Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload.

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories. The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors. The system\u27s use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications

Comparative Analysis of Resilience by Supply Network Structure

This research applies Kim, et al.’s (2015) supply network structure archetypes to case data related to two disruptions in three industries in Brazil. A total of seven supply networks were studied, through in-depth interviews and archival documents. The findings suggest that there may be additional supply network structures that are relevant. Centralization appears to be a function of the size of the focal firm. There was evidence of an evolution of supply network structures with focal firm size

Nutritional factors associated with acne vulgaris

Acne Vulgaris is a common dermatological condition defined as a chronic inflammatory dermatosis of the pilosebaceous unit that affects more than 17 million Americans.^1 Although it is not considered a dangerous condition, it may drastically impair quality of life and leave a substantial psychological impact.^2 Acne’s multifactorial pathogenesis is typically categorized into four aspects: increased sebum production, altered keratinization, inflammation, and bacterial colonization.^3 Dietary factors contribution to the pathogenesis of acne has remained controversial throughout the literature. However, currently there exists a greater understanding between how diet may influence endocrine factors contributing to acne pathogenesis.^4 Additionally, recent published evidence and public paradigm shifts highlighting the relationship between diet and health have caused a resurgence of this topic, particularly among patients seeking a more gentle, alternate solution to current treatments. Some of the most promising recent correlating evidence supports an association between acne prevalence and dairy consumption, particularly skim milk consumption.^5 It is hypothesized that milk consumption affects the presence of both reproductive, non-reproductive hormones, and growth factors in our body, which may contribute to increased acne prevalence.^6 However, there has been a lack of randomized controlled trials to determine the cause and effects nature of this relationship, as all previous studies are observational.^1 Therefore, this study will conduct a randomized controlled trial to determine the significance between dietary non-fermented dairy consumption and acne prevalence in adolescents. We hypothesize the adaptation of a diet of decreased dairy consumption will decrease the prevalence or severity of acne vulgaris in adolescents between the ages of 13-18. It is our hope that the conclusion of this study will advance our understanding of the dietary correlation between dairy and acne vulgaris in order to provide further insight to guide medical practitioners’ ability to help treat this distressing condition

Out-of-hours primary care services: Demands and patient referral patterns in a Veneto region (Italy) Local Health Authority

open7PURPOSE: The aim of this study was to describe the characteristics of patients admitted to an out-of-hours (OOH) service and to analyze the related outputs. SETTING: A retrospective population-based cohort study was conducted by analyzing an electronic database recording 23,980 OOH service contacts in 2011 at a Local Health Authority in the Veneto Region (North-East Italy). METHOD: A multinomial logistic regression was used to compare the characteristics of contacts handled by the OOH physicians with cases referred to other services. RESULTS: OOH service contact rates were higher for the oldest and youngest age groups and for females rather than males. More than half of the contacts concerned patients who were seen by a OOH physician. More than one in three contacts related problems managed over the phone; only ≈10% of the patients were referred to other services. Many factors, including demographic variables, process-logistic variables and clinical characteristics of the contact, were associated with the decision to visit the patient's home (rather than provide telephone advice alone), or to refer patients to an ED or to a specialist. Our study demonstrated, even after adjusting, certain OOH physicians were more likely than their colleagues to refer a patient to an ED. CONCLUSION: Our study shows that OOH services meet composite and variously expressed demands. The determining factors associated with cases referred to other health care services should be considered when designing clinical pathways in order to ensure a continuity of care. The unwarranted variability in OOH physicians' performance needs to be addressed.Purpose: The aim of this study was to describe the characteristics of patients admitted to an out-of-hours (OOH) service and to analyze the related outputs. Setting: A retrospective population-based cohort study was conducted by analyzing an electronic database recording 23,980 OOH service contacts in 2011 at a Local Health Authority in the Veneto Region (North-East Italy). Method: A multinomial logistic regression was used to compare the characteristics of contacts handled by the OOH physicians with cases referred to other services. Results: OOH service contact rates were higher for the oldest and youngest age groups and for females rather than males. More than half of the contacts concerned patients who were seen by a OOH physician. More than one in three contacts related problems managed over the phone; only ≈10% of the patients were referred to other services. Many factors, including demographic variables, process-logistic variables and clinical characteristics of the contact, were associated with the decision to visit the patient's home (rather than provide telephone advice alone), or to refer patients to an ED or to a specialist. Our study demonstrated, even after adjusting, certain OOH physicians were more likely than their colleagues to refer a patient to an ED. Conclusion: Our study shows that OOH services meet composite and variously expressed demands. The determining factors associated with cases referred to other health care services should be considered when designing clinical pathways in order to ensure a continuity of care. The unwarranted variability in OOH physicians' performance needs to be addressed.openBuja, Alessandra; Toffanin, R; Rigon, S; Sandona', Paolo; Carraro, D; Damiani, G; Baldo, VincenzoBuja, Alessandra; Toffanin, R; Rigon, S; Sandona', Paolo; Carraro, D; Damiani, G; Baldo, Vincenz

Centerscope

Centerscope, formerly Scope, was published by the Boston University Medical Center "to communicate the concern of the Medical Center for the development and maintenance of improved health care in contemporary society.

An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis

open access articleThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions