ABSTRACT

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that includes symptoms such as inattentiveness, hyperactivity and impulsiveness. It is considered as an important public health issue, and prevalence of diagnosis has increased as awareness of the disease grew over the past years. Supply of specialist medical experts has not kept pace with the increasing demand for assessment, both due to financial pressures on health systems and the difficulty to train new experts, resulting in growing waiting lists. Patients are not being treated quickly enough causing problems in other areas of health systems (e.g. increased GP visits, increased risk of self-harm and accidents) and more broadly (e.g. time off work, relationship problems). Advances in machine learning make it possible to attempt to diagnose ADHD based on the analysis of relevant data, and this could inform clinical practice. This paper reports on findings related to the mental health services of a specialist Trust within the UK’s National Health Service (NHS). The analysis studied data of adult patients who underwent diagnosis over the past few years, and developed a diagnostic model for ADHD in adults. The results demonstrate that it is indeed possible to correctly diagnose ADHD patients with promising statistical accuracy.

摘要

注意缺陷多动障碍 (ADHD) 是一种神经发育障碍，包括注意力不集中、多动和冲动等症状。它被认为是一个重要的公共卫生问题，随着过去几年对该疾病的认识不断提高，诊断的患病率也有所增加。由于卫生系统的财政压力和培训新专家的困难，专科医学专家的供应跟不上不断增长的评估需求，导致等候名单不断增加。患者没有得到足够快的治疗，从而导致卫生系统其他领域的问题（例如，GP 就诊次数增加、自残和事故风险增加）和更广泛的问题（例如休假、人际关系问题）。机器学习的进步使得根据相关数据的分析尝试诊断 ADHD 成为可能，这可以为临床实践提供信息。本文报告了与英国国民健康服务 (NHS) 内的专家信托基金的心理健康服务相关的调查结果。该分析研究了过去几年接受诊断的成年患者的数据，并开发了成人多动症的诊断模型。结果表明，确实有可能以有希望的统计准确性正确诊断 ADHD 患者。并开发了成人多动症的诊断模型。结果表明，确实有可能以有希望的统计准确性正确诊断 ADHD 患者。并开发了成人多动症的诊断模型。结果表明，确实有可能以有希望的统计准确性正确诊断 ADHD 患者。