A narrative-based reasoning with applications in decision support for social service organizations

Wang, Wai Ming; Cheung, Chi Fai

doi:10.1016/j.eswa.2010.08.118

Cited by 20 publications

(7 citation statements)

References 32 publications

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“…From a psychological point of view, the increasing trend of psychological morbidity (Wang et al, 2007) certainly adds to the burden of mental health care providers to offer timely and quality services so as to maintain the health of the community (Wang & Cheung, 2011). Given that mental health problems require costly investigation before diagnosis is reached (Salmon, Dowrick, & Ring, 2004), the need of automated support tools to give faster responses along with cost-cutting is more than evident.…”

Section: Related Workmentioning

confidence: 99%

“…Stemming from this need, several recent and relevant works aim to provide CDSS in the fields of psychology and psychiatry (e.g. Baumgartner, Ferrari & Palermo, 2008;Delgado et al, 2005;Razzouk et al, 2006;Suhasini, Palanivel & Ramalingam, 2011;Toro et al, 2012;Trivedi et al, 2004;Wang & Cheung, 2011;Wang et al, 2007). In this scenario, the importance and influence of ontologies in DSS scenario could provide improve psychological diagnosis.…”

Section: Related Workmentioning

confidence: 99%

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Towards an ontology for psychological disorders

Rodríguez-González

Rodríguez

Casado‐Lumbreras

et al. 2012

IJMSO

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Psychological diagnosis is the process by which mental health professionals determine if problems that affect a person meet all specific criteria for a psychological disorder. In the last years decision support systems (DSS) have helped practitioners in the field of psychological clinical diagnosis with notable results. Given that ontologies are created, among other goals, to allow different sorts of formal reasoning, they are seen as valid artifacts to support a new generation of DSS in mental health professionals. Although in recent years some initiatives have emerged in order to build realist ontologies of mental diseases, this field presents a remarkable heterogeneity of data and two different clinical classification systems that is why authors present an ontology with the aim of reusing existing works and serving as the key element of a clinical DSS in the field of mental disorders.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Towards an ontology for psychological disorders

Rodríguez-González

Rodríguez

Casado‐Lumbreras

et al. 2012

IJMSO

View full text Add to dashboard Cite

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“…According to recent estimates (Ritchie, 2018 ; WHO, 2020 ), approximately one billion individuals suffer from mental health disorders (e.g., depression, anxiety, bipolar disorder), costing the global economy trillions of dollars in disability payments and lost productivity. However, a significant proportion of individuals with mental illnesses do not receive treatment or the quality of care they need, often due to resource shortages (Docrat et al, 2019 ; Petersen et al, 2019 ; Wainberg et al, 2017 ; Wang & Cheung, 2011 ). For example, in many countries, there is fewer than one psychiatrist for every 100,000 people (Hanna et al, 2018 ; Jenkins et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

“…For example, in many countries, there is fewer than one psychiatrist for every 100,000 people (Hanna et al, 2018 ; Jenkins et al, 2010 ). Moreover, certain tools and methods used by mental health professionals to make care-related decisions (e.g., formulating accurate diagnoses) are inadequate (Kilbourne et al, 2018 ; Wang & Cheung, 2011 ). Additionally, recent pandemics (e.g., COVID-19) and epidemics (e.g., opioid overdose) have exacerbated the mental health crisis worldwide (Johnson et al, 2021 ; Khanal et al, 2020 ; Ransing et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

An AI-based Decision Support System for Predicting Mental Health Disorders

et al. 2022

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Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants’ answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy.

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“…Incorporating narratives can help people have a more comprehensible understanding of business decision challenges through listening to others' such as clients' similar stories. KB-DSS based on narrative-reasoning can equip decision makers to better adapt to existing experience and discover more innovative ideas to solve new decision problems [13].…”

Section: Related Workmentioning

confidence: 99%