A tree search approach for the solution of set problems using alternative relaxations

A number of alternative relaxations for the family of set problems (FSP) in general and set covering problems (SCP) in particular are introduced and discussed. These are (i) Network flow relaxation, (ii) Assignment relaxation, (iii) Shortest route relaxation, (iv) Minimum spanning tree relaxation. A unified tree search method is developed which makes use of these relaxations. Computational experience of processing a collection of test problems is reported

Set covering and set partitioning: a collection of test problems

It is now well established that set covering and set partitioning models play a central role in many scheduling applications. There are many algorithms which solve these problems. In order to test and validate such implementations we have collected a range of test problems taken from different contexts. A brief description of these models, their applications and summary model data, are supplied in this paper

Sentiment analysis of health care tweets: review of the methods used.

BACKGROUND: Twitter is a microblogging service where users can send and read short 140-character messages called "tweets." There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field. OBJECTIVE: The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed. METHODS: A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy. RESULTS: A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study's final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used. CONCLUSIONS: Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting-specific corpus of manually annotated tweets first

A Patient-Centered Framework for Evaluating Digital Maturity of Health Services: A Systematic Review

© Kelsey Flott, Ryan Callahan, Ara Darzi, Erik Mayer.Background: Digital maturity is the extent to which digital technologies are used as enablers to deliver a high-quality health service. Extensive literature exists about how to assess the components of digital maturity, but it has not been used to design a comprehensive framework for evaluation. Consequently, the measurement systems that do exist are limited to evaluating digital programs within one service or care setting, meaning that digital maturity evaluation is not accounting for the needs of patients across their care pathways. Objective: The objective of our study was to identify the best methods and metrics for evaluating digital maturity and to create a novel, evidence-based tool for evaluating digital maturity across patient care pathways. Methods: We systematically reviewed the literature to find the best methods and metrics for evaluating digital maturity. We searched the PubMed database for all papers relevant to digital maturity evaluation. Papers were selected if they provided insight into how to appraise digital systems within the health service and if they indicated the factors that constitute or facilitate digital maturity. Papers were analyzed to identify methodology for evaluating digital maturity and indicators of digitally mature systems. We then used the resulting information about methodology to design an evaluation framework. Following that, the indicators of digital maturity were extracted and grouped into increasing levels of maturity and operationalized as metrics within the evaluation framework. Results: We identified 28 papers as relevant to evaluating digital maturity, from which we derived 5 themes. The first theme concerned general evaluation methodology for constructing the framework (7 papers). The following 4 themes were the increasing levels of digital maturity: resources and ability (6 papers), usage (7 papers), interoperability (3 papers), and impact (5 papers). The framework includes metrics for each of these levels at each stage of the typical patient care pathway. Conclusions: The framework uses a patient-centric model that departs from traditional service-specific measurements and allows for novel insights into how digital programs benefit patients across the health system

Future broadband access network challenges

Copyright @ 2010 IEEEThe optical and wireless communication systems convergence will activate the potential capacity of photonic technology for providing the expected growth in interactive video, voice communication and data traffic services that are cost effective and a green communication service. The last decade growth of the broadband internet projects the number of active users will grow to over 2 billion globally by the end of 2014. Enabling the abandoned capacity of photonic signal processing is the promising solution for seamless transportation of the future consumer traffic demand. In this paper, the future traffic growth of the internet, wireless worldwide subscribers, and the end-users during the last and next decades is investigated. The challenges of the traditional access networks and Radio over Fiber solution are presented

The Preservation of Cued Recall in the Acute Mentally Fatigued State: A Randomised Crossover Study.

The objective of this study is to investigate the impact of acute mental fatigue on the recall of clinical information in the non-sleep-deprived state. Acute mental fatigue in the non-sleep-deprived subject is rarely studied in the medical workforce. Patient handover has been highlighted as an area of high risk especially in fatigued subjects. This study evaluates the deterioration in recall of clinical information over 2 h with cognitively demanding work in non-sleep-deprived subjects.A randomised crossover study involving twenty medical students assessed free (presentation) and cued (MCQ) recall of clinical case histories at 0 and 2 h under low and high cognitive load using the N-Back task. Acute mental fatigue was assessed through the Visual Analogue Scale, Stanford Scale and NASA-TLX Mental Workload Rating Scale.Free recall is significantly impaired by increased cognitive load (p < 0.05) with subjects demonstrating perceived mental fatigue during the high cognitive load assessment. There was no significant difference in the amount of information retrieved by cued recall under high and low cognitive load conditions (p = 1).This study demonstrates the loss of clinical information over a short time period involving a mentally fatiguing, high cognitive load task. Free recall for the handover of clinical information is unreliable. Memory cues maintain recall of clinical information. This study provides evidence towards the requirement for standardisation of a structured patient handover. The use of memory cues (involving recognition memory and cued recall methodology) would be beneficial in a handover checklist to aid recall of clinical information and supports evidence for their adoption into clinical practice

Systematic review of hospital readmissions in stroke patients

Background Previous evidence on factors and causes of readmissions associated with high-impact users of stroke is scanty. The aim of the study was to investigate common causes and pattern of short- and long-term readmissions stroke patients by conducting a systematic review of studies using hospital administrative data. Common risk factors associated with the change of readmission rate were also examined. Methods The literature search was conducted from 15th February to 15th March 2016 using various databases, such as Medline, Embase, and Web of Science. Results There were total of 24 studies (n=2,126,617) included in the review. Only 4 studies assessed causes of readmissions in stroke patients with the follow-up duration from 30 days to 5 years. Common causes of readmissions in majority of the studies were recurrent stroke, infections and cardiac conditions. Common patient-related risk factors associated with increased readmission rate were age and history of coronary heart disease, heart failure, renal disease, respiratory disease, peripheral arterial disease and diabetes. Among stroke-related factors, length of stay of index stroke admission was associated with increased readmission rate, followed by bowel incontinence, feeding tube and urinary catheter. Conclusion Although risk factors and common causes of readmission were identified, but none of the previous studies investigated causes and their sequence of readmissions among high-impact stroke users

Could Artificial Intelligence be a Therapeutic for Mental Issues?

The utilization of artificial intelligence (AI) possesses the capacity to fundamentally transform the landscape of mental health care through the provision of very valuable therapeutic interventions. AI possesses the capacity to evaluate extensive quantities of data, enabling it to aid in the identification of early signs of mental health concerns and provide tailored solutions that cater to specific requirements. The utilization of virtual therapists powered by AI has the potential to provide a secure environment for persons experiencing social anxiety or worries associated with stigma. This form of therapy offers continuous support and diminishes the obstacles that hinder individuals from seeking assistance. In addition, AI algorithms possess the capability to effectively identify and analyze patterns and trends derived from many sources, including social media posts and smartphone usage. This enables the assessment of an individual’s mental health and facilitates the provision of timely solutions. Through the utilization of natural language processing and sentiment analysis, AI has the potential to augment current therapeutic practices by proficiently monitoring patient advancements and analyzing therapeutic dialogues. Therefore, the incorporation of AI into the realm of mental health intervention holds promise in enhancing accessibility, cost, and efficacy, while mitigating the strain on conventional healthcare systems

SeaWiFS Technical Report Series. Volume 7: Cloud screening for polar orbiting visible and infrared (IR) satellite sensors

Methods for detecting and screening cloud contamination from satellite derived visible and infrared data are reviewed in this document. The methods are applicable to past, present, and future polar orbiting satellite radiometers. Such instruments include the Coastal Zone Color Scanner (CZCS), operational from 1978 through 1986; the Advanced Very High Resolution Radiometer (AVHRR); the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), scheduled for launch in August 1993; and the Moderate Resolution Imaging Spectrometer (IMODIS). Constant threshold methods are the least demanding computationally, and often provide adequate results. An improvement to these methods are the least demanding computationally, and often provide adequate results. An improvement to these methods is to determine the thresholds dynamically by adjusting them according to the areal and temporal distributions of the surrounding pixels. Spatial coherence methods set thresholds based on the expected spatial variability of the data. Other statistically derived methods and various combinations of basic methods are also reviewed. The complexity of the methods is ultimately limited by the computing resources. Finally, some criteria for evaluating cloud screening methods are discussed