Clinical Decision Support Systems for Palliative Care Referral: Design and Evaluation of Frailty and Mortality Predictive Models

[ES] Los Cuidados Paliativos (PC) son cuidados médicos especializados cuyo objetivo esmejorar la calidad de vida de los pacientes con enfermedades graves. Históricamente,se han aplicado a los pacientes en fase terminal, especialmente a los que tienen undiagnóstico oncológico. Sin embargo, los resultados de las investigaciones actualessugieren que la PC afecta positivamente a la calidad de vida de los pacientes condiferentes enfermedades. La tendencia actual sobre la PC es incluir a pacientes nooncológicos con afecciones como la EPOC, la insuficiencia de funciones orgánicas ola demencia. Sin embargo, la identificación de los pacientes con esas necesidades escompleja, por lo que se requieren herramientas alternativas basadas en datos clínicos. La creciente demanda de PC puede beneficiarse de una herramienta de cribadopara identificar a los pacientes con necesidades de PC durante el ingreso hospitalario.Se han propuesto varias herramientas, como la Pregunta Sorpresa (SQ) o la creaciónde diferentes índices y puntuaciones, con distintos grados de éxito. Recientemente,el uso de algoritmos de inteligencia artificial, en concreto de Machine Learning (ML), ha surgido como una solución potencial dada su capacidad de aprendizaje a partirde las Historias Clínicas Electrónicas (EHR) y con la expectativa de proporcionarpredicciones precisas para el ingreso en programas de PC. Esta tesis se centra en la creación de herramientas digitales basadas en ML para la identificación de pacientes con necesidades de cuidados paliativos en el momento del ingreso hospitalario. Hemos utilizado la mortalidad y la fragilidad como los dos criterios clínicos para la toma de decisiones, siendo la corta supervivencia y el aumento de la fragilidad, nuestros objetivos para hacer predicciones. También nos hemos centrado en la implementación de estas herramientas en entornos clínicos y en el estudio de su usabilidad y aceptación en los flujos de trabajo clínicos. Para lograr estos objetivos, en primer lugar, estudiamos y comparamos algoritmos de ML para la supervivencia a un año en pacientes adultos durante el ingreso hospitalario. Para ello, definimos una variable binaria a predecir, equivalente a la SQ y definimos el conjunto de variables predictivas basadas en la literatura. Comparamos modelos basados en Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Random Forest (RF), Gradient Boosting Machine (GBM) y Multilayer Perceptron (MLP), atendiendo a su rendimiento, especialmente al Área bajo la curva ROC (AUC ROC). Además, obtuvimos información sobre la importancia de las variables para los modelos basados en árboles utilizando el criterio GINI. En segundo lugar, estudiamos la medición de la fragilidad de la calidad de vida(QoL) en los candidatos a la intervención en PC. Para este segundo estudio, redujimosla franja de edad de la población a pacientes ancianos (≥ 65 años) como grupo objetivo. A continuación, creamos tres modelos diferentes: 1) la adaptación del modelo demortalidad a un año para pacientes ancianos, 2) un modelo de regresión para estimarel número de días desde el ingreso hasta la muerte para complementar los resultadosdel primer modelo, y finalmente, 3) un modelo predictivo del estado de fragilidad aun año. Estos modelos se compartieron con la comunidad académica a través de unaaplicación web b que permite la entrada de datos y muestra la predicción de los tresmodelos y unos gráficos con la importancia de las variables. En tercer lugar, propusimos una versión del modelo de mortalidad a un año enforma de calculadora online. Esta versión se diseñó para maximizar el acceso de losprofesionales minimizando los requisitos de datos y haciendo que el software respondiera a las plataformas tecnológicas actuales. Así pues, se eliminaron las variablesadministrativas específicas de la fuente de datos y se trabajó en un proceso para minimizar las variables de entrada requeridas, manteniendo al mismo tiempo un ROCAUC elevado del modelo. Como resultado, e[CA] Les Cures Pal·liatives (PC) són cures mèdiques especialitzades l'objectiu de les qualsés millorar la qualitat de vida dels pacients amb malalties greus. Històricament, s'hanaplicat als pacients en fase terminal, especialment als quals tenen un diagnòstic oncològic. No obstant això, els resultats de les investigacions actuals suggereixen que lesPC afecten positivament a la qualitat de vida dels pacients amb diferents malalties. Latendència actual sobre les PC és incloure a pacients no oncològics amb afeccions comla malaltia pulmonar obstructiva crònica, la insuficiència de funcions orgàniques o lademència. No obstant això, la identificació dels pacients amb aqueixes necessitats éscomplexa, per la qual cosa es requereixen eines alternatives basades en dades clíniques. La creixent demanda de PC pot beneficiar-se d'una eina de garbellat per a identificar als pacients amb necessitats de PC durant l'ingrés hospitalari. S'han proposatdiverses eines, com la Pregunta Sorpresa (SQ) o la creació de diferents índexs i puntuacions, amb diferents graus d'èxit. Recentment, l'ús d'algorismes d'intel·ligènciaartificial, en concret de Machine Learning (ML), ha sorgit com una potencial soluciódonada la seua capacitat d'aprenentatge a partir de les Històries Clíniques Electròniques (EHR) i amb l'expectativa de proporcionar prediccions precises per a l'ingrés enprogrames de PC. Aquesta tesi se centra en la creació d'eines digitals basades en MLper a la identificació de pacients amb necessitats de cures pal·liatives durant l'ingréshospitalari. Hem utilitzat mortalitat i fragilitat com els dos criteris clínics per a lapresa de decisions, sent la curta supervivència i la major fragilitat els nostres objectiusa predir. Després, ens hem centrat en la seua implementació en entorns clínics i hemestudiat la seua usabilitat i acceptació en els fluxos de treball clínics.Aquesta tesi se centra en la creació d'eines digitals basades en ML per a la identificació de pacients amb necessitats de cures pal·liatives en el moment de l'ingrés hospitalari. Hem utilitzat la mortalitat i la fragilitat com els dos criteris clínics per ala presa de decisions, sent la curta supervivència i l'augment de la fragilitat, els nostresobjectius per a fer prediccions. També ens hem centrat en la implementació d'aquesteseines en entorns clínics i en l'estudi de la seua usabilitat i acceptació en els fluxos detreball clínics. Per a aconseguir aquests objectius, en primer lloc, estudiem i comparem algorismesde ML per a la supervivència a un any en pacients adults durant l'ingrés hospitalari.Per a això, definim una variable binària a predir, equivalent a la SQ i definim el conjuntde variables predictives basades en la literatura. Comparem models basats en Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Random Forest (RF), Gradient Boosting Machine (GBM) i Multilayer Perceptron (MLP), atenent el seu rendiment,especialment a l'Àrea sota la corba ROC (AUC ROC). A més, vam obtindre informaciósobre la importància de les variables per als models basats en arbres utilitzant el criteri GINI. En segon lloc, estudiem el mesurament de la fragilitat de la qualitat de vida (QoL)en els candidats a la intervenció en PC. Per a aquest segon estudi, vam reduir lafranja d'edat de la població a pacients ancians (≥ 65 anys) com a grup objectiu. Acontinuació, creem tres models diferents: 1) l'adaptació del model de mortalitat a unany per a pacients ancians, 2) un model de regressió per a estimar el nombre de dies desde l'ingrés fins a la mort per a complementar els resultats del primer model, i finalment,3) un model predictiu de l'estat de fragilitat a un any. Aquests models es van compartiramb la comunitat acadèmica a través d'una aplicació web c que permet l'entrada dedades i mostra la predicció dels tres models i uns gràfics amb la importància de lesvariables. En tercer lloc, vam proposar una versió del model de mortalitat a un any en formade calculadora en línia. Aquesta versió es va di[EN] Palliative Care (PC) is specialized medical care that aims to improve patients' quality of life with serious illnesses. Historically, it has been applied to terminally ill patients, especially those with oncologic diagnoses. However, current research results suggest that PC positively affects the quality of life of patients with different conditions. The current trend on PC is to include non-oncological patients with conditions such as Chronic Obstructive Pulmonary Disease (COPD), organ function failure or dementia. However, the identification of patients with those needs is complex, and therefore alternative tools based on clinical data are required. The growing demand for PC may benefit from a screening tool to identify patients with PC needs during hospital admission. Several tools, such as the Surprise Question (SQ) or the creation of different indexes and scores, have been proposed with varying degrees of success. Recently, the use of artificial intelligence algorithms, specifically Machine Learning (ML), has arisen as a potential solution given their capacity to learn from the Electronic Health Records (EHRs) and with the expectation to provide accurate predictions for admission to PC programs. This thesis focuses on creating ML-based digital tools for identifying patients with palliative care needs at hospital admission. We have used mortality and frailty as the two clinical criteria for decision-making, being short survival and increased frailty, as our targets to make predictions. We also have focused on implementing these tools in clinical settings and studying their usability and acceptance in clinical workflows. To accomplish these objectives, first, we studied and compared ML algorithms for one-year survival in adult patients during hospital admission. To do so, we defined a binary variable to predict, equivalent to the SQ and defined the set of predictive variables based on literature. We compared models based on Support Vector Machine (SVM), k-Nearest Neighbours (kNN), Random Forest (RF), Gradient Boosting Machine (GBM) and Multilayer Perceptron (MLP), attending to their performance, especially to the Area under the ROC curve (AUC ROC). Additionally, we obtained information on the importance of variables for tree-based models using the GINI criterion. Second, we studied frailty measurement of Quality of Life (QoL) in candidates for PC intervention. For this second study, we narrowed the age of the population to elderly patients (≥ 65 years) as the target group. Then we created three different models: 1) for the adaptation of the one-year mortality model for elderly patients, 2) a regression model to estimate the number of days from admission to death to complement the results of the first model, and finally, 3) a predictive model for frailty status at one year. These models were shared with the academic community through a web application a that allows data input and shows the prediction from the three models and some graphs with the importance of the variables. Third, we proposed a version of the 1-year mortality model in the form of an online calculator. This version was designed to maximize access from professionals by minimizing data requirements and making the software responsive to the current technological platforms. So we eliminated the administrative variables specific to the dataset source and worked on a process to minimize the required input variables while maintaining high the model's AUC ROC. As a result, this model retained most of the predictive power and required only seven bed-side inputs. Finally, we evaluated the Clinical Decision Support System (CDSS) web tool on PC with an actual set of users. This evaluation comprised three domains: evaluation of participant's predictions against the ML baseline, the usability of the graphical interface, and user experience measurement. A first evaluation was performed, followed by a period of implementation of improvements and corrections to the plaBlanes Selva, V. (2022). Clinical Decision Support Systems for Palliative Care Referral: Design and Evaluation of Frailty and Mortality Predictive Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19099

Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs

[EN] Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Bad survival prognosis and patients' decline are working criteria to guide PC decision-making for older patients. Still, there is not a clear consensus on when to initiate early PC. This work aims to propose machine learning approaches to predict frailty and mortality in older patients in supporting PC decision-making. Predictive models based on Gradient Boosting Machines (GBM) and Deep Neural Networks (DNN) were implemented for binary 1-year mortality classification, survival estimation and 1-year frailty classification. Besides, we tested the similarity between mortality and frailty distributions. The 1-year mortality classifier achieved an Area Under the Curve Receiver Operating Characteristic (AUC ROC) of 0.87 [0.86, 0.87], whereas the mortality regression model achieved an mean absolute error (MAE) of 333.13 [323.10, 342.49] days. Moreover, the 1-year frailty classifier obtained an AUC ROC of 0.89 [0.88, 0.90]. Mortality and frailty criteria were weakly correlated and had different distributions, which can be interpreted as these assessment measurements are complementary for PC decision-making. This study provides new models that can be part of decision-making systems for PC services in older patients after their external validation.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the InAdvance project (H2020-SC1-BHC-2018-2020 No. 825750).Blanes-Selva, V.; Doñate-Martínez, A.; Linklater, G.; Garcia-Gomez, JM. (2022). Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs. Health Informatics Journal. 28(2):1-18. https://doi.org/10.1177/1460458222109259211828

A user-centered chatbot to identify and interconnect individual, social and environmental risk factors related to overweight and obesity

[EN] The objective of this study was to assess the feasibility of using a user-centered chatbotfor collecting linked data to study overweight and obesity causes ina target population. In total 980 people participated in the feasibility study organized in three studies: (1) within a group of university students (88 participants), (2) in a small town (422 participants), and (3) within a university community (470 participants). We gathered self-reported data through the Wakamola chatbot regarding participants diet, physical activity, social network, living area, obesity-associated diseases, and sociodemographic data. For each study, we calculated the mean Body Mass Index (BMI) and number of people in each BMI level. Also, we defined and calculated scores (1-100 scale) regarding global health, BMI, alimentation, physical activity and social network. Moreover, we graphically represented obesity risk for living areas and the social network with nodes colored by BMI. Students group results: Mean BMI 21.37 (SD 2.57) (normal weight), 8 people underweight, 5 overweight, 0 obesity, global health status 78.21, alimentation 63.64, physical activity 65.08 and social 26.54, 3 areas with mean BMI level of obesity, 17 with overweight level. Small town ' s study results: Mean BMI 25.66 (SD 4.29) (overweight), 2 people underweight, 63 overweight, 26 obesity, global health status 69.42, alimentation 64.60, physical activity 60.61 and social 1.14, 1 area with mean BMI in normal weight; University ' s study results: Mean BMI 23.63 (SD 3.7) (normal weight), 22 people underweight, 86 overweight, 28 obesity, global health status 81.03, alimentation 81.84, physical activity 70.01 and social 1.47, 3 areas in obesity level, 19 in overweight level. Wakamola is a health care chatbot useful to collect relevant data from populations in the risk of overweight and obesity. Besides, the chatbot provides individual self-assessment of BMI and general status regarding the style of living. Moreover, Wakamola connects users in a social network to help the study of O & O ' s causes from an individual, social and socio-economic perspective.Funding for this study was provided by the authors' various departments, and partially by the Crowd Health Project (Collective Wisdom Driving Public Health Policies [727560]).Asensio-Cuesta, S.; Blanes-Selva, V.; Conejero, JA.; Portolés, M.; Garcia-Gomez, JM. (2022). A user-centered chatbot to identify and interconnect individual, social and environmental risk factors related to overweight and obesity. Informatics for Health and Social Care. 47(1):38-52. https://doi.org/10.1080/17538157.2021.1923501385247

Responsive and Minimalist App Based on Explainable AI to Assess Palliative Care Needs during Bedside Consultations on Older Patients

[EN] Palliative care is an alternative to standard care for gravely ill patients that has demonstrated many clinical benefits in cost-effective interventions. It is expected to grow in demand soon, so it is necessary to detect those patients who may benefit from these programs using a personalised objective criterion at the correct time. Our goal was to develop a responsive and minimalist web application embedding a 1-year mortality explainable predictive model to assess palliative care at bedside consultation. A 1-year mortality predictive model has been trained. We ranked the input variables and evaluated models with an increasing number of variables. We selected the model with the seven most relevant variables. Finally, we created a responsive, minimalist and explainable app to support bedside decision making for older palliative care. The selected variables are age, medication, Charlson, Barthel, urea, RDW-SD and metastatic tumour. The predictive model achieved an AUC ROC of 0.83 [CI: 0.82, 0.84]. A Shapley value graph was used for explainability. The app allows identifying patients in need of palliative care using the bad prognosis criterion, which can be a useful, easy and quick tool to support healthcare professionals in obtaining a fast recommendation in order to allocate health resources efficiently.This work was supported by the InAdvance project (H2020-SC1-BHC-2018-2020 grant agreement number 825750.) and the CANCERLEss project (H2020-SC1-2020-Single-Stage-RTD grant agreement number 965351), both funded by the European Union's Horizon 2020 research and innovation programme.Blanes-Selva, V.; Doñate-Martínez, A.; Linklater, G.; Garcés-Ferrer, J.; Garcia-Gomez, JM. (2021). Responsive and Minimalist App Based on Explainable AI to Assess Palliative Care Needs during Bedside Consultations on Older Patients. Sustainability. 13(17):1-11. https://doi.org/10.3390/su13179844111131

User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals

[EN] Objective:Although clinical decision support systems (CDSS) have many benefits for clinical practice, they also have several barriers to their acceptance by professionals. Our objective in this study was to design and validate The Aleph palliative care (PC) CDSS through a user-centred method, considering the predictions of the artificial intelligence (AI) core, usability and user experience (UX). Methods:We performed two rounds of individual evaluation sessions with potential users. Each session included a model evaluation, a task test and a usability and UX assessment. Results:The machine learning (ML) predictive models outperformed the participants in the three predictive tasks. System Usability Scale (SUS) reported 62.7 +/- 14.1 and 65 +/- 26.2 on a 100-point rating scale for both rounds, respectively, while User Experience Questionnaire - Short Version (UEQ-S) scores were 1.42 and 1.5 on the -3 to 3 scale. Conclusions:The think-aloud method and including the UX dimension helped us to identify most of the workflow implementation issues. The system has good UX hedonic qualities; participants were interested in the tool and responded positively to it. Performance regarding usability was modest but acceptable.The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the InAdvance project (H2020-SC1-BHC-2018¿2020 grant number 825750) and the CANCERLESS project (H2020-SC1-2020-Single-Stage-RTD grant number 965351), both funded by the European Union¿s Horizon 2020 research and innovation programme. Also, it was partially supported by the ALBATROSS project (National Plan for Scientific and Technical Research and Innovation 2017¿ 2020, grant number PID2019-104978RB-I00)Blanes-Selva, V.; Asensio-Cuesta, S.; Doñate-Martínez, A.; Pereira Mesquita, F.; Garcia-Gomez, JM. (2023). User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals. Digital Health. 9:1-13. https://doi.org/10.1177/20552076221150735113

Functional requirements to mitigate the Risk of Harm to Patients from Artificial Intelligence in Healthcare

The Directorate General for Parliamentary Research Services of the European Parliament has prepared a report to the Members of the European Parliament where they enumerate seven main risks of Artificial Intelligence (AI) in medicine and healthcare: patient harm due to AI errors, misuse of medical AI tools, bias in AI and the perpetuation of existing inequities, lack of transparency, privacy and security issues, gaps in accountability, and obstacles in implementation. In this study, we propose fourteen functional requirements that AI systems may implement to reduce the risks associated with their medical purpose: AI passport, User management, Regulation check, Academic use only disclaimer, data quality assessment, Clinicians double check, Continuous performance evaluation, Audit trail, Continuous usability test, Review of retrospective/simulated cases, Bias check, eXplainable AI, Encryption and use of field-tested libraries, and Semantic interoperability. Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.Comment: 14 pages, 1 figure, 1 tabl

Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch

[EN] The objective of this work was to develop a predictive model to aid non-clinical dispatchers to classify emergency medical call incidents by their life-threatening level (yes/no), admissible response delay (undelayable, minutes, hours, days) and emergency system jurisdiction (emergency system/primary care) in real time. We used a total of 1 244 624 independent incidents from the Valencian emergency medical dispatch service in Spain, compiled in retrospective from 2009 to 2012, including clinical features, demographics, circumstantial factors and free text dispatcher observations. Based on them, we designed and developed DeepEMC2, a deep ensemble multitask model integrating four subnetworks: three specialized to context, clinical and text data, respectively, and another to ensemble the former. The four subnetworks are composed in turn by multi-layer perceptron modules, bidirectional long short-term memory units and a bidirectional encoding representations from transformers module. DeepEMC2 showed a macro F1-score of 0.759 in life-threatening classification, 0.576 in admissible response delay and 0.757 in emergency system jurisdiction. These results show a substantial performance increase of 12.5 %, 17.5 % and 5.1 %, respectively, with respect to the current in-house triage protocol of the Valencian emergency medical dispatch service. Besides, DeepEMC2 significantly outperformed a set of baseline machine learning models, including naive bayes, logistic regression, random forest and gradient boosting (¿ = 0.05). Hence, DeepEMC2 is able to: 1) capture information present in emergency medical calls not considered by the existing triage protocol, and 2) model complex data dependencies not feasible by the tested baseline models. Likewise, our results suggest that most of this unconsidered information is present in the free text dispatcher observations. To our knowledge, this study describes the first deep learning model undertaking emergency medical call incidents classification. Its adoption in medical dispatch centers would potentially improve emergency dispatch processes, resulting in a positive impact in patient wellbeing and health services sustainability.This work has been supported by the Valencian agency for security and emergency response project A1800173041, the Ministry of Science, Innovation and Universities of Spain program FPU18/06441 and the EU Horizon 2020 project InAdvance 825750Ferri-Borredà, P.; Sáez Silvestre, C.; Felix-De Castro, A.; Juan-Albarracín, J.; Blanes-Selva, V.; Sánchez-Cuesta, P.; Garcia-Gomez, JM. (2021). Deep ensemble multitask classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch. Artificial Intelligence in Medicine. 117:1-13. https://doi.org/10.1016/j.artmed.2021.102088S11311

A User-Centered Chatbot (Wakamola) to Collect Linked Data in Population Networks to Support Studies of Overweight and Obesity Causes: Design and Pilot Study

[EN] Background: Obesity and overweight are a serious health problem worldwide with multiple and connected causes. Simultaneously, chatbots are becoming increasingly popular as a way to interact with users in mobile health apps. Objective: This study reports the user-centered design and feasibility study of a chatbot to collect linked data to support the study of individual and social overweight and obesity causes in populations. Methods: We first studied the users' needs and gathered users' graphical preferences through an open survey on 52 wireframes designed by 150 design students; it also included questions about sociodemographics, diet and activity habits, the need for overweight and obesity apps, and desired functionality. We also interviewed an expert panel. We then designed and developed a chatbot. Finally, we conducted a pilot study to test feasibility. Results: We collected 452 answers to the survey and interviewed 4 specialists. Based on this research, we developed a Telegram chatbot named Wakamola structured in six sections: personal, diet, physical activity, social network, user's status score, and project information. We defined a user's status score as a normalized sum (0-100) of scores about diet (frequency of eating 50 foods), physical activity, BMI, and social network. We performed a pilot to evaluate the chatbot implementation among 85 healthy volunteers. Of 74 participants who completed all sections, we found 8 underweight people (11%), 5 overweight people (7%), and no obesity cases. The mean BMI was 21.4 kg/m(2) (normal weight). The most consumed foods were olive oil, milk and derivatives, cereals, vegetables, and fruits. People walked 10 minutes on 5.8 days per week, slept 7.02 hours per day, and were sitting 30.57 hours per week. Moreover, we were able to create a social network with 74 users, 178 relations, and 12 communities. Conclusions: The Telegram chatbot Wakamola is a feasible tool to collect data from a population about sociodemographics, diet patterns, physical activity, BMI, and specific diseases. Besides, the chatbot allows the connection of users in a social network to study overweight and obesity causes from both individual and social perspectives.Moreover, the authors acknowledge the funding support for this study provided by the CrowdHealth Project (Collective Wisdom Driving Public Health Policies, 727560).Asensio-Cuesta, S.; Blanes-Selva, V.; Conejero, JA.; Frigola, A.; Portolés, MG.; Merino-Torres, JF.; Rubio Almanza, M.... (2021). A User-Centered Chatbot (Wakamola) to Collect Linked Data in Population Networks to Support Studies of Overweight and Obesity Causes: Design and Pilot Study. JMIR Medical Informatics. 9(4):1-14. https://doi.org/10.2196/17503S1149

Modelo de bases de datos e inteligencia artificial para el juego de cartas coleccionables 'Magic: The Gathering'

[ES] En el juego de cartas coleccionables Magic: The Gathering se nos plantea un problema de elección: un jugador dispone de un conjunto de alrededor de 64 cartas y debe elegir las 23 mejores para incrementar sus posibilidades de éxito. El problema estriba en que no todas las posibilidades son factibles y además las interacciones entre cartas suelen jugar un papel muy importante, esto influye en que la elección del conjunto óptimo no sea sencilla. Para llevar a cabo esta tarea hemos construido una aplicación modular en Python 3 cuyas partes principales han sido: un módulo de web scrapping para recuperar información desde Internet, un conjunto de funciones para guardarla y manipularla haciendo uso de bases de datos y el propio algoritmo constructor de mazos que hace uso de toda la información disponible para hacer la selección. El desarrollo se ha completado con varios módulos de pruebas parametrizados que permiten ajustar el comportamiento del algoritmo dependiendo de la edición de cartas que este tratando, lo que nos ayuda a ajustarnos a un comportamiento más cercano al conocimiento experto.[CA] En el joc de cartes coleccionables Magic: The gathering se’ns planteja un problema d’elecció: un jugador disposa d’un conjunt d’unes 64 cartes y deu escollir les 23 millors per a augmentar les seues probabilitats d’èxit. El problema consistix en que no totes les posibilitats son factibles i a més les interaccions entre cartes solen jugar un paper molt important, tot aço influix en que la elecció del conjunt òptim no siga senzilla. Per a realitzar aquesta feina, hem construit una aplicació modular en Python 3 les parts principals de la qual han sigut: un mòdul de web scrapping per a recuperar informació d’internet, un conjunt de funcions per a guardarla i manipularla utilitzant bases de dades i el propi algoritme constructor de baralles que fa ús de tota la informació disponible per a realitzar la selecció. El desenvolupament s’ha realitzat amb diversos mòduls de proves parametritzats que permeten ajustar el comportament de l’algoritme depenent de l’edició de cartes amb la que estem tractant, lo que ens ajuda a ajustar-nos a un comportament més proper al coneixement expert.[EN] There is a trading card game Magic: The Gathering that brings up an election issue: A game player has around a 64 cards and he has to choose the best 23rd cards, so he can improve his chances to win. The problem is, in one hand that not all the chances are feasible and in the other that an important role is played by the interaction between the cards so it makes the choice of the ideal combination quite difficult for the player. With a view to accomplish this task, we have made a en Python 3 modular application which main parts are: A web scrapping module to recover all the information; Some function as a whole to save it and keep it, plus using the database we can manipulate it; And finally, the deck cards builder algorithm which uses all the available information to make the selection. Development of this application have been completed with some parameterized proof modules in order to adjust how the algorithm works when it does it with an edition of cards or another different. It helps us to adjust us to a closer performance of an expert knowledgeBlanes Selva, V. (2017). Modelo de bases de datos e inteligencia artificial para el juego de cartas coleccionables 'Magic: The Gathering'. http://hdl.handle.net/10251/87005.TFG