A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Bucholc, Magda; Ding, Xuemei; Haiying,; Hy, Wang; Glass, David H.; Huī, Wánɡ; Prasad, Girijesh; Maguire, Liam; Bjourson, Anthony J.; McClean, Paula; Todd, Stephen; Finn, David P.; Wong‐Lin, KongFatt

doi:10.1016/j.eswa.2019.04.022

Cited by 87 publications

(94 citation statements)

References 118 publications

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“…Both CS and NB have important roles in improving the performance of our model. Similar observations have been reported in the literature 20 . Note that not all biomarkers of these modalities are used in the training process, but only the features selected by the RFE technique.…”

Section: Resultssupporting

confidence: 93%

“…These features form about 15% of the whole feature set. Inspired by 20 , the features selected with RF-RFE are clustered into six modality kinds: (1) cognitive scores (CS) [eight features]; (2) neuropsychological battery (NB) [six features]; (3) MRI [two features], (4) PET [three features], (5) genetics [five features], and (6) medical history (MH) (lab test and demographics) [four features]. It is worth noting that the selected features based on RF-RFE are the most discriminant and informative features for the current classification problem ( P < 0.05, Kruskal–Wallis test).…”

Section: Resultsmentioning

confidence: 99%

“…AD could be diagnosed after two years of memory problems 19 . There is an emerging need for advanced AD detection and prediction models that can serve as a helping hand for medical practitioners to diagnose or detect the disease earlier and more accurately 5 , 19 , 20 . These models can be used to build the inference engines of an AD clinical decision support system (CDSS).…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, although these studies achieved tremendous advances in prediction, they are not expected to be acceptable in the medical environment. There exists a significant gap between academic research outcomes and their effective utilization in medical practice due to several reasons 20 . The entire patient medical history must be considered to achieve intuitive, stable, and robust decisions 20 .…”

Section: Introductionmentioning

confidence: 99%

“…There exists a significant gap between academic research outcomes and their effective utilization in medical practice due to several reasons 20 . The entire patient medical history must be considered to achieve intuitive, stable, and robust decisions 20 . Most DL-based methods only concentrate on analysis of neuroimaging, i.e., Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET).…”

Section: Introductionmentioning

confidence: 99%

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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

El–Sappagh

Alonso

Islam

et al. 2021

Sci Rep

198

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Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

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Section: Resultssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

El–Sappagh

Alonso

Islam

et al. 2021

Sci Rep

198

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Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

Alzheimer's & Dementia

Self Cite

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Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well‐documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI‐enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co‐produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care.HIGHLIGHTS Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

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Current advances in digital cognitive assessment for preclinical Alzheimer's disease

Öhman

Hassenstab

Berron

et al. 2021

Alz & Dem Diag Ass & Dis Mo

105

102

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There is a pressing need to capture and track subtle cognitive change at the preclinical stage of Alzheimer's disease (AD) rapidly, cost‐effectively, and with high sensitivity. Concurrently, the landscape of digital cognitive assessment is rapidly evolving as technology advances, older adult tech‐adoption increases, and external events (i.e., COVID‐19) necessitate remote digital assessment. Here, we provide a snapshot review of the current state of digital cognitive assessment for preclinical AD including different device platforms/assessment approaches, levels of validation, and implementation challenges. We focus on articles, grants, and recent conference proceedings specifically querying the relationship between digital cognitive assessments and established biomarkers for preclinical AD (e.g., amyloid beta and tau) in clinically normal (CN) individuals. Several digital assessments were identified across platforms (e.g., digital pens, smartphones). Digital assessments varied by intended setting (e.g., remote vs. in‐clinic), level of supervision (e.g., self vs. supervised), and device origin (personal vs. study‐provided). At least 11 publications characterize digital cognitive assessment against AD biomarkers among CN. First available data demonstrate promising validity of this approach against both conventional assessment methods (moderate to large effect sizes) and relevant biomarkers (predominantly weak to moderate effect sizes). We discuss levels of validation and issues relating to usability, data quality, data protection, and attrition. While still in its infancy, digital cognitive assessment, especially when administered remotely, will undoubtedly play a major future role in screening for and tracking preclinical AD.

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A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Cited by 87 publications

References 118 publications

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Artificial intelligence for dementia research methods optimization

Current advances in digital cognitive assessment for preclinical Alzheimer's disease

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