Prediction of Prognosis in Patients with Trauma by Using Machine Learning.

Background and Objectives: We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. Materials and Methods: We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Then, we subjected this database to different machine learning techniques and chose the one with the highest accuracy by using cross-validation. The primary endpoint was mortality, and the secondary endpoint was requirement for chronic care. Results: Data of 5871 patients were collected. We then used the eXtreme Gradient Boosting (xGBT) machine learning model to create two algorithms: a complete model and a short-term model. The complete model exhibited an 86% recall for recovery, 30% for chronic care, 67% for mortality, and 80% for complications; the short-term model fitted for ED displayed an 89% recall for recovery, 25% for chronic care, and 41% for mortality. Conclusions: We developed a machine learning algorithm that displayed good recall for the healthy recovery group but unsatisfactory results for those requiring chronic care or having a risk of mortality. The prediction power of this algorithm may be improved by implementing features such as age group classification, severity selection, and score calibration of trauma-related variables.

Stateful-Service-Based Pupil Recognition in Natural Light Environments.

Smartphones are currently extensively used worldwide, and advances in hardware quality have engendered improvements in smartphone image quality, which is occasionally comparable to the quality of medical imaging systems. This paper proposes two algorithms for pupil recognition: a stateful-service-based pupil recognition mechanism and color component low-pass filtering algorithm. The PRSSM algorithm can determine pupil diameters in images captured in indoor natural light environments, and the CCLPF algorithm can determine pupil diameters in those captured outdoors under sunlight. The PRSSM algorithm converts RGB colors into the hue saturation value color space and performs adaptive thresholding, morphological operations, and contour detection for effectively analyzing the diameter of the pupil. The CCLPF algorithm derives the average matrix for the red components of eye images captured in outdoor environments. It also performs low-pass filtering, morphological and contour detection operations, and rule-of-thumb correction. This algorithm can effectively analyze pupil diameter in outdoor natural light. Traditional ruler-based measurements of pupil diameter were used as the reference to verify the accuracy of the PRSSM and CCLPF algorithms and to compare their accuracy with that of the other algorithm. The errors in pupil diameter data were smaller for the PRSSM and CCLPF algorithms than for the other algorithm.

Predicting outcomes after trauma: Prognostic model development based on admission features through machine learning.

ABSTRACT: In an overcrowded emergency department (ED), trauma surgeons and emergency physicians need an accurate prognostic predictor for critical decision-making involving patients with severe trauma. We aimed to develope a machine learning-based early prognostic model based on admission features and initial ED management.We only recruited patients with severe trauma (defined as an injury severity score >15) as the study cohort and excluded children (defined as patients <16 years old) from a 4-years database (Chi-Mei Medical Center, from January 2015, to December 2018) recording the clinical features of all admitted trauma patients. We considered only patient features that could be determined within the first 2 hours after arrival to the ED. These variables included Glasgow Coma Scale (GCS) score; heart rate; respiratory rate; mean arterial pressure (MAP); prehospital cardiac arrest; abbreviated injury scales (AIS) of head and neck, thorax, and abdomen; and ED interventions (tracheal intubation/tracheostomy, blood product transfusion, thoracostomy, and cardiopulmonary resuscitation). The endpoint for prognostic analyses was mortality within 7 days of admission.We divided the study cohort into the early death group (149 patients who died within 7 days of admission) and non-early death group (2083 patients who survived at >7 days of admission). The extreme Gradient Boosting (XGBoost) machine learning model provided mortality prediction with higher accuracy (94.0%), higher sensitivity (98.0%), moderate specificity (54.8%), higher positive predict value (PPV) (95.4%), and moderate negative predictive value (NPV) (74.2%).We developed a machine learning-based prognostic model that showed high accuracy, high sensitivity, and high PPV for predicting the mortality of patients with severe trauma.

Using cellular automata for parking recommendations in smart environments.

In this work, we propose an innovative adaptive recommendation mechanism for smart parking. The cognitive RF module will transmit the vehicle location information and the parking space requirements to the parking congestion computing center (PCCC) when the driver must find a parking space. Moreover, for the parking spaces, we use a cellular automata (CA) model mechanism that can adjust to full and not full parking lot situations. Here, the PCCC can compute the nearest parking lot, the parking lot status and the current or opposite driving direction with the vehicle location information. By considering the driving direction, we can determine when the vehicles must turn around and thus reduce road congestion and speed up finding a parking space. The recommendation will be sent to the drivers through a wireless communication cognitive radio (CR) model after the computation and analysis by the PCCC. The current study evaluates the performance of this approach by conducting computer simulations. The simulation results show the strengths of the proposed smart parking mechanism in terms of avoiding increased congestion and decreasing the time to find a parking space.

Adaptive broadcasting mechanism for bandwidth allocation in mobile services.

This paper proposes a tree-based adaptive broadcasting (TAB) algorithm for data dissemination to improve data access efficiency. The proposed TAB algorithm first constructs a broadcast tree to determine the broadcast frequency of each data and splits the broadcast tree into some broadcast wood to generate the broadcast program. In addition, this paper develops an analytical model to derive the mean access latency of the generated broadcast program. In light of the derived results, both the index channel's bandwidth and the data channel's bandwidth can be optimally allocated to maximize bandwidth utilization. This paper presents experiments to help evaluate the effectiveness of the proposed strategy. From the experimental results, it can be seen that the proposed mechanism is feasible in practice.