Abstract: Novel tools and techniques are provided for assessing, predicting and/or effectiveness of cardiopulmonary resuscitation (“CPR”), in some cases, noninvasively. In various embodiments, tools and techniques are provided for implementing rapid estimation of a patient's compensatory reserve index (“CRI”) before, during, and after CPR is performed, and using the CRI and variations in CRI values to determine, in some instances, in real-time, the effectiveness of CPR that is performed.

Abstract: Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient's physiological state. In an aspect, some tools and techniques can estimate predict the onset of conditions intracranial pressure, an amount of blood volume loss, cardiovascular collapse, and/or dehydration. Some tools can recommend (and, in some cases, administer) a therapeutic treatment for the patient's condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

Abstract: Novel tools and techniques for assessing, predicting and/or estimating effectiveness of fluid resuscitation of a patient and/or an amount of fluid needed for effective resuscitation of the patient, in some cases, noninvasively.

Abstract: Novel tools and techniques for assessing, predicting and/or estimating effectiveness of hydration of a patient and/or an amount of fluid needed for effective hydration of the patient, in some cases, noninvasively.

Abstract: Tools and techniques for estimating and/or predicting a patient's current and/or future blood pressure. In some cases, the tools will analyze physiological data captured from the patient against a model of blood pressure values to estimate/predict the patient's blood pressure value. In particular cases, derived parameters, such as a patient's compensatory reserve index (“CRI”) can be analyzed against such models, while in other cases, data captured from sensors can be directly analyzed against such models.

Abstract: Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient's intracranial pressure. In an aspect, some tools and techniques can predict the onset of conditions such as herniation and/or can recommend (and, in some cases, administer) a therapeutic treatment for the patient's condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

Abstract: Tools and techniques for estimating a probability that a patient is bleeding or has sustained intravascular volume loss (e.g., due to hemodialysis or dehydration) and/or to estimate a patient's current hemodynamic reserve index, track the patient's hemodynamic reserve index over time, and/or predict a patient's hemodynamic reserve index in the future. Tools and techniques for estimating and/or predicting a patient's dehydration state. Tools and techniques for controlling a hemodialysis machine based on the patient's estimated and/or predicted hemodynamic reserve index.

Abstract: Methods and systems are disclosed for autonomously building a predictive model of outcomes. A most-predictive set of signals Sk is identified out of a set of signals s1, s2, . . . , sD for each of one or more outcomes ok. A set of probabilistic predictive models ôk=Mk (Sk) is autonomously learned, where ôk is a prediction of outcome ok derived from the model Mk that uses as inputs values obtained from the set of signals Sk. The step of autonomously learning is repeated incrementally from data that contains examples of values of signals s1, s2, . . . , sD and corresponding outcomes o1, o2, . . . , oK. Various embodiments are also disclosed that apply predictive models to various physiological events and to autonomous robotic navigation.

Abstract: Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient's intracranial pressure. In an aspect, some tools and techniques can predict the onset of conditions such as herniation and/or can recommend (and, in some cases, administer) a therapeutic treatment for the patient's condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

Abstract: Tools and techniques for enhancing intelligent medical monitoring, and in particular monitoring that employs models for estimating and/or predicting physiological conditions. In an aspect, some of these tools and techniques employ active physical perturbation of a test subject (or patient), to induce physiological changes in the subject. By monitoring one or more of the patient's physiological parameters shortly before, during, and/or after the physical perturbation, the subject's reaction to the perturbation can be determined, and this reaction can be used to estimate and/or predict the subject's physiological state and/or clinical condition. In a particular case, the subject's response to the physical perturbation can be used to construct and/or refine a model that can be applied to analyze the subject's physiological parameters to produce such predications and/or estimations.