Multilevel Survival Modeling With Structured Penalties for Disease Prediction From Imaging Genetics Data

Lu, Pascal; Colliot, Olivier

doi:10.1109/jbhi.2021.3100918

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…Their approach promotes the adoption of graph-based deep learning algorithms for illness detection since it enhances classification performance and flexibility. However, [37] found that based just on symptoms, it might be difficult to distinguish between unipolar and bipolar depression. Measuring functional connectivity (FC), particularly dynamic FC, offers one potential way to distinguish BD from UD.…”

Section: In-depth Review Of Different Models For Identification Of Hu...mentioning

confidence: 99%

Review Of Models & Techniques For Identification Of Human Health Diseases

2022

pnr

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Human health diseases are caused due to genetic disorders, environmental conditions, presence of Alpha synuclein bodies, etc. Identification of this disease can be done via estimation of body tremors, slowed movements, fever, change in corpuscles, rigid muscles, impaired posture & balance, pain analysis, loss of automatic movements, speech changes, and writing changes. To estimate these effects, researchers have proposed a wide variety of machine learning techniques that can analyze speech signals, electroencephalogram (EEG) signals, electrocardiogram (ECG) signals, handwriting variations, gait identification & classification, etc. These models are highly variant in terms of their internal operating characteristics, which makes it ambiguous for clinical designers to identify optimal models for their deployments. Furthermore, these models also showcase highly variant performance levels as per their input feature sets. Thus, it is difficult to identify optimal models for a given set of input parameters. To overcome these issues, this paper initially reviews a wide variety of Human Health disease identification models in terms of their contextual nuances, operating advantages, functional limitations, quantitative characteristics, and deployment-specific future scopes. This will assist readers to identify functional models that can be applied for their operation-specific use cases. After referring this comparison, readers will be presented with a comparative analysis of these models in terms of their detection accuracy, classification delay, input datasets, computational complexity, and scalability levels. Based on this comparison, readers will be able to identify optimally performing models as per their qualitative characteristics.

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Section: In-depth Review Of Different Models For Identification Of Hu...mentioning

confidence: 99%

Review Of Models & Techniques For Identification Of Human Health Diseases

2022

pnr

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Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Jeong

Kim

2023

Sci Rep

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Early identification and treatment of moderate cognitive impairment (MCI) can halt or postpone Alzheimer’s disease (AD) and preserve brain function. For prompt diagnosis and AD reversal, precise prediction in the early and late phases of MCI is essential. This research investigates multimodal framework-based multitask learning in the following situations: (1) Differentiating early mild cognitive impairment (eMCI) from late MCI and (2) predicting when an MCI patient would acquire AD. Clinical data and two radiomics features on three brain areas deduced from magnetic resonance imaging were investigated (MRI). We proposed an attention-based module, Stack Polynomial Attention Network (SPAN), to firmly encode clinical and radiomics data input characteristics for successful representation from a small dataset. To improve multimodal data learning, we computed a potent factor using adaptive exponential decay (AED). We used experiments from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort study, which included 249 eMCI and 427 lMCI participants at baseline visits. The proposed multimodal strategy yielded the best c-index score in time prediction of MCI to AD conversion (0.85) and the best accuracy in MCI-stage categorization ($$83.19\%$$ 83.19 % ). Moreover, our performance was equivalent to that of contemporary research.

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Predicting Progression to Clinical Alzheimer’s Disease Dementia Using the Random Survival Forest

Song

Asken

Armstrong

et al. 2023

JAD

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Background: Assessing the risk of developing clinical Alzheimer’s disease (AD) dementia, by machine learning survival analysis approaches, among participants registered in Alzheimer’s Disease Centers is important for AD dementia management. Objective: To construct a prediction model for the onset time of clinical AD dementia using the National Alzheimer Coordinating Center (NACC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) registered cohorts. Methods: A model was constructed using the Random Survival Forest (RSF) approach and internally and externally validated on the NACC cohort and the ADNI cohort. An R package and a Shiny app were provided for accessing the model. Results: We built a predictive model having the six predictors: delayed logical memory score (story recall), CDR® Dementia Staging Instrument - Sum of Boxes, general orientation in CDR®, ability to remember dates and ability to pay bills in the Functional Activities Questionnaire, and patient age. The C indices of the model were 90.82% (SE = 0.71% ) and 86.51% (SE = 0.75% ) in NACC and ADNI respectively. The time-dependent AUC and accuracy at 48 months were 92.48% (SE = 1.12% ) and 88.66% (SE = 1.00% ) respectively in NACC, and 90.16% (SE = 1.12% ) and 85.00% (SE = 1.14% ) respectively in ADNI. Conclusion: The model showed good prediction performance and the six predictors were easy to obtain, cost-effective, and non-invasive. The model could be used to inform clinicians and patients on the probability of developing clinical AD dementia in 4 years with high accuracy.

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Multilevel Survival Modeling With Structured Penalties for Disease Prediction From Imaging Genetics Data

Cited by 3 publications

References 67 publications

Review Of Models & Techniques For Identification Of Human Health Diseases

Review Of Models & Techniques For Identification Of Human Health Diseases

Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay

Predicting Progression to Clinical Alzheimer’s Disease Dementia Using the Random Survival Forest

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