Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses

Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain

Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification

Recent technological advancements have led to a large number of patents in a diverse range of domains, making it challenging for human experts to analyze and manage. State-of-the-art methods for multi-label patent classification rely on deep neural networks (DNNs), which are complex and often considered black-boxes due to their opaque decision-making processes. In this paper, we propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) to provide human-understandable explanations for predictions. We train several DNN models, including Bi-LSTM, CNN, and CNN-BiLSTM, and propagate the predictions backward from the output layer up to the input layer of the model to identify the relevance of words for individual predictions. Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class. Experimental results on two datasets comprising two-million patent texts demonstrate high performance in terms of various evaluation measures. The explanations generated for each prediction highlight important relevant words that align with the predicted class, making the prediction more understandable. Explainable systems have the potential to facilitate the adoption of complex AI-enabled methods for patent classification in real-world applications.Comment: This is the pre-print of the submitted manuscript on the World Conference on eXplainable Artificial Intelligence (xAI2023), Lisbon, Portugal. The published manuscript can be found here https://doi.org/10.1007/978-3-031-44067-0_2

Barriers, Perceptions and Compliance: Hand Hygiene in the Operating Room & Endoscopy Suite

Despite poor observed HH compliance, the majority of OR and EPR respondents are aware of HH policies and the benefits in reducing HAIs There is adequate access to foam in the OR/EPR and it is physically tolerated Although HH practices are encouraged in both areas, OR/EPR managers poorly role model HH OR nurses are empowered HH advocates, knowledgeable of the benefits of HH and may serve as change agents to improve HH compliance Hospitals promoting HH in the OR/EPR should: Be knowledgeable of perceptions and barriers across services Increase the awareness/education of HH to all providers Empower employees to address colleagues’ HH Remind supervisors to lead by example Measure HH compliance with feedback to managers and front line provider

Weight-based vs. BSA-based Fluid Resuscitation Predictions in Pediatric Burn Patients

Fluid resuscitation for pediatric burns uses formulas that estimate fluid requirements based on weight, and/or body surface area (BSA) along with percent total burn surface area (TBSA). Adult studies have shown that these formulas can cause fluid overload in obese patients and increase risk of complications. These findings have not been validated in pediatric patients. This study provides a retrospective review conducted with 110 children (≤ 18 years old) admitted to an ABA-verified urban pediatric burn center from October 2008 to May 2020. Patients were resuscitated with the weight-based Parkland formula, and had fluids titrated to urine output every two hours. BSA-based Galveston and BSA-incorporated Cincinnati formula resuscitation predictions were also calculated. Complications were collected throughout the hospital stay. Patients were classified into CDC-defined weight groups based on percentile ranges. We found that predicted resuscitation volumes increased as CDC percentile increased for all three formulas (p=0.033, 0.092, 0.038), however there were no significant differences between overweight and obese children. Total fluid administered was higher as CDC percentile increased (p=0.023). However, overweight children received more total fluid than obese children. The difference between total fluids given and Galveston predicted resuscitation volumes were significant across all groups (p=0.042); however, the difference using the Parkland and Cincinnati formulas were not statistically significant. There were more children in the normal weight group who developed complications compared to other groups, but these findings were not significant. Overall, the Parkland formula tended to underpredict fluid needs in the underweight, normal, and overweight children, and it overpredicted fluid needs for the obese. Further research is needed to determine the value of weight-based vs BSA-based or incorporated formulas in terms of their risk of complications

Total Body Photography and Sequential Digital Dermoscopy Imaging for Melanoma Surveillance in Patients Starting Natalizumab for Multiple Sclerosis

Introduction: Natalizumab is an integrin receptor antagonist that has been approved by the Food and Drug Administration to treat multiple sclerosis and Crohn’s disease. The drug has been linked to an increased risk of melanoma. This brief report highlights an innovative clinical approach for monitoring the skin of patients prescribed natalizumab. Methods: We include 2 cases from our skin oncology clinic and a literature review on the incidence of melanoma in patients prescribed natalizumab between 2004 and 2019. Results: In addition to our 2 cases, we found 193 reports of patients with melanoma who were prescribed natalizumab. We propose an innovative and proactive approach using total body photography and sequential digital dermoscopy imaging before starting and while treating patients with natalizumab. Discussion: Given the mechanism of action of natalizumab, many of the melanomas diagnosed likely arose from preexisting melanocytic nevi. Using total body photography before starting this high-risk medication and then sequential digital dermoscopy imaging will increase a dermatologist’s ability to recognize new and preexisting skin lesions that have evolved since the patient began taking natalizumab. Conclusions: Using the latest non-invasive technology to detect skin cancer supports systematic and objective monitoring of changing melanocytic growths in patients prescribed natalizumab, resulting in earlier detection of melanoma and greater cure rates

Enhanced efficiency of genetic programming toward cardiomyocyte creation through topographical cues

AbstractGeneration of de novo cardiomyocytes through viral over-expression of key transcription factors represents a highly promising strategy for cardiac muscle tissue regeneration. Although the feasibility of cell reprogramming has been proven possible both in vitro and in vivo, the efficiency of the process remains extremely low. Here, we report a chemical-free technique in which topographical cues, more specifically parallel microgrooves, enhance the directed differentiation of cardiac progenitors into cardiomyocyte-like cells. Using a lentivirus-mediated direct reprogramming strategy for expression of Myocardin, Tbx5, and Mef2c, we showed that the microgrooved substrate provokes an increase in histone H3 acetylation (AcH3), known to be a permissive environment for reprogramming by “stemness” factors, as well as stimulation of myocardin sumoylation, a post-translational modification essential to the transcriptional function of this key co-activator. These biochemical effects mimicked those of a pharmacological histone deacetylase inhibitor, valproic acid (VPA), and like VPA markedly augmented the expression of cardiomyocyte-specific proteins by the genetically engineered cells. No instructive effect was seen in cells unresponsive to VPA. In addition, the anisotropy resulting from parallel microgrooves induced cellular alignment, mimicking the native ventricular myocardium and augmenting sarcomere organization