How priming with body odors affects decision speeds in consumer behavior

To date, odor research has primarily focused on the behavioral efects of common odors on consumer perception and choices. We report a study that examines, for the frst time, the efects of human body odor cues on consumer purchase behaviors. The infuence of human chemosignals produced in three conditions, namely happiness, fear, a relaxed condition (rest), and a control condition (no odor), were examined on willingness to pay (WTP) judgments across various products. We focused on the speed with which participants reached such decisions. The central fnding revealed that participants exposed to human odors reached decisions signifcantly faster than the no odor control group. The main driving force is that human body odors activate the presence of others during decision-making. This, in turn, afects response speed. The broader implications of this fnding for consumer behavior are discussed.Comunidade Europeia e Generalitat Valencianainfo:eu-repo/semantics/publishedVersio

Predicting morbidity by local similarities in multi-scale patient trajectories

[EN] Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.This work was supported by the CrowdHealth project (COLLECTIVE WISDOM DRIVING PUBLIC HEALTH POLICIES (727560)) and the MTS4up project (DPI2016-80054-R).Carrasco-Ribelles, LA.; Pardo-Más, JR.; Tortajada, S.; Sáez Silvestre, C.; Valdivieso, B.; Garcia-Gomez, JM. (2021). Predicting morbidity by local similarities in multi-scale patient trajectories. Journal of Biomedical Informatics. 120:1-9. https://doi.org/10.1016/j.jbi.2021.103837S1912

Contribution of frailty to multimorbidity patterns and trajectories: Longitudinal dynamic cohort study of aging people

Background: Multimorbidity and frailty are characteristics of aging that need individualized evaluation, and there is a 2-way causal relationship between them. Thus, considering frailty in analyses of multimorbidity is important for tailoring social and health care to the specific needs of older people. Objective: This study aimed to assess how the inclusion of frailty contributes to identifying and characterizing multimorbidity patterns in people aged 65 years or older. Methods: Longitudinal data were drawn from electronic health records through the SIDIAP (Sistema d’Informació pel Desenvolupament de la Investigació a l’Atenció Primària) primary care database for the population aged 65 years or older from 2010 to 2019 in Catalonia, Spain. Frailty and multimorbidity were measured annually using validated tools (eFRAGICAP, a cumulative deficit model; and Swedish National Study of Aging and Care in Kungsholmen [SNAC-K], respectively). Two sets of 11 multimorbidity patterns were obtained using fuzzy c-means. Both considered the chronic conditions of the participants. In addition, one set included age, and the other included frailty. Cox models were used to test their associations with death, nursing home admission, and home care need. Trajectories were defined as the evolution of the patterns over the follow-up period. Results: The study included 1,456,052 unique participants (mean follow-up of 7.0 years). Most patterns were similar in both sets in terms of the most prevalent conditions. However, the patterns that considered frailty were better for identifying the population whose main conditions imposed limitations on daily life, with a higher prevalence of frail individuals in patterns like chronic ulcers &peripheral vascular. This set also included a dementia-specific pattern and showed a better fit with the risk of nursing home admission and home care need. On the other hand, the risk of death had a better fit with the set of patterns that did not include frailty. The change in patterns when considering frailty also led to a change in trajectories. On average, participants were in 1.8 patterns during their follow-up, while 45.1% (656,778/1,456,052) remained in the same pattern. Conclusions: Our results suggest that frailty should be considered in addition to chronic diseases when studying multimorbidity patterns in older adults. Multimorbidity patterns and trajectories can help to identify patients with specific needs. The patterns that considered frailty were better for identifying the risk of certain age-related outcomes, such as nursing home admission or home care need, while those considering age were better for identifying the risk of death. Clinical and social intervention guidelines and resource planning can be tailored based on the prevalence of these patterns and trajectories.The project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded in 2019 under the Health Strategy Action 2013-2016, within the National Research Programme oriented to Societal Challenges, within the Technical, Scientific and Research National Plan 2013-2016 (reference PI19/00535), and the PFIS Grant FI20/00040, co-funded with European Union ERDF (European Regional Development Fund) funds.Peer ReviewedPostprint (published version

Prediction models using artificial intelligence and longitudinal data from electronic health records: a systematic methodological review

Objective: To describe and appraise the use of artificial intelligence (AI) techniques that can cope with longitudinal data from electronic health records (EHRs) to predict health-related outcomes. Methods: This review included studies in any language that: EHR was at least one of the data sources, collected longitudinal data, used an AI technique capable of handling longitudinal data, and predicted any health-related outcomes. We searched MEDLINE, Scopus, Web of Science, and IEEE Xplorer from inception to January 3, 2022. Information on the dataset, prediction task, data preprocessing, feature selection, method, validation, performance, and implementation was extracted and summarized using descriptive statistics. Risk of bias and completeness of reporting were assessed using a short form of PROBAST and TRIPOD, respectively. Results: Eighty-one studies were included. Follow-up time and number of registers per patient varied greatly, and most predicted disease development or next event based on diagnoses and drug treatments. Architectures generally were based on Recurrent Neural Networks-like layers, though in recent years combining different layers or transformers has become more popular. About half of the included studies performed hyperparameter tuning and used attention mechanisms. Most performed a single train-test partition and could not correctly assess the variability of the model’s performance. Reporting quality was poor, and a third of the studies were at high risk of bias. Conclusions: AI models are increasingly using longitudinal data. However, the heterogeneity in reporting methodology and results, and the lack of public EHR datasets and code sharing, complicate the possibility of replication.The project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded on the 2019 call under the Health Strategy Action 2013-2016, within the National Research Programme oriented to Societal Challenges, within the Technical, Scientific and Innovation Research National Plan 2013-2016 (reference PI19/00535), and the PFIS Grant FI20/00040, cofunded with European Union ERDF (European Regional Development Fund) funds. The project has also been partially funded by Generalitat de Catalunya through the AGAUR (grant numbers 2021-SGR-01033, 2021-SGR-01537).Peer ReviewedPostprint (published version

Dynamics of multimorbidity and frailty, and their contribution to mortality, nursing home and home care need: A primary care cohort of 1 456 052 ageing people

Envejecimiento; Fragilidad; Mortalidad; Atención primariaAging; Fragility; Mortality; Primary health careEnvelliment; Fragilitat; Mortalitat; Atenció primàriaBackground: Prevalence of both multimorbidity and frailty increases with age, but more evidence is needed to elucidate their relationship and their association with other health-related outcomes. We analysed the dynamics of both conditions as people age and calculate the associated risk of death, nursing home admission, and need for home care. Methods: Data were drawn from the primary care electronic health records of a longitudinal cohort of people aged 65 or older in Catalonia in 2010-2019. Frailty and multimorbidity were measured using validated instruments (eFRAGICAP, a cumulative deficit model; and SNAC-K, respectively), and their longitudinal evolution was described. Cox regression models accounted for the competing risk of death and adjusted by sex, socioeconomical status, and time-varying age, alcohol and smoking. Findings: We included 1 456 052 patients. Prevalence of multimorbidity was consistently high regardless of age, while frailty almost quadrupled from 65 to 99 years. Frailty worsened and also changed with age: up to 84 years, it was more related to concurrent diseases, and afterwards, to frailty-related deficits. While concurrent diseases contributed more to mortality, frailty-related deficits increased the risk of institutionalisation and the need for home care. Interpretation: The nature of people's multimorbidity and frailty vary with age, as does their impact on health status. People become frailer as they age, and their frailty is more characterised by disability and other symptoms than by diseases. Mortality is most associated with the number of comorbidities, whereas frailty-related deficits are associated with needing specialised care.Instituto de Salud Carlos III through PI19/00535, and the PFIS Grant FI20/00040 (Co-funded by European Regional Development Fund/European Social Fund)

Dynamics of multimorbidity and frailty, and their contribution to mortality, nursing home and home care need: A primary care cohort of 1 456 052 ageing people

Background: Prevalence of both multimorbidity and frailty increases with age, but more evidence is needed to elucidate their relationship and their association with other health-related outcomes. We analysed the dynamics of both conditions as people age and calculate the associated risk of death, nursing home admission, and need for home care. Methods: Data were drawn from the primary care electronic health records of a longitudinal cohort of people aged 65 or older in Catalonia in 2010–2019. Frailty and multimorbidity were measured using validated instruments (eFRAGICAP, a cumulative deficit model; and SNAC-K, respectively), and their longitudinal evolution was described. Cox regression models accounted for the competing risk of death and adjusted by sex, socioeconomical status, and time-varying age, alcohol and smoking. Findings: We included 1 456 052 patients. Prevalence of multimorbidity was consistently high regardless of age, while frailty almost quadrupled from 65 to 99 years. Frailty worsened and also changed with age: up to 84 years, it was more related to concurrent diseases, and afterwards, to frailty-related deficits. While concurrent diseases contributed more to mortality, frailty-related deficits increased the risk of institutionalisation and the need for home care. Interpretation: The nature of people’s multimorbidity and frailty vary with age, as does their impact on health status. People become frailer as they age, and their frailty is more characterised by disability and other symptoms than by diseases. Mortality is most associated with the number of comorbidities, whereas frailty-related deficits are associated with needing specialised care.The project received a research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded on the 2019 call under the Health Strategy Action 2013−2016, within the National Research Programme oriented to Societal Challenges,within the Technical, Scientific and Innovation Research National Plan 2013−2016, (reference PI19/00535), and the PFIS Grant FI20/00040, co-funded with European Union ERDF (European Regional Development Fund) funds. The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of this work.Peer ReviewedPostprint (published version

Desarrollo de un algoritmo de asignación de riesgo de desarrollo de cardiopatías en pacientes diabéticos en base a su ruta clínica

[ES] Introducción: La diabetes es un trastorno metabólico que afecta a más del 10% de la población española, cifra que aumenta cada año. Estos pacientes pueden desarrollar muchas complicaciones y la prevención es crucial para evitar comorbilidades. En este trabajo se desarrolla un algoritmo de programación dinámica que predice el riesgo de que un diabético desarrolle una cardiopatía en base a su historia clínica. Materiales: Se dispone de una base de datos de pacientes diabéticos del Hospital La Fe de Valencia de los años 2012 a 2015 con 9670 pacientes. Metodología: Como base del algoritmo se utilizará el Smith-Waterman, utilizado habitualmente para encontrar alineamientos locales en secuencias genéticas. Para ello, se hace una definición formal de ruta clínica que permite que la historia clínica pueda ser analizada por el algoritmo. En la misma se incluyen eventos de diagnóstico, consultas externas y resultados de laboratorio. Tras una revisión bibliográfica se exponen qué parámetros clínicos son de interés a la hora de predecir cardiopatías en pacientes diabéticos. Se realiza una adaptación del algoritmo Smith-Waterman para tenerlos en cuenta y para encontrar similitudes locales entre las historias de dos pacientes, de forma que, si la ruta clínica de un nuevo paciente se parece mucho a la de un paciente que desarrolló una cardiopatía, se etiqueta al nuevo como en riesgo de desarrollarla también. Se evalúa la importancia de cada uno de los parámetros con diferentes experimentos, así como el método de imputación de riesgo más apropiado. Para ello se divide la base de datos en validación y test y se da el recall, la precisión y la especificidad. Resultados: Se obtiene una lista de parámetros útiles a la hora de predecir cardiopatías en diabéticos, como el tiempo entre eventos, los diagnósticos de cardiologías y la coincidencia del código CIE-9. También un método de imputación que consigue que el algoritmo prediga la aparición de la enfermedad con una precisión, una especificidad y un recall de 0.8, dándole al paciente la condición clínica que predomine entre los 5 individuos a los que más se parezca.[CA] Introducció: La diabetis és un trastorn metabòlic que afecta més del 10% de la població espanyola, xifra que augmenta cada any. Aquests pacients poden desenvolupar moltes complicacions i la prevenció és crucial per evitar comorbiditats. En aquest treball es desenvolupa un algoritme de programació dinàmica que prediu el risc de que un diabètic desenvolupe una cardiopatia en base a la seua història clínica. Com a base de l'algoritme s'utilitzarà el Smith-Waterman, utilitzat habitualment per trobar alineaments locals en seqüències genètiques. Materials: Es disposa d'una base de dades de pacients diabètics de l'Hospital La Fe de València dels anys 2012 a 2015 amb 9670 pacients. Metodologia: Com a base de l'algoritme s'utilitzarà el Smith-Waterman, utilitzat habitualment per trobar alineaments locals en seqüències genètiques. Per a això, es fa una definició formal de ruta clínica que permet que la història clínica puga ser analitzada per l'algoritme. En la mateixa s'inclouen esdeveniments de diagnòstic, consultes externes i resultats de laboratori. Després d'una revisió bibliogràfica s'exposen quins paràmetres clínics són d'interès a l'hora de predir cardiopaties en pacients diabètics. Es realitza una adaptació de l'algorisme SmithWaterman per tindre’ls en compte i per trobar similituds locals entre les històries de dos pacients, de manera que si la ruta clínica d'un nou pacient s'assembla molt a la d'un pacient que va desenvolupar una cardiopatia, es etiqueta al nou com en risc de desenvolupar-la també. S'estudia la importància de cada un dels paràmetres amb diferents experiments, així com el mètode d'imputació de risc més apropiat. Per a això es divideix la base de dades en validació i test i es dóna el recall, la precisió i l'especificitat. Resultats: S'obté una llista de paràmetres útils a l'hora de predir cardiopaties en diabètics, com el temps entre esdeveniments, els diagnòstics de cardiologías i la coincidència del codi CIM-9. També un mètode d'imputació que aconsegueix que l'algoritme prediga l'aparició de la malaltia amb una precisió, una especificitat i un recall de 0.8, donant-li al pacient la condició clínica que predomine entre els 5 individus als que més s'assemble.[EN] Introduction: Diabetes is a metabolic disorder that affects more than 10% of the Spanish population, percentage that increases every year. These patients can develop many complications and prevention is crucial to avoid comorbidities. In this work, a dynamic programming algorithm that predicts the risk that a diabetic will develop a heart disease based on their clinical history is developed. The Smith-Waterman, commonly used to find local alignments in genetic sequences, will be used as the basis of the algorithm. Materials: A database of diabetic patients of the La Fe Hospital in Valencia from 2012 to 2015 with 9670 patients. Methodology: The basis of the algorithm will be the Smith-Waterman, commonly used to find local alignments in genetic sequences. To do this, a formal definition of clinical pathway is made. This allows the clinical history to be analyzed by the algorithm. It includes diagnostic events, external consultations and laboratory results. After a review of the literature, the clinical parameters that are of interest when predicting heart disease in diabetic patients are exposed. An adaptation of the Smith-Waterman algorithm is done to take them into account and to find local similarities between the histories of two patients, so if the clinical pathway of a new patient closely resembles to the one from a patient who developed heart disease, the new patient is labelled as in risk of developing it too. The importance of each of the parameters with different experiments is studied, as well as the most appropriate method of risk imputation. To do this, the database is divided into validation and testing and recall, precision and specificity are given. Results: A list of useful parameters when predicting cardiopathies in diabetics is obtained, such as the time between events, the diagnosis of cardiology and the coincidence of the ICD-9 code. Also, a method of imputation that achieves the algorithm predicts the appearance of the disease with a precision, a specificity and a recall of 0.8, giving the patient the clinical condition that predominates among the 5 individuals to which it most resembles.Carrasco Ribelles, LA. (2018). Desarrollo de un algoritmo de asignación de riesgo de desarrollo de cardiopatías en pacientes diabéticos en base a su ruta clínica. http://hdl.handle.net/10251/106554TFG

Combining Virtual Reality and Machine Learning for Leadership Styles Recognition

[EN] The aim of this study was to evaluate the viability of a new selection procedure based on machine learning (ML) and virtual reality (VR). Specifically, decision-making behaviours and eye-gaze patterns were used to classify individuals based on their leadership styles while immersed in virtual environments that represented social workplace situations. The virtual environments were designed using an evidence-centred design approach. Interaction and gaze patterns were recorded in 83 subjects, who were classified as having either high or low leadership style, which was assessed using the Multifactor leadership questionnaire. A ML model that combined behaviour outputs and eye-gaze patterns was developed to predict subjects¿ leadership styles (high vs low). The results indicated that the different styles could be differentiated by eye-gaze patterns and behaviours carried out during immersive VR. Eye-tracking measures contributed more significantly to this ifferentiation than behavioural metrics. Although the results should be taken with caution as the small sample does not allow eneralization of the data, this study illustrates the potential for a future research roadmap that combines VR, implicit measures, and ML for personnel selection.This work was co-founded by the European Union through the Operational Program of the European Regional Development Fund (FEDER) of the Valencian Community 2014-2020 (IDIFEDER/2018/029). This work is part of the "Rebrand" project with reference PROMETEU/2019/105 funded by the Generalitat Valenciana.Parra Vargas, E.; García-Delgado, A.; Carrasco-Ribelles, LA.; Chicchi Giglioli, IA.; Marín-Morales, J.; Giglio, C.; Alcañiz Raya, ML. (2022). Combining Virtual Reality and Machine Learning for Leadership Styles Recognition. Frontiers in Psychology. 13:1-15. https://doi.org/10.3389/fpsyg.2022.8642661151