of AKI prediction is above the any AKI threshold based on the dynamic intervention data and/or the dynamic patient condition data.

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.

Abstract: When evaluating the quality of photoplethysmography (PPG) signal (52) measured from a patient monitor (e.g., a finger sensor or the like), multiple features of the PPG signal are extracted and analyzed to facilitate assigning a score to the PPG signal or portions (e.g., heartbeats) thereof. Heartbeats in the PPG signal are segmented out using concurrently captured electrocardiograph (ECG) signal (50), and for each heartbeat, a plurality of extracted features are analyzed. If all extracted features satisfy one or more predetermined criteria for each feature, then the heartbeat waveform is compared to a predefined heartbeat template. If the waveform matches the template (e.g., within a predetermined match percentage or the like), then the heartbeat is classified as “clean.” If the heartbeat does not patch the template, or if one or more of the extracted features fails to satisfy its one or more pre-determined criteria, the heartbeat is classified as “noisy.

Abstract: Instructions (108) cause a processor (104) to: classify a clinical report for a subject under evaluation by one of anatomical organ or disease; identify and retrieve clinical reports for the same subject from the healthcare data source(s); group the retrieved clinical report by one of anatomical organ or disease; select a group of the clinical report, wherein the group includes reports for a same or related one of the anatomical organ or the disease; build a model that predicts semantic relationships between nodes in the reports in the selected group of reports based on one or more of extracted parameters or keywords; compare one of the parameter values or the keywords across the reports using the model; construct a graphical timeline of the reports; highlight differences in the parameter values or the keywords based on a result of the compare; and visually present the graphical timeline with the highlighted differences.

Abstract: Methods and systems for determining the blood pressure of a patient. The system may include a first device configured to collect a first plurality of blood pressure measurements of the patient, a second device configured to collect a second plurality of blood pressure measurements of the patient, and a processor configured to identify a divergence between the first plurality and the second plurality, retrieve, from a memory, a clinical event, compare the first plurality and the second plurality to the clinical event, and determine that the first plurality is more accurate than the second plurality based on the comparison.

Abstract: A method for training a baseline risk model, including: pre-processing input data by normalizing continuous variable inputs and producing one-hot input features for categorical variables; providing definitions for clean input data and dirty input data based upon various input data related to a patient condition; segmenting the input data into clean input data and dirty input data, wherein the clean input data includes a first subset and a second subset, where the first subset and the second subset include all of the clean input data and are disjoint; training a machine learning model using the first subset of the clean data; and evaluating the performance of the trained machine learning model using the second subset of the clean input data and the dirty input data.

Abstract: In various embodiments, a first classification assigned to a periodic component of an electrical waveform that represents electrical activity in a patient's heart may be identified. A corresponding periodic component of a hemodynamic waveform that represents hemodynamic activity in the patient's cardiovascular system may be analyzed. The corresponding periodic component may be causally related to the periodic component of the electrical waveform. Based on the analysis, the previously-assigned classification may be assigned to the corresponding periodic component of the hemodynamic waveform in response to a determination, based on the analyzing, that the previously-assigned classification also applies to the corresponding periodic component. In a database of hemodynamic templates, a hemodynamic template associated with the previously-assigned classification may be updated to include one or more features of the corresponding periodic component of the hemodynamic waveform.

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: Techniques are described herein for using time series data such as vital signs data and laboratory data or other time series data as input across machine learning models to predict a change in stage of a medical condition of a patient. In various embodiments, patient data comprising vital signs data of a patient and laboratory data or other time series data of the patient corresponding to an observation window may be received. A time series model may be used to predict a change in stage of a medical condition in the patient in a prediction window based on the patient data. The predicted change in stage of the medical condition may be output.

Abstract: In various embodiments, a first classification assigned to a periodic component of an electrical waveform that represents electrical activity in a patient's heart may be identified (302). A corresponding periodic component of a hemodynamic waveform that represents hemodynamic activity in the patient's cardiovascular system may be analyzed (306, 318, 328). The corresponding periodic component may be causally related to the periodic component of the electrical waveform. Based on the analysis, the previously-assigned classification may be assigned (312, 324) to the corresponding periodic component of the hemodynamic waveform in response to a determination, based on the analyzing, that the previously-assigned classification also applies to the corresponding periodic component.

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: Systems and methods for adapting a first machine learning model that takes clinical data as input, based on a second set of training data. The first machine learning model having been trained on a first set of training data. The method comprises adding an adaption module to the first machine learning model, the adaption module comprising a second machine learning model, and training the second machine learning model using a second set of training data to take an output of the first machine learning model as input and provide an adjusted output.

Abstract: A machine learning model, including: a categorical input feature, having a defined set of values; a plurality of non-categorical input features; a word embedding layer configured to convert the categorical input feature into an output in a word space having two dimensions; and a machine learning network configured to receive the output of the word embedding layer and the plurality of non-categorical input features and to produce a machine learning model output.

Abstract: A method and system method for determining a patient's acute kidney injury (AKI) stage, including: receiving a patient's current AKI stage; calculating a patient's new AKI stage; comparing the new AKI stage to the current AKI stage; updating the patient's AKI stage to the new AKI stage when the new AKI stage is greater than the current AKI stage; calculating AKI stage exit criteria and an AKI exit stage value; determining whether the AKI stage exit criteria are satisfied; and reducing the patient's AKI stage to the exit AKI stage when the AKI stage exit criteria are satisfied.

Abstract: A method for training a baseline risk model, including: pre-processing input data by normalizing continuous variable inputs and producing one-hot input features for categorical variables; providing definitions for clean input data and dirty input data based upon various input data related to a patient condition; segmenting the input data into clean input data and dirty input data, wherein the clean input data includes a first subset and a second subset, where the first subset and the second subset include all of the clean input data and are disjoint; training a machine learning model using the first subset of the clean data; and evaluating the performance of the trained machine learning model using the second subset of the clean input data and the dirty input data.

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.