Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study.

BACKGROUND: Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. METHODS: We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. RESULTS: Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time." CONCLUSION: We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.

Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study.

Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area.

From Sensor Data to Educational Insights.

Technology is gradually becoming an integral part of learning at all levels of educational [...].

Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game.

Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of Shadowspect, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using Shadowspect so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate.

A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms.

The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs. However, a review of different MMLA studies highlighted that ad-hoc and rigid architectures cannot be scaled up to real contexts. In this work, we propose a novel MMLA architecture that builds on software-defined networks and network function virtualization principles. We exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way. Additionally, through some experiments, we demonstrate the feasibility and performance of our architecture when different classroom devices are reconfigured with diverse learning tools. These findings and the proposed architecture can be useful for other researchers in the area of MMLA and educational technologies envisioning the future of smart classrooms. Future work should aim to deploy this architecture in real educational scenarios with MMLA applications.

Textbook outcome in patients with biliary duct injury during cholecystectomy.

BACKGROUND: Iatrogenic bile duct injury (BDI) during cholecystectomy is associated with a complex and heterogeneous management owing to the burden of morbidity until their definitive treatment. This study aimed to define the textbook outcomes (TOs) after BDI with the purpose to indicate the ideal treatment and to improve it management. METHODS: We collected data from patients with an BDI between 1990 and 2022 from 27 hospitals. TO was defined as a successful conservative treatment of the iatrogenic BDI or only minor complications after BDI or patients in whom the first repair resolves the iatrogenic BDI without complications or with minor complications. RESULTS: We included 808 patients and a total of 394 patients (46.9%) achieved TO. Overall complications in TO and non-TO groups were 11.9% and 86%, respectively (P < .001). Major complications and mortality in the non-TO group were 57.4% and 9.2%, respectively. The use of end-to-end bile duct anastomosis repair was higher in the non-TO group (23.1 vs 7.8, P < .001). Factors associated with achieving a TO were injury in a specialized center (adjusted odds ratio [aOR], 4.01; 95% CI, 2.68-5.99; P < .001), transfer for a first repair (aOR, 5.72; 95% CI, 3.51-9.34; P < .001), conservative management (aOR, 5.00; 95% CI, 1.63-15.36; P = .005), or surgical management (aOR, 2.45; 95% CI, 1.50-4.00; P < .001). CONCLUSION: TO largely depends on where the BDI is managed and the type of injury. It allows hepatobiliary centers to identify domains of improvement of perioperative management of patients with BDI.

Annotated Flickr dataset for identification of professional photographers.

We collected and computed various data and statistics from a sample of Flickr users who uploaded photos to the platform in December 2021 and their photos, obtaining a final number of 27,516 users and 2,647,928 photos. Having the total number of photos uploaded and the number of photos uploaded in December by each user, we selected a representative sample of those whose activity was not overly concentrated in December and obtained data from those who specified their occupation. In addition to the data collected directly from Flickr, we enriched the dataset with new features resulting from the automated analysis of the photos and their comments. One of the most valuable features of this data collection is that each photo has three Image Quality Assessment scores representing aesthetic and technical aspects. For this, we used Convolutional Neural Networks trained with human-labeled data. Furthermore, we added labels to indicate whether the user is a professional photographer, so the data are specially prepared for supervised training.

Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.

BACKGROUND: Iatrogenic bile duct injury (IBDI) is a challenging surgical complication. IBDI management can be guided by artificial intelligence models. Our study identified the factors associated with successful initial repair of IBDI and predicted the success of definitive repair based on patient risk levels. METHODS: This is a retrospective multi-institution cohort of patients with IBDI after cholecystectomy conducted between 1990 and 2020. We implemented a decision tree analysis to determine the factors that contribute to successful initial repair and developed a risk-scoring model based on the Comprehensive Complication Index. RESULTS: We analyzed 748 patients across 22 hospitals. Our decision tree model was 82.8% accurate in predicting the success of the initial repair. Non-type E (p < 0.01), treatment in specialized centers (p < 0.01), and surgical repair (p < 0.001) were associated with better prognosis. The risk-scoring model was 82.3% (79.0-85.3%, 95% confidence interval [CI]) and 71.7% (63.8-78.7%, 95% CI) accurate in predicting success in the development and validation cohorts, respectively. Surgical repair, successful initial repair, and repair between 2 and 6 weeks were associated with better outcomes. DISCUSSION: Machine learning algorithms for IBDI are a novel tool may help to improve the decision-making process and guide management of these patients.

Vascular injury during cholecystectomy: A multicenter critical analysis behind the drama.

BACKGROUND: The management of a vascular injury during cholecystectomy is still very complicated, especially in centers not specialized in complex hepatobiliary surgery. METHODS: This was a multi-institutional retrospective study in patients with vascular injuries during cholecystectomy from 18 centers in 4 countries. The aim of the study was to analyze the management of vascular injuries focusing on referral, time to perform the repair, and different treatments options outcomes. RESULTS: A total of 104 patients were included. Twenty-nine patients underwent vascular repair (27.9%), 13 (12.5%) liver resection, and 1 liver transplant as a first treatment. Eighty-four (80.4%) vascular and biliary injuries occurred in nonspecialized centers and 45 (53.6%) were immediately transferred. Intraoperative diagnosed injuries were rare in referred patients (18% vs 84%, P = .001). The patients managed at the hospital where the injury occurred had a higher number of reoperations (64% vs 20%, P ˂ .001). The need for vascular reconstruction was associated with higher mortality (P = .04). Two of the 4 patients transplanted died. CONCLUSION: Vascular lesions during cholecystectomy are a potentially life-threatening complication. Management of referral to specialized centers to perform multiple complex multidisciplinary procedures should be mandatory. Late vascular repair has not shown to be associated with worse results.

Twitter social bots: The 2019 Spanish general election data.

The term social bots refer to software-controlled accounts that actively participate in the social platforms to influence public opinion toward desired directions. To this extent, this data descriptor presents a Twitter dataset collected from October 4th to November 11th, 2019, within the context of the Spanish general election. Starting from 46 hashtags, the collection contains almost eight hundred thousand users involved in political discussions, with a total of 5.8 million tweets. The proposed data descriptor is related to the research article available at [1]. Its main objectives are: i) to enable worldwide researchers to improve the data gathering, organization, and preprocessing phases; ii) to test machine-learning-powered proposals; and, finally, iii) to improve state-of-the-art solutions on social bots detection, analysis, and classification. Note that the data are anonymized to preserve the privacy of the users. Throughout our analysis, we enriched the collected data with meaningful features in addition to the ones provided by Twitter. In particular, the tweets collection presents the tweets' topic mentions and keywords (in the form of political bag-of-words), and the sentiment score. The users' collection includes one field indicating the likelihood of one account being a bot. Furthermore, for those accounts classified as bots, it also includes a score that indicates the affinity to a political party and the followers/followings list.