A classification and review of tools for developing and interacting with machine learning systems

[Abstract] In this paper we aim to bring some order to the myriad of tools that have emerged in the field of Artificial Intelligence (AI), focusing on the field of Machine Learning (ML). For this purpose, we suggest a classification of the tools in which the categories are organized following the development lifecycle of an ML system and we make a review of the existing tools within each section of the classification. We believe this will help to better understand the ecosystem of tools currently available and will also allow us to identify niches in which to develop new tools to aid in the development of AI and ML systems. After reviewing the state-of-the-art of the tools, we have identified three trends in them: the incorporation of humans into the loop of the machine learning process, the movement from ad-hoc and experimental approaches to a more engineering perspective and the ability to make it easier to develop intelligent systems for people without an educational background in the area, in order to move the focus from the technical environment to the domain-specific problem.This work has been supported by the State Research Agency of the Spanish Government, grant (PID2019-107194GB-I00 / AEI / 10.13039/501100011033) and by the Xunta de Galicia, grant (ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund-Galicia 2014-2020 Program), by grant ED431G 2019/01Xunta de Galicia; ED431C 2018/34Xunta de Galicia; ED431G 2019/0

Improving Medical Data Annotation Including Humans in the Machine Learning Loop

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021[Abstract] At present, the great majority of Artificial Intelligence (AI) systems require the participation of humans in their development, tuning, and maintenance. Particularly, Machine Learning (ML) systems could greatly benefit from their expertise or knowledge. Thus, there is an increasing interest around how humans interact with those systems to obtain the best performance for both the AI system and the humans involved. Several approaches have been studied and proposed in the literature that can be gathered under the umbrella term of Human-in-the-Loop Machine Learning. The application of those techniques to the health informatics environment could provide a great value on prognosis and diagnosis tasks contributing to develop a better health service for Cancer related diseases.This work has been supported by the State Research Agency of the Spanish Government, grant (PID2019-107194GB-I00/AEI/10.13039/501100011033) and by the Xunta de Galicia, grant (ED431C 2018/34) with the European Union ERDF funds. We wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by grant ED431G 2019/01Xunta de Galicia; ED431C 2018/34Xunta de Galicia; ED431G 2019/0

Lifestyle changes and mental health during the COVID-19 pandemic: A repeated, cross-sectional web survey

This study aimed to compare self-reported changes on lifestyle behaviors during two phases of the COVID-19 pandemic in Spain, and to evaluate clinical and sociodemographic factors associated with lifestyles. Methods: Two cross-sectional web surveys were conducted during lockdown (April 15-May 15, 2020) and seven months later (November 16-December 16, 2020). Lifestyle behaviors were self-reported by a multidimensional scale (SMILE-C). Two separate samples of respondents were analyzed. A multivariate regression model was performed to evaluate the association of SMILE-C scores with demographic and clinical variables. Results: The sample comprised, 3412 participants from the first survey (S1) and in the S1 and 3635 from the second (S2). SMILE-C score decreased across surveys (p < 0.001). The rates of positive screenings for depression and anxiety were similar between the surveys, whereas those for alcohol abuse decreased (p < 0.001). Most participants in S2 reported that their lifestyle had not changed compared to those before the pandemic. Variables independently associated with an unhealthier lifestyle were working as an essential worker, lower educational level, previous mental disease, worse self-rated health, totally/moderate changes on diet, sleep or social support, as well as positive screenings for alcohol abuse, anxiety and depression. Limitations: The cross-sectional design and recruitment by non-probabilistic methods limit inferring causality and the external validity of the results.Instituto Carlos III, PI16/01770Ministerio de Ciencia e Innovación, PI15/00283, PI18/0080

Gender differences in addiction severity

Gender has been associated with substance use disorders (SUD). However, there are few studies that have evaluated gender differences in a global and a standardized way, and with a large sample of patients with SUD. Our goal is to analyze the role of gender in addiction severity throughout multiple life domains, using the Addiction Severity Index-6 (ASI-6). A naturalistic, multicenter and prospective study was conducted. A total of 221 patients with SUD (80.1% men) were interviewed with the ASI-6. Our results indicate that the Recent Summary Scores (RSSs) of men and women are similar, with the exception of Psychiatric and Partner- Problems, where women showed higher severity (p = .017 and p = .013, respectively). Statistically significant gender differences were found in certain aspects of the ASI-6 domains: men have more problems of physical health, legal issues, and alcohol and other substance use; and woman score higher in problems of mental health, social network, subjective evaluations of SUD consequences, and treatment needs. These results should be taken into account to improve the identification, prevention, and treatment of SUD.Se ha descrito que el género es un factor que condiciona los trastornos por uso de sustancias (TUS). Sin embargo, hay pocos estudios que hayan evaluado esas diferencias de género de manera global, estandarizada y en una muestra amplia de pacientes con TUS. Nuestro objetivo es analizar el rol del género en la gravedad de la adicción a través de los diversos dominios de vida mediante el Addiction Severity Index-6 (ASI-6). Se llevó a cabo un estudio naturalístico, multicéntrico y prospectivo con una muestra compuesta por 221 pacientes con TUS (80,1% hombres). Los participantes fueron entrevistados con el ASI-6. Los resultados han mostrado que las Puntuaciones Sumarias Recientes (PSRs) son similares entre hombres y mujeres a excepción de las correspondientes a Salud mental y Pareja- Problemas, donde las mujeres presentan mayor gravedad (p = 0,017 y p = 0,013, respectivamente). Por otra parte, se han encontrado diferencias estadísticamente significativas e diversos aspectos concretos de las áreas contempladas por el ASI-6, que indican que los hombres presentan más problemas en cuanto a salud física, cuestiones legales y uso de alcohol y drogas, y la mujeres en salud mental, red social y la valoración subjetiva sobre las consecuencias del TUS y la necesidad de tratamiento. Estos resultados deben tenerse en cuenta a la hora de implementar una mejora en la identificación, prevención y tratamiento de los TUS

Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach

[Abstract]: Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges.This work has been supported by the State Research Agency of the Spanish Government (Grant PID2019-107194GB-I00/AEI/10.13039/501100011033) and by the Xunta de Galicia (Grant ED431C 2022/44), supported in turn by the EU European Regional Development Fund. We wish to acknowledge support received from the Centro de Investigación de Galicia CITIC, funded by the Xunta de Galicia and the European Regional Development Fund (Galicia 2014–2020 Program; Grant ED431G 2019/01).Xunta de Galicia; ED431C 2022/44Xunta de Galicia; ED431G 2019/0

Lifestyle in undergraduate students and demographically matched controls during the covid-19 pandemic in Spain

Few studies have used a multidimensional approach to describe lifestyle changes among undergraduate students during the COVID-19 pandemic or have included controls. This study aimed to evaluate lifestyle behaviors and mental health of undergraduate students and compare them with an age and sex-matched control group. A cross-sectional web survey using snowball sampling was conducted several months after the beginning of COVID-19 pandemic in Spain. A sample of 221 students was recruited. The main outcome was the total SMILE-C score. Students showed a better SMILE-C score than controls (79.8 +- 8.1 vs. 77.2 +- 8.3; p < 0.001), although these differences disappeared after controlling for covariates. While groups did not differ in the screenings of depression and alcohol abuse, students reported lower rates of anxiety (28.5% vs. 37.1%; p = 0.042). A lower number of cohabitants, poorer self-perceived health and positive screening for depression and anxiety, or for depression only were independently associated (p < 0.05) with unhealthier lifestyles in both groups. History of mental illness and financial difficulties were predictors of unhealthier lifestyles for students, whereas totally/moderate changes in substance abuse and stress management (p < 0.05) were predictors for the members of the control group. Several months after the pandemic, undergraduate students and other young adults had similar lifestyles

Tratamientos psicológicos empíricamente apoyados para adultos: Una revisión selectiva

Antecedentes: los tratamientos psicológicos han mostrado su eficacia, efectividad y eficiencia para el abordaje de los trastornos mentales; no obstante, considerando el conocimiento científico generado en los últimos años, no se dispone de trabajos de actualización en español sobre cuáles son los tratamientos psicológicos con respaldo empírico. El objetivo fue realizar una revisión selectiva de los principales tratamientos psicológicos empíricamente apoyados para el abordaje de trastornos mentales en personas adultas. Método: se recogen niveles de evidencia y grados de recomendación en función de los criterios propuestos por el Sistema Nacional de Salud de España (en las Guías de Práctica Clínica) para diferentes trastornos psicológicos. Resultados: los resultados sugieren que los tratamientos psicológicos disponen de apoyo empírico para el abordaje de un amplio elenco de trastornos psicológicos. El grado de apoyo empírico oscila de bajo a alto en función del trastorno psicológico analizado. La revisión sugiere que ciertos campos de intervención necesitan una mayor investigación. Conclusiones: a partir de esta revisión selectiva, los profesionales de la psicología podrán disponer de información rigurosa y actualizada que les permita tomar decisiones informadas a la hora de implementar aquellos procedimientos psicoterapéuticos empíricamente fundamentados en función de las características de las personas que demandan ayuda. Background: Psychological treatments have shown their efficacy, effectiveness, and efficiency in dealing with mental disorders. However, considering the scientific knowledge generated in recent years, in the Spanish context, there are no updating studies about empirically supported psychological treatments. The main goal was to carry out a selective review of the main empirically supported psychological treatments for mental disorders in adults. Method: Levels of evidence and degrees of recommendation were collected based on the criteria proposed by the Spanish National Health System (Clinical Practice Guidelines) for different psychological disorders. Results: The results indicate that psychological treatments have empirical support for the approach to a wide range of psychological disorders. These levels of empirical evidence gathered range from low to high depending on the psychological disorder analysed. The review indicates the existence of certain fields of intervention that need further investigation. Conclusions: Based on this selective review, psychology professionals will be able to have rigorous, up-to-date information that allows them to make informed decisions when implementing empirically based psychotherapeutic procedures based on the characteristics of the people who require help

Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer Treatment

Cursos e Congresos, C-155[Abstract] The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models on a diagnosis scenario with the goal to decide whether a patient should be prescribed with a chemotherapy treatment. To validate the diagnosis results we explore different explainability approaches: Decision Tree, Random Forest, and model agnostic ad-hoc models, and compare them against a standard Pancreatic Cancer treatment set of rules. The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models. To validate the diagnostic results we explore different explainability approaches: Decision Tree based approach, Random Forest based approach, and different model agnostic ad-hoc approaches, and we compare them against a standard Pancreatic Cancer treatment set of rulesXunta de Galcia; ED431G 2019/01Xunta de Galcia; ED431C 2022/44This work has been supported by the State Research Agency of the Spanish Government (grant PID2019-107194GB-I00/AEI/10.13039/501100011033) and by the Xunta de Galicia (grant ED431C2022/44), supported in turn by the EU European Regional Development Fund. We wish to acknowledge support received from the Centro de Investigaci ´on de Galicia CITIC, funded by the Xunta de Galicia and the European Regional Development Fund (Galicia 2014-2020 Program; grant ED431G 2019/01

Neurologic soft signs in borderline personality disorder

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Is the biological nature of depressive symptoms in borderline patients without concomitant Axis I pathology idiosyncratic? Sleep EEG comparison with recurrent brief, major depression and control subjects.

The relationship between borderline personality disorder (BPD) and the affective disorders is controversial, and we have previously compared BPD and major depression (MD) with endocrinological measures and sleep electroencephalography (S-EEG). We have also compared BPD, MD and recurrent brief depression (RBD) using endocrine tests. We have proposed that depressive symptoms in BPD might have a biological substrate that is distinct from those in depressive illness without comorbid BPD. BPD has been proposed to overlap with RBD, which has been found to share perturbed biological substrates with MD, but we have not found the same biological pattern in BPD. When endocrinological data in BPD, MD and RBD were compared, we did not find evidence of biological linkage between BPD and RBD. To clarify the biological nature of depressive symptoms in BPD, we examined S-EEG characteristics in BPD, RBD, MD and controls. Among 20 BPD patients, 12 were also diagnosed as having clinical RBD. BPD patients showed differences in sleep continuity and especially in sleep architecture compared with RBD, MD and controls. BPD with or without clinical RBD did not show significant differences in any parameter. BPD with or without clinical RBD had less slow sleep activity not only than MD but also than non-borderline RBD patients. We propose that although BPD patients can have concomitant MD, they often exhibit a specific BPD-associated affective syndrome that is different from both MD and non-borderline RBD in the quality and duration of symptoms and the biological substrate.Journal Articleinfo:eu-repo/semantics/publishe