Alcuni casi di risultati fuorvianti nell'applicazione del Machine Learning in Medicina

Ormai da anni si assiste alla progressiva introduzione ed adozione dell’Intelligenza Artificiale in ogni settore lavorativo, economico e nella vita di ciascun individuo.Anche le tecniche di Machine Learning hanno fatto progressi sostanziali in molti settori d’industria, come ad esempio in Medicina dove è addirittura considerata la grande speranza del 21esimo secolo per migliorare le prospettive di vita dell’umanità. In questo campo ha già portato grandi trasformazioni nel sistema sanitario e si presume che porterà ulteriori progressi nei prossimi anni: fornirà ai medici un supporto sempre più sicuro ed efficiente nel raccogliere, organizzare, analizzare i dati clinici, fare diagnosi precoci e trovare migliori soluzioni per i pazienti. Sebbene l’introduzione dell’utilizzo di questi algoritmi abbia portato miglioramenti e benefici in Medicina, non esiste ancora la certezza che un loro utilizzo massivo e generalizzato garantisca un incremento dell’efficacia dell’attività clinica. Infatti il risultato delle predizioni degli algoritmi può talvolta essere fuorviante e inatteso, portando il medico a decisioni sbagliate

Classification trees outperform logistic regression predictions of attrition in the U.S. Marine Corps

The present study compared the performance of machine learning classification models against logistic regression in the context of predicting training attrition from the Delayed Enlistment Program in the United States Marine Corps (UMSC) with scores from the Tailored Adaptive Personality Assessment System (TAPAS). The base-rate of attrition was low which made the model training process difficult, but the random-forest model outperformed logistic regression in predicting cases of attrition in a stratified 50% attrition sample

REFORMS: Reporting Standards for Machine Learning Based Science

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science. ML methods are often applied and fail in similar ways across disciplines. Motivated by this observation, our goal is to provide clear reporting standards for ML-based science. Drawing from an extensive review of past literature, we present the REFORMS checklist ($\textbf{Re}$porting Standards $\textbf{For}$ $\textbf{M}$achine Learning Based $\textbf{S}$cience). It consists of 32 questions and a paired set of guidelines. REFORMS was developed based on a consensus of 19 researchers across computer science, data science, mathematics, social sciences, and biomedical sciences. REFORMS can serve as a resource for researchers when designing and implementing a study, for referees when reviewing papers, and for journals when enforcing standards for transparency and reproducibility

Preventive Treatments for Psychosis: Umbrella Review (Just the Evidence)

Background: Indicated primary prevention in young people at Clinical High Risk for Psychosis (CHR-P) is a promising avenue for improving outcomes of one of the most severe mental disorders but their effectiveness has recently been questioned. Methods: Umbrella review. A multi-step independent literature search of Web of Science until January 11, 2019, identified interventional meta-analyses in CHR-P individuals. The individual randomised controlled trials that were analysed by the meta-analyses were extracted. A review of ongoing trials and a simulation of living meta-analysis complemented the analysis. Results: Seven meta-analyses investigating preventive treatments in CHR-P individuals were included. None of them produced pooled effect sizes across psychological, pharmacological, or other types of interventions. The outcomes analysed encompassed risk of psychosis onset, the acceptability of treatments, the severity of attenuated positive/negative psychotic symptoms, depression, symptom-related distress, social functioning, general functioning, and quality of life. These meta-analyses were based on 20 randomised controlled trials: the vast majority defined the prevention of psychosis onset as their primary outcome of interest and only powered to large effect sizes. There was no evidence to favour any preventive intervention over any other (or control condition) for improving any of these clinical outcomes. Caution is required when making clinical recommendations for the prevention of psychosis in individuals at risk. Discussion: Prevention of psychosis from a CHR-P state has been, and should remain, the primary outcome of interventional research, refined and complemented by other clinically meaningful outcomes. Stagnation of knowledge should promote innovative and collaborative research efforts, in line with the progressive and incremental nature of medical knowledge. Advancements will most likely be associated with the development of new experimental therapeutics that are ongoing along with the ability to deconstruct the high heterogeneity within CHR-P populations. This would require the estimation of treatment-specific effect sizes through living individual participant data meta-analyses, controlling risk enrichment during recruitment, statistical power, and embedding precision medicine within youth mental health services that can accommodate sequential prognosis and advanced trial designs. Conclusions: The evidence-based challenges and proposed solutions addressed by this umbrella review can inform the next generation of research into preventive treatments for psychosis.ope

Does mental toughness moderate the relationship between pain intensity and working memory?

The purpose of this study was to investigate whether mental toughness can moderate the relationship between pain and attention. Two studies were conducted with the second addressing methodological issues encountered in the first. The studies consisted of a within-subjects design and involved the completion of a 2-back task in a ‘Pain’ condition and a ‘No Pain’ condition. The pain manipulation was a cold pressor machine circulating water at 12oC ± 1oC, which participants held their hand in for 2 minutes whilst completing the 2-back task in the ‘Pain’ condition. Independent variables were mental toughness and pain intensity ratings, and the dependent variable were 2-back performance scores in each condition. Results did not support the hypotheses: performance on the 2-back task was not worse in the ‘Pain’ condition compared with the ‘No Pain’ condition; performance on the 2-back task did not decline as pain increased and mental toughness did not moderate the relationship between pain and attention (performance on the 2-back task). Potential reasons for the lack of supportive findings are discussed

Missed appointments in healthcare systems:A national retrospective data linkage project

Healthcare systems across the world generate large volumes of data about patients including information about their age, sex, and medical history. It also captures information on how patients interact across multiple points of care (e.g., hospitals, dentists and general practice). Advances in data availability and computational power now means that much of this data can be leveraged for social good. This ranges from the use of behavioural analytics to better predict service demand through to understanding the impact of behaviour change interventions. In this project, we used patient data to explore the causes of low engagement in healthcare and the impact this has on patients and services. This also involved linking data sets from different organisations (e.g., health, death and education). We observed that serially missing general practice (GP) appointments provided a risk marker for vulnerability and poorer health outcomes. While the project was administratively and methodologically challenging, the interdisciplinary background of the team ensured that the project was ultimately successful. This was particularly important when navigating a variety of different systems used to manage and distribute sensitive patient data. Our results have already started to inform debates concerning how best to reduce non-attendance and increase patient engagement within healthcare systems. Following a series of high-profile publications and associated impact events, non-academic beneficiaries have included governments, policymakers and medical practitioners