Detection of the power lines in UAV remote sensed images using spectral-spatial methods.

In this paper, detection of the power lines on images acquired by Unmanned Aerial Vehicle (UAV) based remote sensing is carried out using spectral-spatial methods. Spectral clustering was performed using Kmeans and Expectation Maximization (EM) algorithm to classify the pixels into the power lines and non-power lines. The spectral clustering methods used in this study are parametric in nature, to automate the number of clusters Davies-Bouldin index (DBI) is used. The UAV remote sensed image is clustered into the number of clusters determined by DBI. The k clustered image is merged into 2 clusters (power lines and non-power lines). Further, spatial segmentation was performed using morphological and geometric operations, to eliminate the non-power line regions. In this study, UAV images acquired at different altitudes and angles were analyzed to validate the robustness of the proposed method. It was observed that the EM with spatial segmentation (EM-Seg) performed better than the Kmeans with spatial segmentation (Kmeans-Seg) on most of the UAV images.

Next generation terminology infrastructure to support interprofessional care planning.

OBJECTIVE: Develop a prototype of an interprofessional terminology and information model infrastructure that can enable care planning applications to facilitate patient-centered care, learn care plan linkages and associations, provide decision support, and enable automated, prospective analytics. DESIGN: The study steps included a 3 step approach: (1) Process model and clinical scenario development, and (2) Requirements analysis, and (3) Development and validation of information and terminology models. RESULTS: Components of the terminology model include: Health Concerns, Goals, Decisions, Interventions, Assessments, and Evaluations. A terminology infrastructure should: (A) Include discrete care plan concepts; (B) Include sets of profession-specific concerns, decisions, and interventions; (C) Communicate rationales, anticipatory guidance, and guidelines that inform decisions among the care team; (D) Define semantic linkages across clinical events and professions; (E) Define sets of shared patient goals and sub-goals, including patient stated goals; (F) Capture evaluation toward achievement of goals. These requirements were mapped to AHRQ Care Coordination Measures Framework. LIMITATIONS: This study used a constrained set of clinician-validated clinical scenarios. Terminology models for goals and decisions are unavailable in SNOMED CT, limiting the ability to evaluate these aspects of the proposed infrastructure. CONCLUSIONS: Defining and linking subsets of care planning concepts appears to be feasible, but also essential to model interprofessional care planning for common co-occurring conditions and chronic diseases. We recommend the creation of goal dynamics and decision concepts in SNOMED CT to further enable the necessary models. Systems with flexible terminology management infrastructure may enable intelligent decision support to identify conflicting and aligned concerns, goals, decisions, and interventions in shared care plans, ultimately decreasing documentation effort and cognitive burden for clinicians and patients.

A continuum of sociotechnical requirements for patient-centered problem lists.

Specific requirements for patient-centered health information technology remain ill-defined. To create operational definitions of patient-centered problem lists, we propose a continuum of sociotechnical requirements with five stages: 1) Intradisciplinary Care Planning: Viewing and searching for problems by discipline; 2) Multi-disciplinary Care Planning: Categorizing problem states to meet discipline-specific needs; 3) Interdisciplinary Care Planning: Sharing and linking problems between disciplines; 4) Integrated and Coordinated Care Planning: Associating problems with assessments, tasks, interventions and outcomes across disciplines for coordination, knowledge development, and reporting; and 5) Patient-Centered Care Planning: Engaging patients in identification of problems and maintenance of their problem list.

Instructions to "push as hard as you can" improve average chest compression depth in dispatcher-assisted cardiopulmonary resuscitation.

BACKGROUND AND OBJECTIVE: Cardiopulmonary resuscitation (CPR) with adequate chest compression depth appears to improve first shock success in cardiac arrest. We evaluate the effect of simplification of chest compression instructions on compression depth in dispatcher-assisted CPR protocol. METHODS: Data from two randomized, double-blinded, controlled trials with identical methodology were combined to obtain 332 records for this analysis. Subjects were randomized to either modified Medical Priority Dispatch System (MPDS) v11.2 protocol or a new simplified protocol. The main difference between the protocols was the instruction to "push as hard as you can" in the simplified protocol, compared to "push down firmly 2in. (5cm)" in MPDS. Data were recorded via a Laerdal ResusciAnne SkillReporter manikin. Primary outcome measures included: chest compression depth, proportion of compressions without error, with adequate depth and with total release. RESULTS: Instructions to "push as hard as you can", compared to "push down firmly 2in. (5cm)", resulted in improved chest compression depth (36.4 mm vs. 29.7 mm, p<0.0001), and improved median proportion of chest compressions done to the correct depth (32% vs. <1%, p<0.0001). No significant difference in median proportion of compressions with total release (100% for both) and average compression rate (99.7 min(-1) vs. 97.5 min(-1), p<0.56) was found. CONCLUSIONS: Modifying dispatcher-assisted CPR instructions by changing "push down firmly 2in. (5cm)" to "push as hard as you can" achieved improvement in chest compression depth at no cost to total release or average chest compression rate.

Instructions to "put the phone down" do not improve the quality of bystander initiated dispatcher-assisted cardiopulmonary resuscitation.

OBJECTIVE: The quality of early bystander CPR appears important in maximizing survival. This trial tests whether explicit instructions to "put the phone down" improve the quality of bystander initiated dispatch-assisted CPR. METHODS: In a randomized, double-blinded, controlled trial, subjects were randomized to a modified version of the Medical Priority Dispatch System (MPDS) version 11.2 protocol or a simplified protocol, each with or without instruction to "put the phone down" during CPR. Data were recorded from a Laerdal Resusci Anne Skillreporter manikin. A simulated emergency medical dispatcher, contacted by cell phone, delivered standardized instructions. Primary outcome measures included chest compression rate, depth, and the proportion of compressions without error, with correct hand position, adequate depth, and total release. Time was measured in two distinct ways: time required for initiation of CPR and total amount of time hands were off the chest during CPR. Proportions were analyzed by Wilcoxon rank sum tests and time variables with ANOVA. All tests used a two-sided alpha-level of 0.05. RESULTS: Two hundred and fifteen subjects were randomized-107 in the "put the phone down" instruction group and 108 in the group without "put the phone down" instructions. The groups were comparable across demographic and experiential variables. The additional instruction to "put the phone down" had no effect on the proportion of compressions administered without error, with the correct depth, and with the correct hand position. Likewise, "put the phone down" did not affect the average compression depth, the average compression rate, the total hands-off-chest time, or the time to initiate chest compressions. A statistically significant, yet trivial, effect was found in the proportion of compressions with total release of the chest wall. CONCLUSIONS: Instructions to "put the phone down" had no effect on the quality of bystander initiated dispatcher-assisted CPR in this trial.

Development and testing of an algorithm for efficient resource positioning in pre-hospital emergency care.

Response times for pre-hospital emergency care may be improved with the use of algorithms that analyzes historical patterns in incident location and suggests optimal places for pre-positioning of emergency response units. We will develop such an algorithm based on cluster analysis and test whether it leads to significant improvement in mileage when compared to actual historical data of dispatching based on fixed stations.