Abstract: Embodiments are disclosed for automatically evaluating records. In the context of a method, an example embodiment includes receiving a set of text produced from a record, identifying, by block manipulation circuitry and from the set of text, one or more blocks of text that are related to a potential conclusion regarding the set of text, and extracting, by the block manipulation circuitry, the one or more blocks of text. The example method further includes concatenating, by the block manipulation circuitry, the extracted one or more blocks into a sequence of words, inputting the sequence of words into a machine learning model, and, in response to inputting the sequence of words into the machine learning model, producing, using the machine learning model, an indication of whether the potential conclusion regarding the record is supported by the sequence of words. Corresponding apparatuses and computer program products are also provided.

Abstract: A method, apparatus and computer program product group records based upon a prediction of the content of the records. In the context of a method, data associated with respective subjects of the records is received and a threshold of a machine learning model is adjusted to satisfy an accuracy requirement for record categorization. In response to analyzing the data, but not the records, by the machine learning model, the method separates, using the machine learning model, the records into the first and second groups with the first group including records that the associated data indicates are more likely to support the addition of a code and the second group including records that the associated data indicates are less likely to support the addition of a code. The method also includes subsequently processing the records in different manners depending upon whether the records are included in the first or second group.

Abstract: A method, apparatus and computer program product refine an automated coding model, such as for a medical chart. For each respective candidate code from a set of candidate codes, the method predicts a probability of the respective code being contained in a medical chart. The method also selects one of the candidate codes as being contained in the medical chart based upon the probability and removes the selected candidate code from the set of candidate codes. The method then repeatedly predicts the probability of a respective code being contained in the medical chart, selects one of the candidate codes based upon the predicted probability and removes the selected candidate code from the set of candidate codes. The method further determines a categorical crossentropy loss as to permit adjustment of one or more parameters of the automated coding model.

Abstract: A dispensing assembly that can include first and second elements is provided. The first element can define a first outlet through which a first liquid is dispensed. The second element can define a second outlet through which a second liquid is dispensed. The first liquid can form an internal liquid stream when dispensed through the first outlet. The second liquid can form an annular liquid column around the internal liquid stream when dispensed through the second outlet.

Abstract: A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.

Abstract: A system includes an ambulatory medical device and a server.

Abstract: A dispensing assembly that can include first and second elements is provided. The first element can define a first outlet through which a first liquid is dispensed. The second element can define a second outlet through which a second liquid is dispensed. The first liquid can form an internal liquid stream when dispensed through the first outlet. The second liquid can form an annular liquid column around the internal liquid stream when dispensed through the second outlet.

Abstract: A dispensing assembly that can include first and second elements is provided. The first element can define a first outlet through which a first liquid is dispensed. The second element can define a second outlet through which a second liquid is dispensed. The first liquid can form an internal liquid stream when dispensed through the first outlet. The second liquid can form an annular liquid column around the internal liquid stream when dispensed through the second outlet.

Abstract: A method, apparatus and computer program product are provided for classifying pages of a document with a linear regression model, and a deep learning (non-linear) model utilizing a neural network. The classification of each page is determined by determining which of the linearly predicted category or the non-linearly predicted category to use, such as for transmission to an auditor. Pages of medical records generated by concatenating reports from distinct sources are classified according to both models, and embodiments determine which classification should be used. The classifications may be optionally smoothed for continuity. The classifications may be sent to auditors and used to review and audit the medical records.

Abstract: Described herein is a beverage system that is configured to produce beverages having different characteristics, such as different levels of carbonation including producing substantially non-carbonated beverages and carbonated beverages with a single machine. The beverage system includes a beverage appliance and a beverage container. The beverage container includes a beverage material, which can include a flavoring ingredient for a target beverage. The beverage appliance is used to access the beverage material from the beverage container and produce the target beverage. The beverage appliance and beverage container are adaptable to produce the target beverage having any of a range of carbonation levels or other characteristics.

Abstract: A package for housing an electric or electronic component is disclosed. The package includes a base and a header that includes a top portion and a support portion. The support portion has a first and a second end that defines a first and a second opening respectively. The top portion is secured to the first end and covers the first opening. The top portion includes a header vent to allow a substance (e.g., air, water, etc.) to flow into or out of an interior of the package. The header is configured to house the component therein. The header can be placed into a circular hole of the circuit board. The base is secured to the second end of the support portion, and is configured to secure the package to the circuit board. The vent can minimize moisture within the package and/or drain liquid from inside of the package.

Abstract: A system includes an ambulatory medical device and a server.

Abstract: A dispensing assembly that can include first and second elements is provided. The first element can define a first outlet through which a first liquid is dispensed. The second element can define a second outlet through which a second liquid is dispensed. The first liquid can form an internal liquid stream when dispensed through the first outlet. The second liquid can form an annular liquid column around the internal liquid stream when dispensed through the second outlet.

Abstract: A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.

Abstract: A package for housing an electric or electronic component is disclosed. The package includes a base and a header that includes a top portion and a support portion. The support portion has a first and a second end that defines a first and a second opening respectively. The top portion is secured to the first end and covers the first opening. The top portion includes a header vent to allow a substance (e.g., air, water, etc.) to flow into or out of an interior of the package. The header is configured to house the component therein. The header can be placed into a circular hole of the circuit board. The base is secured to the second end of the support portion, and is configured to secure the package to the circuit board. The vent can minimize moisture within the package and/or drain liquid from inside of the package.

Abstract: A cardiac monitoring device includes: at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to: obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event if the ECG-derived score is one of above or below the predetermined threshold score determined by the machine learning

Abstract: A system includes an ambulatory medical device and a server.

Abstract: A cardiac monitoring device includes: at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to: obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event if the ECG-derived score is one of above or below the predetermined threshold score determined by the machine learning

Abstract: A cardiac monitoring device includes: at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to: obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event if the ECG-derived score is one of above or below the predetermined threshold score determined by the machine learning

Abstract: A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.