Metrics reloaded: recommendations for image analysis validation.

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers.

Understanding metric-related pitfalls in image analysis validation.

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers.

Exclusion of the non-English-speaking world from the scientific literature: Recommendations for change for addiction journals and publishers.

While English is only the native language of 7.3% of the world's population and less than 20% can speak the language, nearly 75% of all scientific publications are English. To describe how and why scientific contributions from the non-English-speaking world have been excluded from addiction literature, and put forward suggestions for making this literature more accessible to the non-English-speaking population.

Services Available at United States Addiction Treatment Facilities That Offer Medications versus Behavioral Treatment Only: A Cross-Sectional, Observational Analysis.

Purpose: Substance use disorders (SUDs) are widespread and cause significant morbidity and mortality, yet most people in the United States with a SUD do not receive treatment. Recommendations call for widespread use of pharmacotherapy, including medications for opioid use disorder (MOUD). However, many facilities do not offer a full array of medication treatments.

Ten simple rules to host an inclusive conference.

Conferences are spaces to meet and network within and across academic and technical fields, learn about new advances, and share our work. They can help define career paths and create long-lasting collaborations and opportunities. However, these opportunities are not equal for all.

Older Adult Substance Use Treatment First-Time Admissions Between 2008 and 2018.

Objectives: To see whether the percentage of older adults entering substance use treatment for their first time continued to increase and whether there were changes in the use patterns leading to the treatment episode, particularly an increase in illicit drugs.

Design: Public administrative health record study.

Setting: The Treatment Episode Data Sets publicly available from the Substance Abuse Mental Health Services Administration from 2008 to 2018.

Practical foundations of machine learning for addiction research. Part II. Workflow and use cases.

In a continuum with applied statistics, machine learning offers a wide variety of tools to explore, analyze, and understand addiction data. These tools include algorithms that can leverage useful information from data to build models; these models can solve particular tasks to answer addiction scientific questions. In this second part of a two-part review on machine learning, we explain how to apply machine learning methods to addiction research.

Practical foundations of machine learning for addiction research. Part I. Methods and techniques.

Machine learning assembles a broad set of methods and techniques to solve a wide range of problems, such as identifying individuals with substance use disorders (SUD), finding patterns in neuroimages, understanding SUD prognostic factors and their association, or determining addiction genetic underpinnings. However, the addiction research field underuses machine learning. This two-part narrative review focuses on machine learning tools and concepts, providing an introductory insight into their capabilities to facilitate their understanding and acquisition by addiction researchers.

Substance Use in Pregnancy: Identifying Stigma and Improving Care.

This review examines the impact of stigma on pregnant people who use substances. Stigma towards people who use drugs is pervasive and negatively impacts the care of substance-using people by characterizing addiction as a weakness and fostering beliefs that undermine the personal resources needed to access treatment and recover from addiction, including self-efficacy, help seeking and belief that they deserve care. Stigma acts on multiple levels by blaming people for having a problem and then making it difficult for them to get help, but in spite of this, most pregnant people who use substances reduce or stop using when they learn they are pregnant.

Learning deep features for dead and living breast cancer cell classification without staining.

Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases.

Three-month outcomes from a patient-centered program to treat opioid use disorder in Iowa, USA.

Background: Opioid use disorder (OUD), a chronic disease, is a major public health problem. Despite availability of effective treatment, too few people receive it and treatment retention is low. Understanding barriers and facilitators of treatment access and retention is needed to improve outcomes for people with OUD.

Late- and Typical-Onset Heroin Use Among Older Adults Seeking Treatment for Opioid Use Disorder.

Objective: Analyze 10-year trends in opioid use disorder with heroin (OUD-H) among older persons and to compare those with typical-onset (age <30 years) to those with late (age 30+) onset.

Design: Naturalistic observation using the most recent (2008-2017) Treatment Episode Data Set-Admissions (TEDS-A).

Setting: Admission records in TEDS-A come from all public and private U.

A latent class analysis of alcohol and posttraumatic stress symptoms among offspring of parents with and without alcohol use disorder.

The co-occurrence of posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) is widely known, yet few studies have examined whether and how AUD symptoms co-occur with PTSD symptom clusters of hypervigilance, avoidance/numbing, and re-experiencing. The purpose of this study was to examine potential overlap between AUD and posttraumatic stress symptomatology, and to characterize the resultant latent classes in terms of demographics, drinking behaviors, parental AUD, and specific traumas experienced (physical violence, sexual violence, and non-assaultive trauma). We hypothesized that classes would be differentiated by type and severity of AUD and PTS symptoms.

Mechanisms of Alcohol Addiction: Bridging Human and Animal Studies.

Aim: The purpose of this brief narrative review is to address the complexities and benefits of extending animal alcohol addiction research to the human domain, emphasizing Allostasis and Incentive Sensitization, two models that inform many pre-clinical and clinical studies.

Methods: The work reviewed includes a range of approaches, including: a) animal and human studies that target the biology of craving and compulsive consumption; b) human investigations that utilize alcohol self-administration and alcohol challenge paradigms, in some cases across 10 years; c) questionnaires that document changes in the positive and negative reinforcing effects of alcohol with increasing severity of addiction; and d) genomic structural equation modeling based on data from animal and human studies.

Results: Several general themes emerge from specific study findings.

Emergency department visits and hospital readmissions in an Argentine health system.

Background And Goal Of Study: The scope of health in the Sustainable Development Goals is much broader than the Millennium Development Goals, spanning functions such as health-system access and quality of care. Hospital readmission rate and ED-visits within 30 days from discharge are considered low-cost quality indicators. This work assesses an indicator of quality of care in a tertiary referral hospital in Argentina, using data available from clinical records.

Density and Dichotomous Family History Measures of Alcohol Use Disorder as Predictors of Behavioral and Neural Phenotypes: A Comparative Study Across Gender and Race/Ethnicity.

Background: Family history (FH) is an important risk factor for the development of alcohol use disorder (AUD). A variety of dichotomous and density measures of FH have been used to predict alcohol outcomes; yet, a systematic comparison of these FH measures is lacking. We compared 4 density and 4 commonly used dichotomous FH measures and examined variations by gender and race/ethnicity in their associations with age of onset of regular drinking, parietal P3 amplitude to visual target, and likelihood of developing AUD.

Polygenic contributions to alcohol use and alcohol use disorders across population-based and clinically ascertained samples.

Background: Studies suggest that alcohol consumption and alcohol use disorders have distinct genetic backgrounds.

Methods: We examined whether polygenic risk scores (PRS) for consumption and problem subscales of the Alcohol Use Disorders Identification Test (AUDIT-C, AUDIT-P) in the UK Biobank (UKB; N = 121 630) correlate with alcohol outcomes in four independent samples: an ascertained cohort, the Collaborative Study on the Genetics of Alcoholism (COGA; N = 6850), and population-based cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC; N = 5911), Generation Scotland (GS; N = 17 461), and an independent subset of UKB (N = 245 947). Regression models and survival analyses tested whether the PRS were associated with the alcohol-related outcomes.

Development of Alcohol Use Disorder as a Function of Age, Severity, and Comorbidity with Externalizing and Internalizing Disorders in a Young Adult Cohort.

Background: As part of the ongoing Collaborative Study of the Genetics of Alcoholism, we performed a longitudinal study of a high risk cohort of adolescents/young adults from families with a proband with an alcohol use disorder, along with a comparison group of age-matched controls. The intent was to compare the development of alcohol problems in subjects at risk with and without comorbid externalizing and internalizing psychiatric disorders.

Methods: Subjects ( = 3286) were assessed with a structured psychiatric interview at 2 year intervals over 10 years (2004-2017).