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Originally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Cabañeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Cabañeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others.

Natural language processing (NLP) has been used to process text pertaining to patient records and narratives. However, most of the methods used were developed for specific systems, so new research is necessary to assess whether such methods can be easily retargeted for new applications and goals, with the same performance. In this paper, open-source tools are reused as building blocks on which a new system is built. The aim of our work is to evaluate the applicability of the current NLP technology to a new domain: automatic knowledge acquisition of diagnostic and therapeutic procedures from clinical practice guideline free-text documents. In order to do this, two publicly available syntactic parsers, several terminology resources and a tool oriented to identify semantic predications were tailored to increase the performance of each tool individually. We apply this new approach to 171 sentences selected by the experts from a clinical guideline, and compare the results with those of the tools applied with no tailoring. The results of this paper show that with some adaptation, open-source NLP tools can be retargeted for new tasks, providing an accuracy that is equivalent to the methods designed for specific tasks. [ABSTRACT FROM AUTHOR]

Although ATP-binding cassette transporter A1 (ABCA1) is well known for its role in cholesterol efflux and HDL formation, it is expressed in various tissues, where it may have different functions. Because hypoalphalipoproteinemia is highly prevalent in Mexico, we screened the ABCA1 coding sequence in Mexican individuals with low and high HDL cholesterol levels to seek functional variants. A highly frequent nonsynonymous variant (R230C) was identified in low-HDL cholesterol but not in high-HDL cholesterol individuals (P = 0.00006). We thus assessed its frequency in the Mexican-Mestizo general population, seeking possible associations with several metabolic traits. R230C was screened in 429 Mexican Mestizos using Taqman assays, and it was found in 20.1% of these individuals. The variant was significantly associated not only with decreased HDL cholesterol and apolipoprotein A-I levels but also with obesity (odds ratio 2.527, P = 0.005), the metabolic syndrome (1.893, P = 0.0007), and type 2 diabetes (4.527, P = 0.003). All of these associations remained significant after adjusting for admixture (P = 0.011, P = 0.001, and P = 0.006, respectively). This is the first study reporting the association of an ABCA1 variant with obesity and obesity-related comorbidities as being epidemiologically relevant in the Mexican population. [ABSTRACT FROM AUTHOR]

For over a decade, our research group has studied the biology of the native bumblebee, Bombus atratus, to investigate the feasibility of using it to pollinate crops such as tomato, strawberry, blackberry and peppers. Traditionally, captive breeding has depended on the use of captured wild queens to initiate the colonies. The goal of the current work is to investigate conditions required to produce new queens and drones in captivity. In this study, 31 colonies were evaluated under either greenhouse or open field conditions over a 15 month period. A total of 1492 drones (D) and 737 gynes (G, i.e., virgin queens) were produced by all colonies, with 16 colonies producing both drones and gynes (D&G), 11 producing only drones (D) and 4 producing neither. Some of the D&G colonies had more than one sexual phase, but no colonies produced exclusively gynes. More drones and fewer gynes were produced per colony under greenhouse conditions with the highest number of drones produced by D&G colonies. The numbers of immature stages per cell declined in colonies as increasingly more resources were allocated to the production of gynes and the maintenance of increased nest temperature.

Objective: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans., Materials and Methods: Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods-research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods., Results: Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support. Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods., Discussion: The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS., Conclusion: We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: The presence of Antinuclear antibodies/Dense Fine Speckled 70 (ANA/DFS70) has been proposed as a negative biomarker in the process of exclusion of systemic autoimmune/autoinflammatory rheumatic diseases (SARD). The purpose was to evaluate and characterize ANA/DFS70 patients in a large Colombian population with SARD; rheumatoid arthritis (RA), Psoriasis (PsO), Undifferentiated connective tissue disease (UCTD), first-degree relatives of (FDR), and healthy controls (HC)., Methods: ANA determination was performed using indirect immunofluorescence. Samples with positive dense fine granular staining in the nucleoplasm of the interphase cell (AC2) fluorescence were confirmed with CytoBead/ANA and ANA/modified (Knocked out for the PSPI1 gen)., Results: 530 mestizo Colombian participants were included. ANA/DFS70 antibody positivity in the whole group was 2.3%, and 0.8% in SARD; no RA patients were positive. ANA/DFS70 positives in UCTD were three women; the average time of evolution of the disease was 9.4 years. The most frequent clinical findings were arthralgias, non-erosive arthritis, and Raynaud's phenomenon. The PsO positive was a woman with C-reactive protein (CRP) positivity and a negative erythrocyte sedimentation rate (ESR) without any other positive autoantibody or extracutaneous manifestation. FDR and HC positives were 7/8 women. All were negative for other autoantibodies., Conclusions: ANA/DFS70 autoantibodies were present in Colombian patients with SARD at a shallow frequency, they were more prevalent in healthy individuals.

In health-care, there is a need to quantify medical errors. Among these errors, we observe wrong dose prescriptions. Drug dose titration (DT) is the process by which dosage is progressively adjusted to the patient till a steady dose is reached. Depending on the clinical disease, drug, and patient condition, dose titration can follow different procedures. Once modeled, these procedures can serve for clinical homogenization, standardization, decision support and retrospective analysis. Here, we propose a language to model dose titration procedures. The language was used to formalize one- and two-drug titration of chronic and acute cases, and to perform retrospective analysis of the drug titration processes on 253 patients diagnosed of diabetes mellitus type 2 and treated with metformin, 321 patients treated of chonic heart failure with furosemide, 155 patients with hyperuricemia treated with allopurinol as initial drug and febuxostat as alternative drug, and 187 hyperuricemia patients with primary drug allopurinol and supplementary drug probenecid, in order to identify different types of drug titration deviations from standard DT methods., Competing Interests: Declaration of competing interest Authors declare no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Background: Acute respiratory distress syndrome (ARDS) is an intense inflammatory process of the lungs. Most ARDS patients require mechanical ventilation (MV). Few studies have investigated the prediction of MV duration over time. We aimed at characterizing the best early scenario during the first two days in the intensive care unit (ICU) to predict MV duration after ARDS onset using supervised machine learning (ML) approaches. Methods: For model description, we extracted data from the first 3 ICU days after ARDS diagnosis from patients included in the publicly available MIMIC-III database. Disease progression was tracked along those 3 ICU days to assess lung severity according to Berlin criteria. Three robust supervised ML techniques were implemented using Python 3.7 (Light Gradient Boosting Machine (LightGBM); Random Forest (RF); and eXtreme Gradient Boosting (XGBoost)) for predicting MV duration. For external validation, we used the publicly available multicenter database eICU. Results: A total of 2466 and 5153 patients in MIMIC-III and eICU databases, respectively, received MV for >48 h. Median MV duration of extracted patients was 6.5 days (IQR 4.4-9.8 days) in MIMIC-III and 5.0 days (IQR 3.0-9.0 days) in eICU. LightGBM was the best model in predicting MV duration after ARDS onset in MIMIC-III with a root mean square error (RMSE) of 6.10-6.41 days, and it was externally validated in eICU with RMSE of 5.87-6.08 days. The best early prediction model was obtained with data captured in the 2nd day. Conclusions: Supervised ML can make early and accurate predictions of MV duration in ARDS after onset over time across ICUs. Supervised ML models might have important implications for optimizing ICU resource utilization and high acute cost reduction of MV.

Patients in intensive care units are heterogeneous and the daily prediction of their days to discharge (DTD) a complex task that practitioners and computers are not always able to solve satisfactorily. In order to make more precise DTD predictors, it is necessary to have tools for the analysis of the heterogeneity of the patients. Unfortunately, the number of publications in this field is almost non-existent. In order to alleviate this lack of tools, we propose four methods and their corresponding measures to quantify the heterogeneity of intensive patients in the process of determining the DTD. These new methods and measures have been tested with patients admitted over four years to a tertiary hospital in Spain. The results deepen the understanding of the intensive patient and can serve as a basis for the construction of better DTD predictors., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Background: Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO 2 /(FiO 2 xPEEP) or P/FP E ] for PEEP ≥ 5 to address Berlin's definition gap for ARDS severity by using machine learning (ML) approaches., Methods: We examined P/FP E values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO 2 /FiO 2 criteria with P/FP E under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (N = 2738, 1519, and 1341 patients, respectively) from MIMIC-III database according to Berlin criteria for severity. Then, we used the multicenter database eICU (2014-2015) and extracted data from the first 3 ICU days after ARDS onset (N = 5153, 2981, and 2326 patients, respectively). Disease progression in each database was tracked along those 3 ICU days to assess ARDS severity. Three robust ML classification techniques were implemented using Python 3.7 (LightGBM, RF, and XGBoost) for predicting ARDS severity over time., Results: P/FP E ratio outperformed PaO 2 /FiO 2 ratio in all ML models for predicting ARDS severity after onset over time (MIMIC-III: AUC 0.711-0.788 and CORR 0.376-0.566; eICU: AUC 0.734-0.873 and CORR 0.511-0.745)., Conclusions: The novel P/FP E ratio to assess ARDS severity after onset over time is markedly better than current PaO 2 /FiO 2 criteria. The use of P/FP E could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity.

Background: Machine learning predictive models for breast cancer survival can improve if they are made specific to the stage of the cancer at the time of diagnosis. However, the relevance of the clinical parameters in that prediction, and the predictive quality of these models may change over time., Objective: To determine whether the findings on the influence of clinical parameters and the performance of machine learning models in the prediction of breast cancer survival have to be considered temporary or permanent, and if temporary what is the period of validity of the new generated knowledge., Methods: Fifteen recently published relevant conclusions on the application of machine learning methods to predict breast cancer survival were identified. Then, the data on breast cancer in the SEER database were used to construct several data-driven models over time to predict five-year survival of breast cancer. Three different machine learning methods were used. Stage-specific models and joint models for all the stages were considered. The predictive quality of the models and the importance of clinical parameters were subjected to a persistence analysis over time in order to determine the validity and durability of these fifteen conclusions., Results and Conclusions: Only 53% of the conclusions were true for the SEER cases in 1988-2009, and only 75% of these were true over time. Relevant conclusions such as the impossibility to improve survival prediction of the most frequent stages with more data or the importance of the grade of the cancer to predict breast cancer survival of patients with distant metastasis turned to be false when subjected to a temporal analysis. Our study concludes that data-driven knowledge obtained with machine learning methods must be subject to over time validation before it can be clinically and professionally applied., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: In the last ten years, the international workshop on knowledge representation for health care (KR4HC) has hosted outstanding contributions of the artificial intelligence in medicine community pertaining to the formalization and representation of medical knowledge for supporting clinical care. Contributions regarding modeling languages, technologies and methodologies to produce these models, their incorporation into medical decision support systems, and practical applications in concrete medical settings have been the main contributions and the basis to define the evolution of this field across Europe and worldwide., Objectives: Carry out a review of the papers accepted in KR4HC in the 2009-2018 decade, analyze and characterize the topics and trends within this field, and identify challenges for the evolution of the area in the near future., Methods: We reviewed the title, the abstract, and the keywords of the 112 papers that were accepted to the workshop, identified the medical and technological topics involved in these works, provided a classification of these papers in medical and technological perspectives and obtained the timeline of these topics in order to determine interest growths and declines. The experience of the authors in the field and the evidences after the review were the basis to propose a list of challenges of knowledge representation in health care for the future., Results: The most generic knowledge representation methods are ontologies (31%), semantic web related formalisms (26%), decision tables and rules (19%), logic (14%), and probabilistic models (10%). From a medical informatics perspective, knowledge is mainly represented as computer interpretable clinical guidelines (43%), medical domain ontologies (26%), and electronic health care records (22%). Within the knowledge lifecycle, contributions are found in knowledge generation (38%), knowledge specification (24%), exception detection and management (12%), knowledge enactment (8%), temporal knowledge and reasoning (7%), and knowledge sharing and maintenance (7%). The clinical emphasis of knowledge is mainly related to clinical treatments (27%), diagnosis (13%), clinical quality indicators (13%), and guideline integration for multimorbid patients (12%). According to the level of development of the works presented, we distinguished four maturity levels: formal (22%), implementation (52%), testing (13%), and deployment (2%) levels. Some papers described technologies for specific clinical issues or diseases, mainly cancer (22%) and diseases of the circulatory system (20%). Chronicity and comorbidity were present in 10% and 8% of the papers, respectively., Conclusions: KR4HC is a stable community, still active after ten years. A persistent focus has been knowledge representation, with an emphasis on semantic-web ontologies and on clinical-guideline based decision-support. Among others, two topics receive growing attention: integration of computer-interpretable guideline knowledge for the management of multimorbidity patients, and patient empowerment and patient-centric care., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Background: Correct clinical management of circulatory shock in emergency departments (ER) and intensive care units (ICU) is critical. In this context, the transmission of professional skills by means of the practical supervision of real cases at the point of care entails important issues that can be widely overcome with the use of computer knowledge-based e-learning tools., Objective: Shock-Instructor is a web-based e-learning tool implementing the already tested training program model (TPM) that uses a knowledge base about the evidence found in the clinical practice guidelines about seven types of shock. This tool is expected to reduce the learning times and to improve the skills of hospital residents with regard to both the correct application of the guidelines and patient recovery, suppressing the risks of direct interventions., Methods: Shock-Instructor has been used to train residents in the Emergency Department of the Hospital Clínic de Barcelona (Spain) in order to reduce the learning cycle without affecting quality. A case-base with the description of 51 cases with shock and a knowledge-base with 137 clinical rules about the treatment of shock were incorporated to the Shock-Instructor system. A group of 33 residents was involved in a randomized controlled trial to check whether the use of Shock-Instructor can significantly improve the skills of clinicians after one week of problem-based training., Results: No significant differences were found in the skill levels of the intervention (IG) and control (CG) groups prior to learning. However, we observed an improvement of the IG clinicians capacity to stabilize patients with shock in better clinical conditions (5% improvement, p = 0.004), and to reduce the risk of death in 19.52% (p = 0.004), after training. First-year residents in IG enhanced 14.3% their sensitivity in the correct application of guidelines (p = 0.01), and 14.9% the mean survival rate of their patients (p = 0.01), after being trained with Shock-Instructor for a week. Residents with specialties different from ER and ICU enhanced 16.1% their application of guidelines (p = 0.04), and 14.5% the survival rate of the cases attended (p = 0.01)., Conclusions: Web-oriented knowledge-based e-learning computer tools such as Shock-Instructor can significantly reduce the learning time of ER and ICU residents, while enhancing their assimilation of evidence-based medicine and both the survival rate and health condition of patients with shock., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The high prevalence of multimorbid cases is a challenge for Health-Care Systems today. Clinical practice guidelines are the means to register and transmit the available evidence-based medical knowledge concerning concrete diseases. Several computer languages have been defined to represent this knowledge in a way that computers could use to help physicians in the daily practice of medicine. The generation of guidelines for all possible multimorbidities entails several issues that are difficult to address. Consequently, numerous medical informatics technologies have appeared merging computer information structures in a way that the treatment knowledge about single diseases could be combined in order to deliver health-care to patients suffering from multimorbidity. This paper proposes a classification of the most promising current technologies addressing this issue and provides an analysis of their maturity, strengths, and weaknesses. We conclude with an enumeration of ten relevant issues to consider when developing such technologies., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment., Competing Interests: The authors have declared that no competing interests exist.

Chronically ill patients are complex health care cases that require the coordinated interaction of multiple professionals. A correct intervention of these sort of patients entails the accurate analysis of the conditions of each concrete patient and the adaptation of evidence-based standard intervention plans to these conditions. There are some other clinical circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases or prevention, whose detection depends on the capacities of deduction of the professionals involved. In this paper, we introduce an ontology for the care of chronically ill patients and implement two personalization processes and a decision support tool. The first personalization process adapts the contents of the ontology to the particularities observed in the health-care record of a given concrete patient, automatically providing a personalized ontology containing only the clinical information that is relevant for health-care professionals to manage that patient. The second personalization process uses the personalized ontology of a patient to automatically transform intervention plans describing health-care general treatments into individual intervention plans. For comorbid patients, this process concludes with the semi-automatic integration of several individual plans into a single personalized plan. Finally, the ontology is also used as the knowledge base of a decision support tool that helps health-care professionals to detect anomalous circumstances such as wrong diagnoses, unobserved comorbidities, missing information, unobserved related diseases, or preventive actions. Seven health-care centers participating in the K4CARE project, together with the group SAGESA and the Local Health System in the town of Pollenza have served as the validation platform for these two processes and tool. Health-care professionals participating in the evaluation agree about the average quality 84% (5.9/7.0) and utility 90% (6.3/7.0) of the tools and also about the correct reasoning of the decision support tool, according to clinical standards., (Copyright © 2012 Elsevier Inc. All rights reserved.)