Use of advanced machine-learning techniques for noninvasive monitoring of hemorrhage.

نویسندگان

  • Victor A Convertino
  • Steven L Moulton
  • Gregory Z Grudic
  • Caroline A Rickards
  • Carmen Hinojosa-Laborde
  • Robert T Gerhardt
  • Lorne H Blackbourne
  • Kathy L Ryan
چکیده

BACKGROUND Hemorrhagic shock is a leading cause of death in both civilian and battlefield trauma. Currently available medical monitors provide measures of standard vital signs that are insensitive and nonspecific. More important, hypotension and other signs and symptoms of shock can appear when it may be too late to apply effective life-saving interventions. The resulting challenge is that early diagnosis is difficult because hemorrhagic shock is first recognized by late-responding vital signs and symptoms. The purpose of these experiments was to test the hypothesis that state-of-the-art machine-learning techniques, when integrated with novel non-invasive monitoring technologies, could detect early indicators of blood volume loss and impending circulatory failure in conscious, healthy humans who experience reduced central blood volume. METHODS Humans were exposed to progressive reductions in central blood volume using lower body negative pressure as a model of hemorrhage until the onset of hemodynamic decompensation. Continuous, noninvasively measured hemodynamic signals were used for the development of machine-learning algorithms. Accuracy estimates were obtained by building models using signals from all but one subject and testing on that subject. This process was repeated, each time using a different subject. RESULTS The model was 96.5% accurate in predicting the estimated amount of reduced central blood volume, and the correlation between predicted and actual lower body negative pressure level for hemodynamic decompensation was 0.89. CONCLUSIONS Machine modeling can accurately identify reduced central blood volume and predict impending hemodynamic decompensation (shock onset) in individuals. Such a capability can provide decision support for earlier intervention.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Advanced machine learning methods for wind erosion monitoring in southern Iran

Extended abstract Introduction Wind erosion is one the most important factors of land degradation in the arid and semi-arid areas and it is one the most serious environmental problems in the world. In Fars province, 17 cities are prone to wind erosion and are considered as critical zones of wind erosion. One of the most important factors in soil wind erosion is land use/cover change. T...

متن کامل

Modeling of Chloride Ion Separation by Nanofiltration Using Machine Learning Techniques

In this work, several machine learning techniques are presented for nanofiltration modeling. According to the results, specific errors are defined. The rejection due to Nanofiltration increases with pressure but decreases with increasing the concentration of chloride ion. Methods of machine learning represent the rejection of nanofiltration as a function of concentration, pH, pressure and also ...

متن کامل

Machine learning algorithms in air quality modeling

Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...

متن کامل

Machine learning based Visual Evoked Potential (VEP) Signals Recognition

Introduction: Visual evoked potentials contain certain diagnostic information which have proved to be of importance in the visual systems functional integrity. Due to substantial decrease of amplitude in extra macular stimulation in commonly used pattern VEPs, differentiating normal and abnormal signals can prove to be quite an obstacle. Due to developments of use of machine l...

متن کامل

Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • The Journal of trauma

دوره 71 1 Suppl  شماره 

صفحات  -

تاریخ انتشار 2011