An optimization problem in virtual endoscopy

AbstractThis paper studies a graph optimization problem occurring in virtual endoscopy, which concerns finding the central path of a colon model created from helical computed tomography (CT) image data. The central path is an essential aid for navigating through complex anatomy such as colon. Recently, Ge et al. (1998) devised an efficient method for finding the central path of a colon. The method first generates colon data from a helical CT data volume by image segmentation. It then generates a 3D skeleton of the colon. In the ideal situation, namely, if the skeleton does not contain branches, the skeleton will be the desired central path. However, almost always the skeleton contains extra branches caused by holes in the colon model, which are artifacts produced during image segmentation. To remove false branches, Ge et al. (1998) formulated a graph optimization problem for obtaining the central path. This paper presents a refined formulation and justifies that the solution of the refined optimization problem represents the accurate central path of a colon. We then provide a fast algorithm for solving the problem

An entropic feature selection method in perspective of Turing formula

Health data are generally complex in type and small in sample size. Such domain-specific challenges make it difficult to capture information reliably and contribute further to the issue of generalization. To assist the analytics of healthcare datasets, we develop a feature selection method based on the concept of Coverage Adjusted Standardized Mutual Information (CASMI). The main advantages of the proposed method are: 1) it selects features more efficiently with the help of an improved entropy estimator, particularly when the sample size is small, and 2) it automatically learns the number of features to be selected based on the information from sample data. Additionally, the proposed method handles feature redundancy from the perspective of joint-distribution. The proposed method focuses on non-ordinal data, while it works with numerical data with an appropriate binning method. A simulation study comparing the proposed method to six widely cited feature selection methods shows that the proposed method performs better when measured by the Information Recovery Ratio, particularly when the sample size is small

A reinforcement learning agent for head and neck intensity-modulated radiation therapy

Head and neck (HN) cancers pose a difficult problem in the planning of intensity-modulated radiation therapy (IMRT) treatment. The primary tumor can be large and asymmetrical, and multiple organs at risk (OARs) with varying dose-sparing goals lie close to the target volume. Currently, there is no systematic way of automating the generation of IMRT plans, and the manual options face planning quality and long planning time challenges. In this article, we present a reinforcement learning (RL) model for the purposes of providing automated treatment planning to reduce clinical workflow time as well as providing a better starting point for human planners to modify and build upon. Several models with progressing complexity are presented, including the relevant plan dosimetry analysis and model interpretations of the resulting strategies learned by the auto-planning agent. Models were trained on a set of 40 patients and validated on a set of 20 patients. The presented models are shown to be consistent with the requirements of an RL model to be underpinned by a Markov decision process (MDP). In-depth interpretability of the models is presented by examination of the decision space using action hyperplanes. The auto-planning agent was able to generate plans with superior reduction in the mean dose of the left and right parotid glands by approximately 7 Gy ± 2.5 Gy (p < 0.01) over a starting, static template plan with only pre-defined general prescription information. RL plans were comparable to a human expert’s clinical plans for the primary (44 Gy), boost (26 Gy) , and the summed plans (70 Gy) with p-values of 0.43, 0.72, and 0.67, respectively, for the dosimetric endpoints and uniform target coverage normalization. The RL planning agent was able to produce the plans used in validation in an average of 13.58 min, with a minimum and a maximum planning time of 2.27 and 44.82 min, respectively

Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: An intertechnique and interinstitutional study

Purpose: To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution

Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): Architecture

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components

Towards Obesity Surveillance Using Multifaceted Online Social Relational Factors in Reddit

ObjectiveWe aim to better understand online social interactions and environments of individuals interested in weight management from a social media platform called Reddit.IntroductionOverweight and obesity are recognized as one of the greatest modern public health problems1, yet worldwide prevalence of obesity has nearly doubled over the past 30 years2. As part of a strategy to control the obesity pandemic, the WHO recommends an obesity surveillance at the population level3. Empirical studies have shown the importance of social networks in obesity4 and new strategies focusing on social interactions and environments have been proposed5 to prevent the further increase in obesity prevalence. With the increasing use of the internet, online social networks, interactions, and environments (i.e., online social relational factors) deserve more attention.Nearly three- quarters of Americans go online daily6, for functions like connecting with individuals via social network sites7. Like face to face interactions, studies have suggested that social interactions and networks on the internet can influence behavior changes8. Previous studies examining social networking sites typically examine a few selected social networking sites (example studies9,10), although individuals could be members of multiple social networking sites. To better leverage online social relational factors for the purpose of characterizing and monitoring population obesity trends, we investigate weight management community members’ other communities and their level of participation, a first step toward utilizing online multifactorial social interactions and environments.MethodsIn this study, we studied Reddit (http://www.reddit.com), a popular social interaction site, because Reddit hosts many subreddits (i.e., sub-communities), including weight management communities called r/loseit. First, we use a dataset11 — made available on Reddit — that had been used in many informatics studies12–14. For this study, we used a portion of the dataset from Jan 2015 to May 2015. In the first five months of 2015, 5,006,186 members were active in 96,462 subreddits, while submitting 17,851,561 posts and 266,268,920 associated comments. Second, we identified members with more than 3 posts on r/loseit in that period and removed ‘bot’ accounts by manually examining the top 20 frequent posters and their account IDs. Third, we extracted these members’ entire discussions made on Reddit, regardless of the subreddits. Fourth, we identified these members’ overall activities on Reddit and visualized in a network15.ResultsAfter removing bot accounts, we identified 7,734 members who had more than 3 posts in r/loseit from Jan 2015 to May 2015. On average, these members participated in 78.5 subreddits (standard error: 0.1; median: 49.0), while participating in 13,649 unique subreddits as a whole. Members’ participated subreddits are summarized in Figure 1. The size of the nodes represents the number of participating members and the thickness of edges represents the number of members who participated in both subreddits.ConclusionsWe present preliminary findings towards better understanding the online multifactorial social interactions and environments on a social networking site called Reddit. We provide evidence that members encounter many social interactions that occur outside of the community of our interest, the weight management community. However, what members discuss outside of the weight management community as well as the interactions’ influence on weight managements and changes remain unanswered. For example, many members also participate in a subreddit called r/fitness, a community that could share many similar interests with r/loseit. However, the purpose for participating in both communities is unknown. On the basis of our initial analysis, we suggest leveraging online multifaceted social relational factors for the purpose of characterizing and monitoring population obesity trends.References1. Jeffery, R. W. & Utter, J. The changing environment and population obesity in the United States. Obes. Res. 11 Suppl, 12S–22S (2003).2. World Health Organization. Global Health Observatory (GHO) data: Obesity. (2009). Available at: http://www.who.int/gho/ncd/risk_factors/obesity_text/en/; Archived at: http://www.webcitation.org/6rQICh7Oq.3. Bjorntorp, P. et al. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ. Tech. Rep. Ser. 894, i–xii, 1-253 (2000).4. Christakis, N. A. & Fowler, J. H. The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357, 370–9 (2007).5. Leroux, J. S., Moore, S. & Dubé, L. Beyond the ‘I’ in the obesity epidemic: a review of social relational and network interventions on obesity. J. Obes. 2013, 348249 (2013).6. Perrin, A. One fifth of Americans report going online ‘almost constantly’. Pew Research Center (2015). Available at: http://www.pewresearch.org/fact-tank/2015/12/08/one-fifth-of-americans-report-going-online-almost-constantly/; Archived at: http://www.webcitation.org/6s9ZTXRDl.7. Greenwood, S., Perrin, A. & Duggan, M. Social Media Update 2016. Pew Research Center Internet, Science & Tech 1–9 (2016). Available at: http://www.pewinternet.org/2016/11/11/social-media-update-2016/ ; Archived at: http://www.webcitation.org/6q0FDWNRI.8. Laranjo, L. et al. The influence of social networking sites on health behavior change: a systematic review and meta-analysis. J. Am. Med. Inform. Assoc. 22, 243–56 (2015).9. Park, A. et al. ‘How Did We Get Here?’: Topic Drift in Online Health Discussions. J. Med. Internet Res. 18, e284 (2016).10. Park, A., Conway, M. & Chen, A. T. Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Comput. Human Behav. 78, 98–112 (2018).11. Reddit_Member. I have every publicly available Reddit comment for research. ~ 1.7 billion comments @ 250 GB compressed. Any interest in this? (2015). Available at: https://www.reddit.com/r/datasets/comments/3bxlg7/i_have_every_publicly_available_reddit_comment/; Archived at: http://www.webcitation.org/6kgAuNxDE.12. Park, A. & Conway, M. Tracking Health Related Discussions on Reddit for Public Health Applications. Annu. Symp. proceedings. AMIA Symp. 2017, 1362–1371 (2017).13. Park, A. & Conway, M. Harnessing Reddit to Understand the Written-Communication Challenges Experienced by Individuals With Mental Health Disorders: Analysis of Texts From Mental Health Communities. J. Med. Internet Res. 20, e121 (2018).14. Park, A. & Conway, M. Towards Tracking Opium Related Discussions in Social Media. Online J. Public Health Inform. 9, e73 (2017).15. Bastian, M., Heymann, S. & Jacomy, M. Gephi: An Open Source Software for Exploring and Manipulating Networks. (2009).

CASMI—An Entropic Feature Selection Method in Turing’s Perspective

Health data are generally complex in type and small in sample size. Such domain-specific challenges make it difficult to capture information reliably and contribute further to the issue of generalization. To assist the analytics of healthcare datasets, we develop a feature selection method based on the concept of coverage adjusted standardized mutual information (CASMI). The main advantages of the proposed method are: (1) it selects features more efficiently with the help of an improved entropy estimator, particularly when the sample size is small; and (2) it automatically learns the number of features to be selected based on the information from sample data. Additionally, the proposed method handles feature redundancy from the perspective of joint-distribution. The proposed method focuses on non-ordinal data, while it works with numerical data with an appropriate binning method. A simulation study comparing the proposed method to six widely cited feature selection methods shows that the proposed method performs better when measured by the Information Recovery Ratio, particularly when the sample size is small