Titolo: A simple preprocessing method enhances machine learning application to EEG data for differential diagnosis of autism (2022)
Autore: Enzo Grossi, Rebecca White, Ronald Swatzyna
Info: Available as Preprint at Researchgate
A new pre-processing approach to EEG data to detect topological EEG features has been applied to a continuous segment of artifact-free EEG data lasting 10 minutes in ASCII format derived from 50 ASD children and 50 children with other Neuro-Psychiatric Disorders (NPD), matched for age and male/female ratios. Each EEG is transformed into a triangular matrix of 171 values expressing all reciprocal Manhattan distances among the 19 electrodes of the international 10-20 system. From this matrix, the minimum spanning tree (MST) is calculated. Electrode identification serial codes are sorted according to the decreasing number of links in MST, and the number of links in MST is taken as input vectors for machine learning systems. Machine learning systems have been applied to build up a predictive model to distinguish between the two diagnostic classes (autism vs NPD) following a rigorous validation protocol. The best machine learning system (KNN algorithm) obtained a global accuracy of 93.2% (92.37 % sensitivity and 94.03 % specificity) in differentiating ASD subjects from NPD subjects. The results obtained in this study suggest that thanks to the new pre-processing method introduced, there is the possibility to discriminate subjects with autism from subjects affected by other psychiatric disorders with a modest computational time reducing the information to 38 figures.
Titolo: Writing your life story makes you happy: a pilot study on adolescents with psychiatric disorders (2021)
Autore: Federico Troboldi, Giulio Valagussa, Matteo Ghezzi, Enzo Grossi
Info: Available as Preprint at Researchgate
Background
The construction of a life story work within an educational project is an emerging theme in the care of adolescents with psychiatric disorders. The aim of this pilot study is to evaluate whether the production of a life story can improve mood and self-esteem in adolescents with psychopathology.
Methods
Eight subjects aged between 12-17 years presenting various psychiatric disorders and able to write independently, were summoned individually to produce an autobiographical paper in the presence of a teacher and a psychologist. The Profile Of Mood States - Adolescent (POMS-A) and the Rosenberg Self-Esteem (RSE) questionnaires were administered to the subjects before (T0) and immediately after (T1) the writing process. The subjects wrote the paper without limits of time and content. Finally, the subjects were interviewed by the psychologist and the teacher about their perception of the experiment and their mood state. The changes between POMS-A and RSE scores at T1 and at T0 were assessed.
Results
Overall, we found a statistically significant difference between T0 and T1 for anger, confusion, and depression POMS-A sub-scores. Five out of eight subjects had RSE scores under the normal range at T0. At T1, two of these five subjects with a self-esteem score under the normal range score reached a normal range score.
Conclusion
This pilot study suggests that writing a life story conveys an improvement of mood state and self-esteem in adolescents affected by psychiatric disorders.
Notes: Villa Santa Maria Foundation, Tavernerio (Como)
Titolo: Subjective well-being in urban and rural Italy: comparing two survey waves (2021)
Autore: Federica Viganò1, Enzo Grossi2, Giorgio Tavano Blessi3
Info: Available as Preprint at Researchgate
Titolo: COVID-19 When a cluster is a cluster (2020)
Autore: Enzo Grossi
Info: Available as Preprint at Researchgate
The effectiveness of surveillance systems in differentiating outbreak from sporadic cases could have important implications for analytical studies that assume independence among cases. The identification of the spatial clustering should be the first step when developing effective policies to manage and control any new epidemic. The big question is why scientific community is ignoring the fundamental value of cases exact location.
Titolo: The Single Individual and Precision Medicine: how A.I. Can Help to Intercept a Moving and Vague Target (2017)
Autore: Enzo Grossi*,**, Giulia Massini**, Massimo Buscema**,***
Info: Available as Work in progress at Researchgate https://www.researchgate.net/profile/Enzo_Grossi/publications?pubType=workingPaper
The translation of precision medicine in clinical practice will depend mostly from the possibility to make statistical inference at individual level, exactly positioning a new case in the taxonomy space (diagnosis) or in the time space (prognosis). As president Barak Obama said in 2015 in occasion of the launch of Precision Medicine Initiative “Most medical treatments have been designed for the “average patient”. We know otherwise that every patient is a person with unique characteristics. An intervention may be effective for a population but not necessarily for that individual patient. The recommendation of a guideline may not be right for a particular patient because it is not what he or she wants, and implementing the recommendation will not necessarily mean a favorable outcome. As matter of the fact clinical epidemiology and medical statistics have not been suited to answer specific questions at the individual level. They focus on groups of individuals and not on single individuals. Classical statistics by definition needs samples to work, and samples by definition are always greater than one. This explains why for traditional statistics the single individual is a sort of moving and vague target to intercept. Statistical predictive models can fail dramatically when applied to the single individual. In a model that has an overall 90% accuracy in predicting an event on a group level, the degree of confidence can drop substantially when applied to a single subject.One of the major challenges to delivering effective treatment is to devise a method capable of determining the confidence interval of a single individual. This is now possible by feeding the patient data into machine learning systems which are able to map the patient as an “hyperpoint” in a multidimensional space where the patient variables are the coordinates and to measure his “distance” or more simply his similarity with other patients of whom the target of interest ( diagnosis, prognosis or response to a certain treatment) is already known. The neigh borough statistics of this new patient therefore could allow us to calculate the confidence interval pertaining to him, and therefore to make inference at individual level. In this paper we describe how Machine learning systems open new avenues to this formidable task allowing to approach in a consistent and sound way the problem of single individual statistics. As exemplification we propose four complementary examples and approaches using unsupervised (first 3 examples) and supervised (fourth example) machine learning systems.
Titolo: Machine Learning Systems and Precision Medicine: a Conceptual and Experimental Approach to Single Individual Statistics (2017)
Autore: Enzo Grossi*,**, Giulia Massini**, Massimo Buscema**,***
Info: Available as Work in progress at Researchgate https://www.researchgate.net/profile/Enzo_Grossi/publications?pubType=workingPaper
This paper, entitled "Machine Learning Systems and Precision Medicine: a Conceptual and Experimental Approach to Single Individual Statistics" is an original study on the role of machine learning system in allowing to approach in a consistent and sound way the problem of single individual statistics, a fundamental scope of Precision Medicine Initiative. In the last years there has been an explosion of papers published on precision medicine topic, but very few of them, if any have answered to this very simple question: how it is possible to translate with high accuracy the group statistics at an individual level in term of confidence interval. The experience gained with the use of special machine learning systems developed at Semeion research Centre has allowed us to describe three cases studies relevant to precision medicine approach with different unsupervised machine learning systems. The three methods proved to be reliable and easily applicable to real world examples in term of readability, accuracy and reproducibility and seem to have the potential to allow the real translation of precision medicine philosophy in the real world. In a future paper we will cover the topic with supervised machine learning system, through the Fermi mathematics
Titolo: Prevalence and Correlates for Alcohol, Cigarette, and Cannabis Use among Italian High School Students: A National Survey (2016)
Autore: Bruno Genetti, Milena Sperotto, Alessandra Andreotti, Monica Zermiani, Paolo Vian, Enzo Grossi, Giovanni Serpelloni
Info: Available as Work in progress at Researchgate
Aims: This study examined the prevalence of tobacco, alcohol, and cannabis use and their correlates among Italian High School students.
Methods: A nationwide online epidemiological survey based on the European School Survey Project on Alcohol and Other Drugs was administered to Italian high school students in 2012 using two-stage probabilistic cluster sampling. There were 35,980 student respondents with analyzable data drawn from 490 schools. The mean age of respondents was 17.0 (SD=1.4) years and 50.0% were female. The association between past-30-day cigarette, alcohol, and drug use and 11 correlates identified in the literature as potentially important in understanding such use was examined in three separate logistic regression analyses. A critical value of 363.4 was determined for the Wald χ2 test that allowed detection of a small effect size.
Results: Lifetime cigarette, alcohol, and cannabis use was reported by 22,259 (61.9%), 30,754 (85.5%), and 8.142 (22.6%) of the sample, respectively. Past-30-day cigarette smoking was
significantly associated with going out a lot at night (AOR=2.2), more than one day skipping school in the past 30 days (AOR=1.7), lifetime alcohol intoxication (AOR=2.9), lifetime use of at least one drug (AOR=8.4), and friends/siblings who use drugs (AOR=1.8). Past-30-day alcohol use was significantly associated with going out a lot at night (AOR=2.8), lifetime cigarette use (AOR=2.4), use of at least one drug lifetime (AOR=2.3), friends and siblings who use drugs (AOR=1.9), and male gender (AOR=1.6). Significant correlates of past-30-day cannabis use were lifetime cigarette use (AOR=13.7), lifetime alcohol intoxication (AOR=4.0), and having friends/siblings who use illicit drugs (AOR=21.4).
Conclusions: Tobacco, alcohol, and cannabis use is prevalent among Italian high school students. Pediatricians should screen patients for their use. Additional research is needed to find effective approaches to delay and reduce substance use among youth that addresses the significant correlates of use.
