Biomedical informatics insights最新文献_第2页

Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images. 模仿病理学家基于评估的可解释和基于上下文的神经网络建模的组织学图像。

Biomedical informatics insights Pub Date : 2018-10-31 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618807481

Arunima Srivastava, Chaitanya Kulkarni, Kun Huang, Anil Parwani, Parag Mallick, Raghu Machiraju

{"title":"Imitating Pathologist Based Assessment With Interpretable and Context Based Neural Network Modeling of Histology Images.","authors":"Arunima Srivastava, Chaitanya Kulkarni, Kun Huang, Anil Parwani, Parag Mallick, Raghu Machiraju","doi":"10.1177/1178222618807481","DOIUrl":"https://doi.org/10.1177/1178222618807481","url":null,"abstract":"Convolutional neural networks (CNNs) have gained steady popularity as a tool to perform automatic classification of whole slide histology images. While CNNs have proven to be powerful classifiers in this context, they fail to explain this classification, as the network engineered features used for modeling and classification are ONLY interpretable by the CNNs themselves. This work aims at enhancing a traditional neural network model to perform histology image modeling, patient classification, and interpretation of the distinctive features identified by the network within the histology whole slide images (WSIs). We synthesize a workflow which (a) intelligently samples the training data by automatically selecting only image areas that display visible disease-relevant tissue state and (b) isolates regions most pertinent to the trained CNN prediction and translates them to observable and qualitative features such as color, intensity, cell and tissue morphology and texture. We use the Cancer Genome Atlas's Breast Invasive Carcinoma (TCGA-BRCA) histology dataset to build a model predicting patient attributes (disease stage and node status) and the tumor proliferation challenge (TUPAC 2016) breast cancer histology image repository to help identify disease-relevant tissue state (mitotic activity). We find that our enhanced CNN based workflow both increased patient attribute predictive accuracy (~2% increase for disease stage and ~10% increase for node status) and experimentally proved that a data-driven CNN histology model predicting breast invasive carcinoma stages is highly sensitive to features such as color, cell size, and shape, granularity, and uniformity. This work summarizes the need for understanding the widely trusted models built using deep learning and adds a layer of biological context to a technique that functioned as a classification only approach till now.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"10 ","pages":"1178222618807481"},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222618807481","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36743065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Monte Carlo Simulations Demonstrate Algorithmic Interventions Over Time Reduce Hospitalisation in Patients With Schizophrenia and Bipolar Disorder. 蒙特卡罗模拟表明，随着时间的推移，算法干预可以减少精神分裂症和双相情感障碍患者的住院率。

Biomedical informatics insights Pub Date : 2018-10-02 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618803076

Alissa Knight, Geoff A Jarrad, Geoff D Schrader, Jorg Strobel, Dennis Horton, Niranjan Bidargaddi

{"title":"Monte Carlo Simulations Demonstrate Algorithmic Interventions Over Time Reduce Hospitalisation in Patients With Schizophrenia and Bipolar Disorder.","authors":"Alissa Knight, Geoff A Jarrad, Geoff D Schrader, Jorg Strobel, Dennis Horton, Niranjan Bidargaddi","doi":"10.1177/1178222618803076","DOIUrl":"https://doi.org/10.1177/1178222618803076","url":null,"abstract":"Non-adherence with pharmacologic treatment is associated with increased rates of relapse and rehospitalisation among patients with schizophrenia and bipolar disorder. To improve treatment response, remission, and recovery, research efforts are still needed to elucidate how to effectively map patient's response to medication treatment including both therapeutic and adverse effects, compliance, and satisfaction in the prodromal phase of illness (ie, the time period in between direct clinical consultation and relapse). The Actionable Intime Insights (AI2) application draws information from Australian Medicare administrative claims records in real time when compliance with treatment does not meet best practice guidelines for managing chronic severe mental illness. Subsequently, the AI2 application alerts clinicians and patients when patients do not adhere to guidelines for treatment. The aim of this study was to evaluate the impact of the AI2 application on the risk of hospitalisation among simulated patients with schizophrenia and bipolar disorder. Monte Carlo simulation methodology was used to estimate the impact of the AI2 intervention on the probability of hospitalisation over a 2-year period. Results indicated that when the AI2 algorithmic intervention had an efficacy level of (>0.6), over 80% of actioned alerts were contributing to reduced hospitalisation risk among the simulated patients. Such findings indicate the potential utility of the AI2 application should replication studies validate its methodologic and ecological rigour in real-world settings.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"10 ","pages":"1178222618803076"},"PeriodicalIF":0.0,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222618803076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36568832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Natural Language Processing of Social Media as Screening for Suicide Risk. 社交媒体的自然语言处理作为自杀风险筛选。

Biomedical informatics insights Pub Date : 2018-08-27 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618792860

Glen Coppersmith, Ryan Leary, Patrick Crutchley, Alex Fine

{"title":"Natural Language Processing of Social Media as Screening for Suicide Risk.","authors":"Glen Coppersmith, Ryan Leary, Patrick Crutchley, Alex Fine","doi":"10.1177/1178222618792860","DOIUrl":"https://doi.org/10.1177/1178222618792860","url":null,"abstract":"Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people's lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media-and the near-ubiquity of mobile devices used to access social media networks-offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have \"opted in\" for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention-we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one?","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"10 ","pages":"1178222618792860"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222618792860","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36443340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 309

Humanizing Digital Mental Health through Social Media: Centering Experiences of Gang-Involved Youth Exposed to High Rates of Violence. 通过社交媒体实现数字心理健康的人性化：以遭受高暴力率的帮派青年的经历为中心。

Biomedical informatics insights Pub Date : 2018-08-27 DOI: 10.1177/1178222618797076

William R Frey

{"title":"Humanizing Digital Mental Health through Social Media: Centering Experiences of Gang-Involved Youth Exposed to High Rates of Violence.","authors":"William R Frey","doi":"10.1177/1178222618797076","DOIUrl":"10.1177/1178222618797076","url":null,"abstract":"As the lives of young people expand further into digital spaces, our understandings of their expressions and language on social media become more consequential for providing individualized and applicable mental health resources. This holds true for young people exposed to high rates of community violence who may also lack access to health resources offline. Social media may provide insights into the impacts of community violence exposure on mental health. However, much of what is shared on social media contains localized language and context, which poses challenges regarding interpretation. In this perspective, I offer insights gained from the Beyond the Bullets: The Complexities and Ethical Challenges of Interpreting Social Media Posts workshop during the Digital Interventions in Mental Health conference in London, England: (1) social media as an underutilized environmental context in mental health services; (2) interpreting the meaning of social media posts is challenging, and there are additional challenges when users are exposed to offline violence and (3) the importance of having various perspectives when interpreting social media posts to build contextually nuanced and theoretically based understandings of digital social behavior.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"10 ","pages":"1178222618797076"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222618797076","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36443341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Tech Has It's Place. 科技有它的地位。

Biomedical informatics insights Pub Date : 2018-07-09 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618785122

Telixia Inico

引用次数: 0

Experiences of Donating Personal Data to Mental Health Research: An Explorative Anthropological Study. 为心理健康研究捐赠个人数据的经历：人类学探索研究

Biomedical informatics insights Pub Date : 2018-06-27 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618785131

Joanna Sleigh

{"title":"Experiences of Donating Personal Data to Mental Health Research: An Explorative Anthropological Study.","authors":"Joanna Sleigh","doi":"10.1177/1178222618785131","DOIUrl":"10.1177/1178222618785131","url":null,"abstract":"Technological developments, such as the advent of social networking sites, apps, and tracking 'cookies', enable the generation and collection of unprecedented quantities of rich personal and behavioural data, opening up a vast new resource for mental health research. Despite these non-traditional health-related data already forming a vital foundation of many new research avenues, little analysis has been done focusing on the experiences, motivations, and concerns of the individuals already engaged in data sharing and donation practices. This explorative study aims to investigate the experiences of individuals voluntarily donating their data to mental health research, specifically through the open data initiative OurDataHelps.org, which aims to develop effective suicide prevention tools. Qualitative semi-structured interviews and participant observation were used on a small sample of participants, yielding 3 key findings: (1) The relationship between participants and their data traces fluctuated between unconscious agency and hyper awareness through curatorship. (2) Despite having concerns about privacy and surveillance, participants were driven by altruistic motivations to engage with health researchers valued by their community, in the hope that their personal information could be of some benefit to future avenues of research. (3) In most cases represented in this sample group, motivation was found to stem from personal experiences with mental health, suicide, and loss. In the suicide survivor community, the experience of data donation is often valued as a method for emotional processing of a loss, connecting with the experiences of others, or as a way of regaining a sense of 'purpose'. By understanding the motivations of individual participants, future projects can ensure that data donation processes are a positive experience and ultimately, increase and sustain the huge potential resources for health researchers worldwide.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"10 ","pages":"1178222618785131"},"PeriodicalIF":0.0,"publicationDate":"2018-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/33/79/10.1177_1178222618785131.PMC6043936.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36317712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform. 集成生物学和床边(i2b2)平台的自动化安装。

Biomedical informatics insights Pub Date : 2018-06-04 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618777749

Kavishwar B Wagholikar, Michael Mendis, Pralav Dessai, Javier Sanz, Sindy Law, Micheal Gilson, Stephan Sanders, Mahesh Vangala, Douglas S Bell, Shawn N Murphy

{"title":"Automating Installation of the Integrating Biology and the Bedside (i2b2) Platform.","authors":"Kavishwar B Wagholikar, Michael Mendis, Pralav Dessai, Javier Sanz, Sindy Law, Micheal Gilson, Stephan Sanders, Mahesh Vangala, Douglas S Bell, Shawn N Murphy","doi":"10.1177/1178222618777749","DOIUrl":"https://doi.org/10.1177/1178222618777749","url":null,"abstract":"Informatics for Integrating Biology and the Bedside (i2b2) is an open source clinical data analytics platform used at more than 150 institutions for querying patient data. An i2b2 installation (called hive) comprises several i2b2 cells that provide different functionalities. Given the complex architecture of i2b2 installation, creating a working installation of the platform is challenging for new users. This is despite the availability of extensive documentation for i2b2 and access to a large and active mailing list community of i2b2 users. To address this problem, we have created an automated installation package, called i2b2-quickstart, which automatically downloads the latest i2b2 source code and dependencies, and compiles and configures the i2b2 cells to create a functional i2b2 hive installation. This package will serve as a convenient starting point and reference implementation that will facilitate researchers in the installation and exploration of the i2b2 platform.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"10 ","pages":"1178222618777749"},"PeriodicalIF":0.0,"publicationDate":"2018-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222618777749","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36209529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Method for Deriving Quasi-healthy Cohorts From Clinical Data. 一种从临床资料中提取准健康队列的方法。

Biomedical informatics insights Pub Date : 2018-05-29 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618777758

Satoshi Irino, Yukio Kurihara

{"title":"A Method for Deriving Quasi-healthy Cohorts From Clinical Data.","authors":"Satoshi Irino, Yukio Kurihara","doi":"10.1177/1178222618777758","DOIUrl":"https://doi.org/10.1177/1178222618777758","url":null,"abstract":"We evaluated quasi-healthy cohorts (model cohorts), derived from clinical data, to determine how well they simulated control cohorts. Control cohorts comprised individuals extracted from a public checkup database in Japan, under the condition that their values for 3 basic laboratory tests fall within specific reference ranges (3Ts condition). Model cohorts comprised outpatients, extracted from a clinical database at a hospital, under the 3Ts condition or under the condition that their values for 4 laboratory tests fall within specific reference ranges (4Ts condition). Because even a patient with a serious illness, such as cancer, may present with normal values on basic laboratory tests, one additional condition was added: the duration (1 or 3 months; 1M or 3M) during which patients were not hospitalized after their first laboratory test. For evaluations, cohorts were specified by age and sex. The 4Ts + 3M condition was the most effective condition, under which model cohorts were used to successfully simulate age-dependent changes and sex differences in laboratory test values for control cohorts. Therefore, by properly setting the conditions for extracting quasi-healthy individuals, we can derive cohorts from clinical data to simulate various types of cohorts. Although some issues with the proposed method remain to be solved, this approach presents new possibilities for using clinical data for cohort studies.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"10 ","pages":"1178222618777758"},"PeriodicalIF":0.0,"publicationDate":"2018-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222618777758","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36197027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing. 在Twitter上容纳悲伤:基于定性分析和自然语言处理的帮派青年在Twitter上的悲伤表达分析

Biomedical informatics insights Pub Date : 2018-04-03 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618763155

Desmond Upton Patton, Jamie MacBeth, Sarita Schoenebeck, Katherine Shear, Kathleen McKeown

{"title":"Accommodating Grief on Twitter: An Analysis of Expressions of Grief Among Gang Involved Youth on Twitter Using Qualitative Analysis and Natural Language Processing.","authors":"Desmond Upton Patton, Jamie MacBeth, Sarita Schoenebeck, Katherine Shear, Kathleen McKeown","doi":"10.1177/1178222618763155","DOIUrl":"https://doi.org/10.1177/1178222618763155","url":null,"abstract":"There is a dearth of research investigating youths' experience of grief and mourning after the death of close friends or family. Even less research has explored the question of how youth use social media sites to engage in the grieving process. This study employs qualitative analysis and natural language processing to examine tweets that follow 2 deaths. First, we conducted a close textual read on a sample of tweets by Gakirah Barnes, a gang-involved teenaged girl in Chicago, and members of her Twitter network, over a 19-day period in 2014 during which 2 significant deaths occurred: that of Raason \"Lil B\" Shaw and Gakirah's own death. We leverage the grief literature to understand the way Gakirah and her peers express thoughts, feelings, and behaviors at the time of these deaths. We also present and explain the rich and complex style of online communication among gang-involved youth, one that has been overlooked in prior research. Next, we overview the natural language processing output for expressions of loss and grief in our data set based on qualitative findings and present an error analysis on its output for grief. We conclude with a call for interdisciplinary research that analyzes online and offline behaviors to help understand physical and emotional violence and other problematic behaviors prevalent among marginalized communities.","PeriodicalId":88397,"journal":{"name":"Biomedical informatics insights","volume":"10 ","pages":"1178222618763155"},"PeriodicalIF":0.0,"publicationDate":"2018-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1178222618763155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35992702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Proceedings from the Digital Innovation in Mental Health Conference, London, 2017. 2017年伦敦心理健康数字创新会议论文集

Biomedical informatics insights Pub Date : 2018-03-28 eCollection Date: 2018-01-01 DOI: 10.1177/1178222618764732

Becky Inkster

引用次数: 1