Abstract: Examples disclosed herein may involve (i) obtaining data for one or more data variables related to autonomous operation of a vehicle in a test environment being facilitated by the vehicle's autonomy system, (ii) based on the obtained data, evaluating one or more predefined fault rules, each of which comprises (a) one or more predefined criteria related to the one or more data variables and (b) a predefined fault that is to be injected into the autonomy system when the one or more predefined criteria are determined to be satisfied, (iii) based on the evaluation, injecting a predefined fault into the autonomy system, and (iv) capturing data indicative of a response by a response mechanism of the vehicle to the vehicle autonomously operating in accordance with the injected fault.

Abstract: A decision support tool is provided for discharging a patient by predicting the probability of a patient's readmission with pneumonia based on information available prior to discharge. The information used to make the prediction may include labs, vitals, diagnoses, and medications from prior encounters and from the current encounter. At least some of this information may be used to compute one or more severity metrics for the patient, such as a cancer score, an epilepsy or seizure score, a pneumococcal pneumonia score, and an instability score, to be input into one or more prediction models. An ensemble of machine learning models may be applied to the patient information to generate a prediction of that patient being readmitted with pneumonia within a future time interval. Based on the prediction, one or more intervening actions may be initiated to reduce the probability of readmission.

Abstract: A system and an information processing method for advanced neuro rehabilitation is provided. The system comprises processor (201) that may configured to receive a user input that corresponds to a set of answers associated with at least one medical condition of a user to a set of questions associated with the at least one medical condition of the user thorough an input unit. The processor (201) may be further configured to determine objective assessment of at least one of the plurality of BIR stages and the plurality of SCIR stages based on at least one of the first set of data and the clinical evaluation data which is obtained through a clinical evaluation of user. The processor (201) may be further configured to generate, based on the objective assessment, a rehabilitation index that indicates a median probability of functional improvements possible for the user.

Abstract: A decision support tool is provided for discharging a patient by predicting the probability of a patient's readmission with pneumonia based on information available prior to discharge. The information used to make the prediction may include labs, vitals, diagnoses, and medications from prior encounters and from the current encounter. At least some of this information may be used to compute one or more severity metrics for the patient, such as a cancer score, an epilepsy or seizure score, a pneumococcal pneumonia score, and an instability score, to be input into one or more prediction models. An ensemble of machine learning models may be applied to the patient information to generate a prediction of that patient being readmitted with pneumonia within a future time interval. Based on the prediction, one or more intervening actions may be initiated to reduce the probability of readmission.

Abstract: An apparatus and method to reduce bandwidth and latency associated with probabilistic caches.

Abstract: Systems, methods and computer-readable media are provided for predicting probability of a clinical invoice being paid by a specified date. A clinical invoice is a request for payment. The clinical invoice can take the form of an insurance claim, bill to a person (e.g., self-insured, co-pay), or submission to a government entity (e.g., Medicare, Medicaid). Healthcare facilities do not know how long they will have to wait between submitting an invoice and receiving compensation from government, insurance, or other payers. The methods described herein can be used to estimate the probability of payment occurring by a specified date. This information can be used to triage outstanding claims for investigation and to improve outstanding cash forecasts. The technology described herein uses survival curves to estimate when an invoice will be paid.

Abstract: Methods, systems, and computer-readable media for a system and method are provided for assessing the value of predictive models monitoring medical conditions. Trends in an individual's medical condition are determined based on monitoring values, and the trends are associated with the actions performed in response to the monitoring values and in accordance with the predictive models. The trends may indicate that an individual's condition is improving, worsening, or remaining stable in response to the action taken. Models used for multiple conditions for an individual or for a population of individuals may be assessed in this manner to generate knowledge regarding the performance of the models based on the actions taken. This knowledge may be used to assess the value of the models in terms of the models' performance and may provide insight on way to improve the models.

Abstract: The current disclosure describes techniques for managing planarization of features formed on a semiconductor wafer. The disclosed techniques achieve relative planarization of micro bump structures formed on a wafer surface by adjusting the pattern density of the micro bumps formed within various regions on the wafer surface. The surface area size of a micro bump formed within a given wafer surface region may be enlarged or reduced to change the pattern density. A dummy micro bump may be inserted into a given wafer surface region to increase the pattern density.

Abstract: Methods and systems for predicting deterioration of a patient's condition within a future time interval based on a time series of values for monitored physiological variables measured from a patient, and in some instances, providing advanced notice to clinicians or caregivers when deterioration is forecasted or modifying treatment for the patient are provided. In particular, deterioration of a patient's condition is based on a Hopf bifurcation model and is predicted using a ratio of deviations for monitored physiological variables. A ratio of deviations relates the standard deviation and root mean square of successive differences for a set of physiological values measured over time. The RoD for one or more variables, such as heart rate, respiratory rate, and blood pressure, may be used to predict the likelihood of the patient's condition deteriorating into an unstable state as what occurs in a Hopf bifurcation.

Abstract: Methods, systems, and computer-readable media for a system and method are provided for assessing the value of predictive models monitoring medical conditions. Trends in an individual's medical condition are determined based on monitoring values, and the trends are associated with the actions performed in response to the monitoring values and in accordance with the predictive models. The trends may indicate that an individual's condition is improving, worsening, or remaining stable in response to the action taken. Models used for multiple conditions for an individual or for a population of individuals may be assessed in this manner to generate knowledge regarding the performance of the models based on the actions taken. This knowledge may be used to assess the value of the models in terms of the models' performance and may provide insight on way to improve the models.

Abstract: A decision support tool is provided for identifying and assisting clinicians with patient ventilator asynchrony. The information used to make the identification may include data from a patient's ventilator including the volume, flow, and pressure associated with that ventilator. At least some of this information may be used to compute one or more features for a time series of the data received for the patient. These features may be used in connection with heuristic rules and machine learning algorithms to identify instances of patient ventilator asynchrony. Based on the identification, one or more intervening actions may be initiated to reduce the impact of patient ventilator asynchrony.

Abstract: The current disclosure describes techniques for managing planarization of features formed on a semiconductor wafer. The disclosed techniques achieve relative planarization of micro bump structures formed on a wafer surface by adjusting the pattern density of the micro bumps formed within various regions on the wafer surface. The surface area size of a micro bump formed within a given wafer surface region may be enlarged or reduced to change the pattern density. A dummy micro bump may be inserted into a given wafer surface region to increase the pattern density.

Abstract: Methods and systems for managing care team assignment's for individuals, such as a patient, are provided. Embodiments include receiving indicators of actions that are initiated by a clinician and associated with a patient's care. Using times associated with each action, a time series indicating contribution levels for each clinician may be constructed. Care contribution curves measuring a clinician's care contribution level over time may be generated using the time series and a rate of decay for each action, which may be based on the type of action and the role of the clinician who initiated the action. A care contribution score for each clinician may be determined from the clinician's care contribution curve, and a care team assignment for the patient may be created based on care contribution scores for each clinician who initiated an action.

Abstract: A decision support tool is provided for predicting the neonatal vitality scores of a fetus during delivery, the scores being an indicator of future health for the infant anticipated to be born within a future time interval, measured as time to birth. The predicted neonatal vitality score is determined from measurements of physiological variables monitored during labor, such as uterine activity and fetal heart rate. Fetal heart rate variability and patterns may be detected and computed using the monitored physiological variables, and neonatal vitality scores may be predicted based, at least in part, on the variability metrics and fetal heart rate patterns. Scores may be predicted for different delivery methods, such as vaginal delivery or cesarean delivery, for different time-to-birth intervals. In this way, these scores may be used for decision support for care plans during labor, such as increased monitoring and/or modifying the delivery type.

Abstract: Disclosed herein is technology for deriving a planned path for a vehicle that is operating in a geographic area. In an example embodiment, a computing system associated with the vehicle may function to (i) identify a set of path priors to use as a basis for deriving the planned path for the vehicle in the geographic area, (ii) sample path priors in the identified set of path priors and thereby producing a set of points that is representative of the identified set of path priors, (iii) fit a curve to the set of points produced by the sampling, and (iv) derive the planned path for the vehicle based on the fitted curve.

Abstract: Embodiments disclosed herein include electronic packages for optical to electrical switching. In an embodiment, an electronic package comprises a first package substrate and a second package substrate attached to the first package substrate. In an embodiment, a die is attached to the second package substrate. In an embodiment, a plurality of photonic engines are attached to a first surface and a second surface of the first package substrate. In an embodiment, the plurality of photonic engines are communicatively coupled to the die through the first package substrate and the second package substrate.

Abstract: A decision support tool is provided for predicting the neonatal vitality scores of a fetus during delivery, the scores being an indicator of future health for the infant anticipated to be born within a future time interval, measured as time to birth. The predicted neonatal vitality score is determined from measurements of physiological variables monitored during labor, such as uterine activity and fetal heart rate. Fetal heart rate variability and patterns may be detected and computed using the monitored physiological variables, and neonatal vitality scores may be predicted based, at least in part, on the variability metrics and fetal heart rate patterns. Scores may be predicted for different delivery methods, such as vaginal delivery or cesarean delivery, for different time-to-birth intervals. In this way, these scores may be used for decision support for care plans during labor, such as increased monitoring and/or modifying the delivery type.

Abstract: Examples disclosed herein may involve (i) obtaining data for one or more data variables related to autonomous operation of a vehicle in a test environment being facilitated by the vehicle's autonomy system, (ii) based on the obtained data, evaluating one or more predefined fault rules, each of which comprises (a) one or more predefined criteria related to the one or more data variables and (b) a predefined fault that is to be injected into the autonomy system when the one or more predefined criteria are determined to be satisfied, (iii) based on the evaluation, injecting a predefined fault into the autonomy system, and (iv) capturing data indicative of a response by a response mechanism of the vehicle to the vehicle autonomously operating in accordance with the injected fault.

Abstract: A decision support tool is provided for discharging a patient by predicting the probability of a patient's readmission based on information prior to discharge. The information used to make the prediction may include labs, vitals, diagnoses, and medications from prior encounters and from the current encounter. At least some of this information may be used to compute a readmission prediction. One or more recurrent neural networks may be utilized to generate the prediction, and one or more sequences may be generated from the patient information to feed into the one or more recurrent neural networks. Based on the prediction, one or more actions may be initiated to reduce the probability of readmission.

Abstract: A processor includes a core to execute a transaction with a memory via cache; and cache controller having an index mapper circuit to: identify a physical memory address associated with the transaction and having a plurality of bits; determine, based on the plurality of bits, a first set of bits encoding a tag value, a second set of bits encoding a page index value, and a third set of bits encoding a line index value; determine a mapping function corresponding to the tag value; determine, using the mapping function, a bit-placement order; combine, based on the order, second and third set of bits to form an index; generate, using the index, a mapping from the address to a cache line index value identifying a cache line in the cache; and wherein the cache controller is further to access, using the mapping and in response to the transaction, the cache line.