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"Goldstein, Elisha"
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Uncovering happiness : overcoming depression with mindfulness and self-compassion
Goldstein believes that overcoming depression and uncovering happiness is in harnessing our brain's own natural antidepressant power and ultimately creating a more resilient antidepressant brain. In seven simple steps, she shows you how to take back control of your mind, your mood, and your life -- Provided by the publisher.
Book
COVID-19 classification of X-ray images using deep neural networks
Objectives
In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model’s results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals.
Methods
In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image.
Results
Our model achieved accuracy of 90.3%, (95% CI: 86.3–93.7%) specificity of 90% (95% CI: 84.3–94%), and sensitivity of 90.5% (95% CI: 85–94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93–0.97).
Conclusion
We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19.
Key Points
•
A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%.
•
A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings.
Journal Article
Cultivating sacred moments: Implications on well-being and stress
The sacred in life has attributes long believed to promote well-being. This research provides a theoretical, empirical, and qualitative examination of the role of cultivating sacred moments in daily life on subjective well-being (SWB), psychological well-being (PWB), and stress. Seventy-three participants, (68% women, 32% men; 71% Caucasian, 11% Chinese), 87% living within the United States, between the ages of 18 to 54), were randomly assigned to one of two groups: (a) an intervention group who were instructed in cultivating sacred moments for a minimum of 5 minutes a day, for 5 days a week, for 3 weeks, or (b) a treatment-based control group who were instructed in writing about daily activities for a minimum of 5 minutes a day, for 5 days a week, for 3 weeks. Findings indicate that for these participants, the intervention effectively increased scores on assessments that measured satisfaction with life, positive-affect and negative-affect, and 5 out of 6 subscales that measured psychological well-being (PWB), including; positive relations with others, autonomy, environmental mastery, purpose in life, and self-acceptance, stress-reduction, and the occurrence of daily spiritual experiences. The intervention also effectively increased the participants' self-reported levels of connection with self, others, and spirit, increased self-reported awareness of the sacred in daily life, increased self-reported feelings of well-being, and reduced self-reported stress. Although the control group also had significant change in the same assessments as the intervention group, further analysis found that after the 3-week intervention, the intervention group had a significantly greater positive effect in measures of life satisfaction and stress-reduction. Furthermore, the 6-week follow-up showed that the intervention group maintained a greater impact on the measure of life satisfaction than the control group after intervention had ended. Future research is needed to address potential long-term effects of cultivating sacred moments in daily life.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Adobe Acrobat.
Dissertation
Mindfulness, Trauma, and Trance: A Mindfulness‐Based Psychotherapeutic Approach
While there is a surging interest and need for integrating mindfulness in the field of health and medicine in Western culture, the understanding of how to bring it into psychotherapy is still in its infancy. Mindfulness to date has been seen as an adjunct to therapy, but more importantly it is as an orientation that holds the context of theory, method, and skills within the relational healing process. In this chapter, we will outline and emphasize how mindfulness on its own and in concert with positive‐altered states can facilitate state‐dependent, experience‐dependent learning and neuronal shifts in service of a healthier brain. There has been a surge in neuroscientific research pointing to the efficacy of mindfulness in affecting seminal pathways to mind/body healing. An original method of mindful inquiry will be introduced to facilitate a transformation in the client's experience of trauma from a pathological affliction to an opportunity to create mindstrength, flexing areas of the brain toward greater empathy, self‐compassion, lovingkindness, and wisdom individually and relationally. These qualities become introjected with a permanency of trait development. Finally, contraindications will be explored when it may not be skillful to bring mindfulness practices into the psychotherapeutic encounter.
Book Chapter
Point of Care Image Analysis for COVID-19
Early detection of COVID-19 is key in containing the pandemic. Disease detection and evaluation based on imaging is fast and cheap and therefore plays an important role in COVID-19 handling. COVID-19 is easier to detect in chest CT, however, it is expensive, non-portable, and difficult to disinfect, making it unfit as a point-of-care (POC) modality. On the other hand, chest X-ray (CXR) and lung ultrasound (LUS) are widely used, yet, COVID-19 findings in these modalities are not always very clear. Here we train deep neural networks to significantly enhance the capability to detect, grade and monitor COVID-19 patients using CXRs and LUS. Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19. In addition, in collaboration with ULTRa (Ultrasound Laboratory Trento, Italy) and hospitals in Italy we obtained POC ultrasound data with annotations of the severity of disease and trained a deep network for automatic severity grading.
Paper
COVID-19 Classification of X-ray Images Using Deep Neural Networks
In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts. The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans. In this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve. The dataset sourced for this study includes 2362 CXRs, balanced for positive and negative COVID-19, from 1384 patients (63 +/- 18 years, 552 men). Our model achieved 89.7% (314/350) accuracy and 87.1% (156/179) sensitivity in classification of COVID-19 on a test dataset comprising 15% (350 of 2326) of the original data, with AUC of ROC 0.95 and AUC of the P-R curve 0.94. For each image we retrieve images with the most similar DNN-based image embeddings; these can be used to compare with previous cases.
Paper
KIDSDAY
His latest book, \"Small Steps,\" continues the life stories of two \"Holes\" characters, Armpit and X-Ray. After serving time in a juvenile detention facility, they think of a scheme to make money - scalping concert tickets. But will this get them into trouble again? We recommend reading this book to find out. Attention Kidsday readers ages 7 to 16! You can win a copy of \"Small Steps\" autographed by [Louis Sachar]. To enter, send your name, age, address and phone number to: \"Small Steps\" Contest, Kidsday, Newsday, 235 Pinelawn Rd., Melville, NY 11747. One winner will be randomly picked March 9. 1) NEWSDAY PHOTO / MARY BETH FOLEY - Author Louis Sachar with Kidsday reporters [KATHARINA ROSS], left, and [ELISHA GOLDSTEIN]; 2) PHOTO - \"Holes\"
Newspaper Article
KIDSDAY
Interesting, varied and unique describe the new music duo Seminole County. They toured with the Backstreet Boys and recently came out with their debut CD, also called Seminole County. We think kids ages 12 and older will enjoy Seminole County's music the most because they will be able to relate to the lyrics. On July 20, 1969, the crew of Apollo 11 (astronauts Neil Armstrong, [Buzz Aldrin] and Michael Collins) became the first humans to land on the moon. Last month, we spoke with Aldrin, and it was a thrill for us to meet a living piece of history.
Newspaper Article
Nudging Physician Prescription Decisions by Partitioning the Order Set: Results of a Vignette-Based Study
Background
Healthcare professionals are rapidly adopting electronic health records (EHRs). Within EHRs, seemingly innocuous menu design configurations can influence provider decisions for better or worse.
Objective
The purpose of this study was to examine whether the grouping of menu items systematically affects prescribing practices among primary care providers.
Participants
We surveyed 166 primary care providers in a research network of practices in the greater Chicago area, of whom 84 responded (51 % response rate). Respondents and non-respondents were similar on all observable dimensions except that respondents were more likely to work in an academic setting.
Design
The questionnaire consisted of seven clinical vignettes. Each vignette described typical signs and symptoms for acute respiratory infections, and providers chose treatments from a menu of options. For each vignette, providers were randomly assigned to one of two menu partitions. For antibiotic-inappropriate vignettes, the treatment menu either listed over-the-counter (OTC) medications individually while grouping prescriptions together, or displayed the reverse partition. For antibiotic-appropriate vignettes, the treatment menu either listed narrow-spectrum antibiotics individually while grouping broad-spectrum antibiotics, or displayed the reverse partition.
Main Measures
The main outcome was provider treatment choice. For antibiotic-inappropriate vignettes, we categorized responses as prescription drugs or OTC-only options. For antibiotic-appropriate vignettes, we categorized responses as broad- or narrow-spectrum antibiotics.
Key Results
Across vignettes, there was an 11.5 percentage point reduction in choosing aggressive treatment options (e.g., broad-spectrum antibiotics) when aggressive options were grouped compared to when those same options were listed individually (95 % CI: 2.9 to 20.1 %;
p
= .008).
Conclusions
Provider treatment choice appears to be influenced by the grouping of menu options, suggesting that the layout of EHR order sets is not an arbitrary exercise. The careful crafting of EHR order sets can serve as an important opportunity to improve patient care without constraining physicians’ ability to prescribe what they believe is best for their patients.
Journal Article