73,332 research outputs found
ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening
Breast cancer screening policies attempt to achieve timely diagnosis by the
regular screening of apparently healthy women. Various clinical decisions are
needed to manage the screening process; those include: selecting the screening
tests for a woman to take, interpreting the test outcomes, and deciding whether
or not a woman should be referred to a diagnostic test. Such decisions are
currently guided by clinical practice guidelines (CPGs), which represent a
one-size-fits-all approach that are designed to work well on average for a
population, without guaranteeing that it will work well uniformly over that
population. Since the risks and benefits of screening are functions of each
patients features, personalized screening policies that are tailored to the
features of individuals are needed in order to ensure that the right tests are
recommended to the right woman. In order to address this issue, we present
ConfidentCare: a computer-aided clinical decision support system that learns a
personalized screening policy from the electronic health record (EHR) data.
ConfidentCare operates by recognizing clusters of similar patients, and
learning the best screening policy to adopt for each cluster. A cluster of
patients is a set of patients with similar features (e.g. age, breast density,
family history, etc.), and the screening policy is a set of guidelines on what
actions to recommend for a woman given her features and screening test scores.
ConfidentCare algorithm ensures that the policy adopted for every cluster of
patients satisfies a predefined accuracy requirement with a high level of
confidence. We show that our algorithm outperforms the current CPGs in terms of
cost-efficiency and false positive rates
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments
Randomized experiments ensure robust causal inference that are critical to
effective learning analytics research and practice. However, traditional
randomized experiments, like A/B tests, are limiting in large scale digital
learning environments. While traditional experiments can accurately compare two
treatment options, they are less able to inform how to adapt interventions to
continually meet learners' diverse needs. In this work, we introduce a trial
design for developing adaptive interventions in scaled digital learning
environments -- the sequential randomized trial (SRT). With the goal of
improving learner experience and developing interventions that benefit all
learners at all times, SRTs inform how to sequence, time, and personalize
interventions. In this paper, we provide an overview of SRTs, and we illustrate
the advantages they hold compared to traditional experiments. We describe a
novel SRT run in a large scale data science MOOC. The trial results
contextualize how learner engagement can be addressed through inclusive
culturally targeted reminder emails. We also provide practical advice for
researchers who aim to run their own SRTs to develop adaptive interventions in
scaled digital learning environments
SAFS: A Deep Feature Selection Approach for Precision Medicine
In this paper, we propose a new deep feature selection method based on deep
architecture. Our method uses stacked auto-encoders for feature representation
in higher-level abstraction. We developed and applied a novel feature learning
approach to a specific precision medicine problem, which focuses on assessing
and prioritizing risk factors for hypertension (HTN) in a vulnerable
demographic subgroup (African-American). Our approach is to use deep learning
to identify significant risk factors affecting left ventricular mass indexed to
body surface area (LVMI) as an indicator of heart damage risk. The results show
that our feature learning and representation approach leads to better results
in comparison with others
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Addressing barriers to learning: In the classroom and schoolwide.
IntroductionPublic education is at a crossroads. Moving in new directions is imperative. Just tweaking and tinkering with old ideas is a recipe for disaster.Continuing challenges confronting public education highlight why moving school improvement policy and practice in new directions is imperative. With a view to enhancing graduation rates and successful transitions to post-secondary opportunities and well-being, pressing challenges include:Increasing equity of opportunity for every student to succeed, narrowing the achievement gap, and countering the school to prison pipeline Reducing unnecessary referrals for special assistance and special education; Improving school climate and retaining good teachers Reducing the number of low performing schools.As education leaders well know, meeting these challenges requires making sustainable progress inimproving supports for specific subgroups (e.g., English Learners, immigrant newcomers, lagging minorities, homeless students, students with disabilities) increasing the number of disconnected students who re-engage in classroom learning and thus improving attendance, reducing disruptive behaviors (e.g., including bullying and sexual harassment), and decreasing suspensions and dropouts increasing family and community engagement with schools responding effectively when schools experience crises events and preventing crises whenever possible.In some schools, continuous progress related to these concerns is being made. For many districts, however, sustainable progress remains elusive – and will continue to be so as long as the focus of school improvement policy and practice is mainly on improving instruction. Efforts to expand the use of instructional technology, develop new curriculum standards, make teachers more accountable, and improve teacher preparation and licensing all have merit; but they are insufficient for addressing the many everyday barriers to learning and teaching that interfere with effective student engagement in classroom instruction.Most policy makers and administrators know that good instruction delivered by highly qualified teachers cannot ensure that all students have an equal opportunity to succeed at school.Even the best teacher can’t do the job alone. Teachers need student and learning supports in the classroom and schoolwide in order to personalize instruction and provide special assistance when students manifest learning, behavior, and emotional problems. Unfortunately, school improvement plans continue to give short shrift to these critical matters.We recognize, as did a Carnegie Task Force on Education, that school systems are not responsible for meeting every need of their students. But as the task force stressed: when the need directly affects learning, the school must meet the challenge.The most pressing challenge is to enhance equity of opportunity by fundamentally improving how schools address barriers to learning and teaching. The future of public education depends on moving in new directions to accomplish this.Now is the time to fundamentally transform how schools address factors that keep too many students from doing well at school. And while transformation is never easy, pioneering work across the country is showing the way. Trailblazers are redeploying existing funds allocated for addressing barriers to learning and weaving these together with the invaluable resources that can be garnered by collaboration with other agencies and with community stakeholders, family members, and students themselves.The first step in moving forward is to escape old ideas. The second step is to incorporate a new vision in school improvement planning for addressing barriers to learning and teaching and re-engaging disconnected students. Our analyses envision a plan that designs and develops a unified, comprehensive, and equitable system of student and learning supports. The third step is to develop a strategic plan for systemic change, scale-up, and sustainability.This book highlights each of these matters. We invite you to join us in the quest to enhance equity of opportunity for all students to succeed at school and beyond. And we look forward to hearing from you about moving schools forward to make the rhetoric of the Every Student Succeeds Act a reality
Dennis Littky, the Educational Activist: Can His Model Revamp the Public Educational System?
When an individual observes a classroom of today, he will see many elements that are recognizable to anyone who attended school during the last one hundred years, students working from textbooks, repetitive worksheets, and rows of desks holding students completing tasks directed by the teacher. Even though societal and technological advancements are increasing rapidly, our school system has stayed stagnant. What this means for students is the lack of individuality, teachers’ non acceptance of personal interests, lack of personal voice, and in many cases, a non relationship between teacher and student beyond the classroom assignment (Castleman & Littky, 2007)
Advances in computational modelling for personalised medicine after myocardial infarction
Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners
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