Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Park, Ji Hwan; Cho, Han Eol; Kim, Jong Hun; Wall, Melanie M.; Stern, Yaakov; Lim, Hyunsun; Yoo, Shinjae; Kim, Hyoung Seop; Cha, Jiook

doi:10.1038/s41746-020-0256-0

Cited by 106 publications

(83 citation statements)

References 39 publications

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“…In particular, by applying multiple “off‐the‐shelf” ML algorithms on EHR data, including patient demographics, comorbidities, and medication history, we assessed the effectiveness of variables extracted from longitudinal EHRs. Recent work 17 also used ML to predict AD with large‐scale administrative claims data. While our study only used EHR data, we observed comparable performance at the 0‐year prediction task, and significantly improved performance at 1‐year, 2‐year, and 3‐year prediction tasks.…”

Section: Discussionmentioning

confidence: 99%

Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records

Wang

et al. 2020

Learning Health Systems

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Introduction: We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes. Methods: A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi-specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data. The other 6795 patients, labeled as controls, were matched on age and gender with the cases and randomly selected in the ratio of 9:1. Prediction models with multiple ML algorithms were trained on this cohort using 5-fold cross-validation. XGBoost was used to rank the variable importance. Results: Four subphenotypes were computationally derived. Subphenotype A (n = 273; 28.2%) had more patients with cardiovascular diseases; subphenotype B (n = 221; 27.9%) had more patients with mental health illnesses, such as depression and anxiety; patients in subphenotype C (n = 183; 23.1%) were overall older (mean (SD) age, 79.5 (5.4) years) and had the most comorbidities including diabetes, cardiovascular diseases, and mental health disorders; and subphenotype D (n = 115; 14.5%) included patients who took antidementia drugs and had sensory problems, such as deafness and hearing impairment. The 0-year prediction model for AD risk achieved an area under the receiver operating curve (AUC) of 0.764

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Section: Discussionmentioning

confidence: 99%

Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records

Wang

et al. 2020

Learning Health Systems

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“…Clinical patient data has been widely used to predict outcomes such as length of stay and costs [ 36 , 54 , 55 , 56 ]. Recommendations and guidelines were made based on accurate predictions of such measures [ 57 , 58 , 59 ].…”

Section: Resultsmentioning

confidence: 99%

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

Cecula

Dawoodbhoy

et al. 2021

Heliyon

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Background Despite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research. Methods The OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria. Research 3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face. Conclusion Research widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.

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“…The methods tested included random forest, SVM and logistic regression. The reported AUCs of the used models for predicting Alzheimer's disease were around 0.7 [238].…”

Section: Covid-19mentioning

confidence: 99%

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Arabi

2021

Future Drug. Discov.

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The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

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Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Cited by 106 publications

References 39 publications

Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records

Data‐driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records

Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

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