Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data

Mar, Javier; Gorostiza, Ania; Arrospide, Arantzazu; Larrañaga, Igor; Alberdi, Ane; Cernuda, Carlos; Iruín, Álvaro; Tainta, Mikel; Mar-Barrutia, Lorea; Ibarrondo, Oliver

doi:10.1016/j.rpsm.2021.03.001

Cited by 11 publications

(15 citation statements)

References 28 publications

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“…Different outcomes were considered by different studies while incorporating ML or AI techniques. Major neurocognitive disorder, dementia, and Alzheimer's disease were the most common conditions among the articles included in our study and were reported in 11 studies ( 7 , 15 , 16 , 24 , 25 , 27 , 28 , 30 , 31 , 33 , 34 ). Among the other outcomes, a geriatric syndrome (falls, malnutrition, dementia, severe urinary control issues, absence of fecal control, visual impairment, walking difficulty, pressure ulcers, lack of social support, and weight loss) in 3 studies ( 18 , 21 , 22 ), delirium in 2 studies ( 20 , 32 ), mild cognitive impairment ( 33 ), cognitive disorder ( 26 ), multimorbidity pattern ( 23 ), mortality ( 29 ), hospital admission ( 17 ), and themes/topics mentioned in care providers' notes ( 19 ) were considered once.…”

Section: Resultsmentioning

confidence: 99%

“…There were considerable variations among the specific ML or AI methods used by different studies. Random forest was the most common ML technique and was used by seven studies ( 7 , 20 , 28 , 30 – 32 , 34 ). Both natural language processing (NLP) ( 18 , 21 , 25 , 29 ) and logistic regression modeling techniques ( 16 , 24 , 28 , 31 ) were used by four studies.…”

Section: Resultsmentioning

confidence: 99%

“…ROB was estimated as high in terms of both outcome and analysis in one study when PROBAST was applied. Only in three studies (16,31,34) low ROB was observed in all of the PROBAST domains.…”

Section: Study Quality Assessmentmentioning

confidence: 86%

“…After removing duplicates, 363 titles and abstracts were screened for eligibility, and from there, 68 articles were selected for fulltext screening. After assessing full-texts, 21 articles were finally selected for the systematic review (7,(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34). The detailed study selection process is summarized in Figure 1.…”

Section: Study Identification and Selectionmentioning

confidence: 99%

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Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

et al. 2021

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Introduction: Electronic health records (EHR) and administrative healthcare data (AHD) are frequently used in geriatric mental health research to answer various health research questions. However, there is an increasing amount and complexity of data available that may lend itself to alternative analytic approaches using machine learning (ML) or artificial intelligence (AI) methods. We performed a systematic review of the current application of ML or AI approaches to the analysis of EHR and AHD in geriatric mental health.Methods: We searched MEDLINE, Embase, and PsycINFO to identify potential studies. We included all articles that used ML or AI methods on topics related to geriatric mental health utilizing EHR or AHD data. We assessed study quality either by Prediction model Risk OF Bias ASsessment Tool (PROBAST) or Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist.Results: We initially identified 391 articles through an electronic database and reference search, and 21 articles met inclusion criteria. Among the selected studies, EHR was the most used data type, and the datasets were mainly structured. A variety of ML and AI methods were used, with prediction or classification being the main application of ML or AI with the random forest as the most common ML technique. Dementia was the most common mental health condition observed. The relative advantages of ML or AI techniques compared to biostatistical methods were generally not assessed. Only in three studies, low risk of bias (ROB) was observed according to all the PROBAST domains but in none according to QUADAS-2 domains. The quality of study reporting could be further improved.Conclusion: There are currently relatively few studies using ML and AI in geriatric mental health research using EHR and AHD methods, although this field is expanding. Aside from dementia, there are few studies of other geriatric mental health conditions. The lack of consistent information in the selected studies precludes precise comparisons between them. Improving the quality of reporting of ML and AI work in the future would help improve research in the field. Other courses of improvement include using common data models to collect/organize data, and common datasets for ML model validation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

“…ROB was estimated as high in terms of both outcome and analysis in one study when PROBAST was applied. Only in three studies (16,31,34) low ROB was observed in all of the PROBAST domains.…”

Section: Study Quality Assessmentmentioning

confidence: 86%

Section: Study Identification and Selectionmentioning

confidence: 99%

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Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

et al. 2021

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“…Giang, Nguyen, and Tran [ 115 ] proposed a fast-multiple kernel learning framework, referred to as fMKL-DR, to optimize equations to calculate matrix chain multiplication and reduce dimensions in data space to stratify AD patients into different phases. Mar et al [ 116 ] validated random forest models by using them to identify depressive and psychotic clusters according to their presence in the EHRs of all patients diagnosed with dementia.…”

Section: Tasks In Ad Researchmentioning

confidence: 99%

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Jiang

Wang

et al. 2021

Emerging Topics in Life Sciences

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Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

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Incidence of mental disorders in the general population aged 1–30 years disaggregated by gender and socioeconomic status

Mar

Larrañaga

Ibarrondo

et al. 2023

Soc Psychiatry Psychiatr Epidemiol

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Purpose The objective of this study was to estimate the incidence and age of onset of mental disorders diagnosed by gender and socioeconomic status (SES) in children, adolescents, and young adults up to 30 years of age in the whole population of the Basque Country (Spain). Methods All mental health diagnoses documented in Basque Health Service records from 1 January 2003 to 31 December 2018, were classified into eight clusters: anxiety, attention deficit hyperactivity disorder (ADHD), conduct disorders, depression, psychosis/personality disorders, substance use, eating disorders, and self-harm. We calculated incidence and cumulative incidence for each cluster, disaggregated by gender, and socioeconomic status (SES). Poisson regression analyses were performed. Results Overall, 9,486,853 person-years of observation were available for the 609,281 individuals included. ADHD and conduct disorders were diagnosed in the first decade, anxiety and depression disorders in the second and third decades, and psychosis/personality and substance use in the third. The cumulative incidence at 18 years of age for any type of disorder was 15.5%. The group with low SES had a statistically significantly higher incidence of all eight clusters. The incidence of ADHD, conduct disorders, depression, psychosis/personality disorders, and substance use was higher in males and that of anxiety, eating disorders and self-harm was higher in females. Conclusions The incidence of mental disorders is high among children, adolescents, and young adults in the Basque Country underlining the need for preventive interventions. Marked differences by gender and SES highlight mental health inequalities, especially for depression and psychosis in low SES males.

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Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data

Cited by 11 publications

References 28 publications

Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

Use of Machine Learning and Artificial Intelligence Methods in Geriatric Mental Health Research Involving Electronic Health Record or Administrative Claims Data: A Systematic Review

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Incidence of mental disorders in the general population aged 1–30 years disaggregated by gender and socioeconomic status

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