Schizophrenia is a severe yet treatable mental disorder, it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms, therefore there is a need for accurate, personalised assessments. However, the process can be both time-consuming and subjective; hence, there is a motivation to explore automated methods that can offer consistent diagnosis and precise symptom assessments, thereby complementing the work of healthcare practitioners. Machine Learning has demonstrated impressive capabilities across numerous domains, including medicine; the use of Machine Learning in patient assessment holds great promise for healthcare professionals and patients alike, as it can lead to more consistent and accurate symptom estimation.This survey aims to review methodologies that utilise Machine Learning for diagnosis and assessment of schizophrenia. Contrary to previous reviews that primarily focused on binary classification, this work recognises the complexity of the condition and instead, offers an overview of Machine Learning methods designed for fine-grained symptom estimation. We cover multiple modalities, namely Medical Imaging, Electroencephalograms and Audio-Visual, as the illness symptoms can manifest themselves both in a patient's pathology and behaviour. Finally, we analyse the datasets and methodologies used in the studies and identify trends, gaps as well as opportunities for future research.

翻译：精神分裂症是一种严重但可治疗的精神疾病，其诊断依赖于多种主要和次要症状。由于每位患者的症状严重程度不同，诊疗方案需要个性化精准评估。然而，现有评估流程既耗时又具有主观性，因此亟需探索自动化方法以实现一致的诊断和精确的症状评估，从而辅助医疗专业人员的工作。机器学习已在包括医学在内的众多领域展现出卓越能力，其在患者评估中的应用有望为医疗从业者和患者带来更一致、更准确的症状估计。本综述旨在系统梳理利用机器学习进行精神分裂症诊断与评估的方法体系。区别于以往聚焦二分类问题的研究，本研究认识到该疾病的复杂性，转而全面概述面向细粒度症状估计的机器学习方法。考虑到精神分裂症症状既可体现于患者的病理特征也可表现在行为模式中，本文涵盖医学影像、脑电图和视听数据等多模态信息。最后，我们分析了相关研究采用的数据集与方法论，识别当前研究趋势、方法空白及未来研究机遇。