Abstract: This disclosure presents a process for communications in a borehole containing a fluid or drilling mud, where a conventional mud pulser can be utilized to transmit data to a transducer. The transducer, or a communicatively coupled computing system, can perform pre-processing steps to correct the received data using an average of a moving time window of the received data, and then normalize the corrected data. The corrected data can then be utilized as inputs into a machine learning mud pulse recognition network where the data can be classified and an ideal or clean pulse waveform can be overlaid the corrected data. The overlay and the corrected data can be fed into a conventional decoder or decoded by the disclosed process. The decoded data can then be communicated to another system and used as inputs, such as to a well site controller to enable adjustments to well site operation parameters.

Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: Systems, methods, and computer-readable media for in-situ calibration of magnetic field measurements. In some examples, a method can involve generating a magnetic field via a magnetic field source that is coupled to a downhole tool. The magnetic field source can be located within a fixed distance from one or more sensors coupled to the downhole tool. The method can also involve obtaining respective field measurements of the known magnetic field from the one or more sensors, and comparing the respective field measurements from the one or more sensors with respective reference measurements previously obtained from the one or more sensors to yield respective comparisons. The method can then involve determining, based on the respective comparisons, a respective sensitivity drift for each of the one or more sensors.

Abstract: This disclosure presents a process for communications in a borehole containing a fluid or drilling mud, where a conventional mud pulser can be utilized to transmit data to a transducer. The transducer, or a communicatively coupled computing system, can perform pre-processing steps to correct the received data using an average of a moving time window of the received data, and then normalize the corrected data. The corrected data can then be utilized as inputs into a machine learning mud pulse recognition network where the data can be classified and an ideal or clean pulse waveform can be overlaid the corrected data. The overlay and the corrected data can be fed into a conventional decoder or decoded by the disclosed process. The decoded data can then be communicated to another system and used as inputs, such as to a well site controller to enable adjustments to well site operation parameters.

Abstract: Methods, systems, and computer-readable media are provided for generating and outputting medical billing data. Data is captured by pervasive devices before, during, and after a patient encounter with a healthcare provider. Any unstructured data is transformed into usable structured data, and determinations, such as a service provided by the healthcare provider, are made based on that data as well as other stored data. Based on those determinations, billing data for billing claims related to the patient visit is generated and outputted to a payer of the claims. The billing data may include a medical code that is predicted by a billing model. Billing models may be created from data captured by the pervasive devices from patient visits and data in external data stores, such as electronic health records.

Abstract: This disclosure presents a process for communications in a borehole containing a fluid or drilling mud, where a conventional mud pulser can be utilized to transmit data to a transducer. The transducer, or a communicatively coupled computing system, can perform pre-processing steps to correct the received data using an average of a moving time window of the received data, and then normalize the corrected data. The corrected data can then be utilized as inputs into a machine learning mud pulse recognition network where the data can be classified and an ideal or clean pulse waveform can be overlaid the corrected data. The overlay and the corrected data can be fed into a conventional decoder or decoded by the disclosed process. The decoded data can then be communicated to another system and used as inputs, such as to a well site controller to enable adjustments to well site operation parameters.

Abstract: A mechanism is provided in a data processing system to implement a feature extraction tool for graphical user interface based feature extraction. The feature extraction tool receives selection by a user of a dataset from which features are to be extracted. The feature extraction tool loads a plurality of feature definitions. The feature extraction tool generates a graphical user interface that allows the user to add features from the plurality of features to a feature file. The feature extraction tool presents the graphical user interface to the user and receives user selection of at least one feature to be added to the feature file. The feature extraction tool generates the feature file based on the user selection of the at least one feature.

Abstract: Systems, methods, and computer-readable media for in-situ calibration of magnetic field measurements. In some examples, a method can involve generating a magnetic field via a magnetic field source that is coupled to a downhole tool. The magnetic field source can be located within a fixed distance from one or more sensors coupled to the downhole tool. The method can also involve obtaining respective field measurements of the known magnetic field from the one or more sensors, and comparing the respective field measurements from the one or more sensors with respective reference measurements previously obtained from the one or more sensors to yield respective comparisons. The method can then involve determining, based on the respective comparisons, a respective sensitivity drift for each of the one or more sensors.

Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Abstract: In some embodiments, an apparatus and a system, as well as methods, include sampling a received signal that represents a downhole signal source, at a sampling frequency and for a sampling duration, to provide a sampled signal. Further activity may comprise detecting a frequency of a component of the sampled signal from inspection of a frequency domain representation of the sampled signal. Further activity may comprise adjusting at least one operating frequency for the downhole tool such that the at least one operating frequency is outside a frequency range from the frequency of the component of the sampled signal. Additional methods, apparatus, and systems are disclosed.

Abstract: Mapping of semantics in healthcare may involve accessing first transaction data in a first database, the first transaction data corresponding to a collection of a first number of fields defined for a condition using a first semantic system to store information and calculating a first distribution of information in the first transaction data. Mapping may also involve accessing second transaction data in a second database, the second transaction data corresponding to a second semantic system different than the first semantic system and the second database comprising a second number of fields using the second semantic system to store information, and calculating a second distribution of information in the second transaction data. The distributions may then be compared and a map relating the semantic systems may be generated and used to communicate between the first and second semantic systems.

Abstract: Adaptive medical data collection for medical entities may involve triggering an analysis of electronic records in response to information input into an Electronic Medical Record (EMR) of a patient. Determining a potential condition for the patient based on the analysis. Identifying additional information indicated as relevant to the potential condition of the patient, and generating a request for the identified additional information.

Abstract: Adaptive medical data collection for medical entities may involve managing content by receiving data indicating a context, identifying at least one application or knowledge base associated with the context, designating the identified application or knowledge base as active, and accessing the active application or knowledge base to provide information at an interface point for a medical professionals and a patient.

Abstract: Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.

Abstract: Adaptive medical data collection for medical entities may involve triggering an analysis of electronic records in response to information input into an Electronic Medical Record (EMR) of a patient. Determining a potential condition for the patient based on the analysis. Identifying additional information indicated as relevant to the potential condition of the patient, and generating a request for the identified additional information.

Abstract: Mapping of semantics in healthcare may involve accessing first transaction data in a first database, the first transaction data corresponding to a collection of a first number of fields defined for a condition using a first semantic system to store information and calculating a first distribution of information in the first transaction data. Mapping may also involve accessing second transaction data in a second database, the second transaction data corresponding to a second semantic system different than the first semantic system and the second database comprising a second number of fields using the second semantic system to store information, and calculating a second distribution of information in the second transaction data. The distributions may then be compared and a map relating the semantic systems may be generated and used to communicate between the first and second semantic systems.

Abstract: Mapping of semantics in healthcare may involve accessing first transaction data of a first healthcare entity in a first database, the first transaction data corresponding to a collection of a first number of fields defined for a condition using a first semantic system to store information and calculating a first distribution of information in the first transaction data. Mapping may also involve accessing second transaction data of a second healthcare entity in a second database, the second transaction data corresponding to a second semantic system different than the first semantic system and the second database comprising a second number of fields using the second semantic system to store information, the second number of fields larger than the first number of fields and calculating a second distribution of information in the second transaction data. The distributions may then be compared and a map relating the semantic systems may be generated.