[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118895-en":3,"doc-seo-118895-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118895,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Approaches for Fine-Grained Symptom Estimation in Schizophrenia - A Comprehensive Review","Schizophrenia is a severe but treatable mental disorder, yet diagnosis and assessment depend on complex primary and secondary symptoms whose severity varies across individuals. While clinical standards and symptom-scoring frameworks exist, assessment can be time-consuming and subjective, motivating automated and more consistent approaches. This survey reviews machine learning methods for fine-grained symptom estimation in schizophrenia, moving beyond binary patient-vs-control framing. It covers multimodal inputs including medical imaging (MRI), EEG, and audio-visual signals, and analyzes study methodologies, highlighting trends, gaps, and opportunities for future research.","Machine Learning Approaches for Fine-Grained Symptom Estimation in Schizophrenia: A Comprehensive Review  \nNiki Maria Foteinopoulou and Ioannis Patras  \nSchool of Electronic Engineering and Computer Science  \nQueen Mary University of London  \nLondon, United Kingdom  \n{n.m.foteinopoulou, [i.patras](i.patras}@qmul.ac.uk)[}](i.patras}@qmul.ac.uk)[@qmul.ac.uk](i.patras}@qmul.ac.uk)  \narXiv :2310 . 16677v1 [ cs .HC] 25 Oct 2023  \nAbstract—Schizophrenia is a severe yet treatable mental disorder, whose definition has evolved significantly since its inception in the early 20th century. Initially conceived as a broad term encompassing various serious mental health conditions, it is being 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, while diagnostic and assessment standards exist, the process can be both timeconsuming and subjective; hence, there is a compelling motivation to explore automated methods that can offer consistent diagnosis and precise symptom assessments, thereby complementing the work of healthcare practitioners. Machine Learning, a dominant paradigm in Artificial Intelligence, 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 paper aims to review methodologies that utilise Machine Learning for diagnosis and assessment of schizophrenia. Contrary to previous reviews that primarily focused on binary classifications distinguishing patients from healthy control groups, this work recognises that schizophrenia is a complex condition with manifestations that extend beyond a simple binary categorisation and instead, offers an overview of Machine Learning methods designed for fine-grained estimation of schizophrenia symptoms. We cover multiple modalities, namely Medical Imaging in the form of Magnetic Resonance Imaging, Electroencephalogramsand Audio-Visual input, as the illness symptoms can manifest themselves both in a patient’s pathology and behaviour. Finally, we analyse the machine learning methodologies used in the studies included in the survey and identify trends and gaps in the literature and opportunities for future research.  \nIndex Terms—Fine-grained labels, Schizophrenia, Mental Health, Machine Learning  \nI. INTRODUCTION  \nSchizophrenia is a mental disorder with debilitating effects [1], [2]; the term schizophrenia, first appeared by Eugen Bleuler in 1908, in an attempt to redefine what until that point was thought to be premature dementia [3] . At the time, the condition was thought to be a separation in personality, thinking, and general cognitive function, as described by the components of the term which translate from ancient Greek toto split and mind. Historically, there has been a great misunderstanding of the condition by both the general population and  \nearly psychiatrists, often used as a blanket diagnosis for very serious mental illnesses. As research progressed, the understanding of the illness has been improved and the definition has been narrowed down. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSMV) [4], for a diagnosis of schizophrenia the patient needs to demonstrate at least two symptoms of the primary categories after at least one episode of psychosis. More specifically, one of the symptoms needs to be hallucinations, delusions or disorganised speech and a second symptom can be oneof the negative symptoms [5] (eg. Blunted Affect) . However, post-diagnosis, and similarly to most mental illnesses, several secondary symptoms are associated with the disease which makes each diagnosis unique and the illness diverse overall. The complete spectrum of primary ","cbCaic7Ylcspc6xO","https://ap.wps.com/l/cbCaic7Ylcspc6xO","pdf",1174313,1,19,"English","en",105,"# Introduction\n## Schizophrenia: definition, diagnosis, and symptom spectrum\n## Motivation for automated assessment\n# Machine learning survey scope\n## Fine-grained vs binary classification approaches\n## Multimodal data for symptom estimation\n# Analysis and literature insights\n## Trends, gaps, and future research opportunities","[{\"question\":\"Why is fine-grained symptom estimation important for schizophrenia?\",\"answer\":\"Because schizophrenia symptoms span both primary and secondary categories with varying intensities, which shape each individual’s diagnosis and treatment course. Fine-grained estimation supports more personalized and accurate assessment.\"},{\"question\":\"What limitations exist in current clinical symptom assessment?\",\"answer\":\"Clinical interviews rely on practitioner observation, standardized frameworks, post-interview assessments, self-reports, and family input. Direct observation and quantification of verbal and non-verbal cues can be labor-intensive and subjective, making the process slower and less consistent.\"},{\"question\":\"Which modalities does the survey cover for machine learning–based symptom estimation?\",\"answer\":\"The survey includes multiple modalities: medical imaging via MRI, electroencephalograms (EEG), and audio-visual input, reflecting that symptoms can appear in both pathology and behavior.\"}]","Machine Learning Approaches for Fine-Grained Symptom Estimation in Schizophrenia - A Comprehensive Review | PDF",1785720828,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-approaches-for-fine-grained-symptom-estimation-in-schizophrenia-a-comprehensive-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-for-fine-grained-symptom-estimation-in-schizophrenia-a-comprehensive-review/118895/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is fine-grained symptom estimation important for schizophrenia?","Question",{"text":75,"@type":76},"Because schizophrenia symptoms span both primary and secondary categories with varying intensities, which shape each individual’s diagnosis and treatment course. Fine-grained estimation supports more personalized and accurate assessment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations exist in current clinical symptom assessment?",{"text":80,"@type":76},"Clinical interviews rely on practitioner observation, standardized frameworks, post-interview assessments, self-reports, and family input. Direct observation and quantification of verbal and non-verbal cues can be labor-intensive and subjective, making the process slower and less consistent.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modalities does the survey cover for machine learning–based symptom estimation?",{"text":84,"@type":76},"The survey includes multiple modalities: medical imaging via MRI, electroencephalograms (EEG), and audio-visual input, reflecting that symptoms can appear in both pathology and behavior.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]