Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni, Gopi; Sagaro, Getu Gamo; Chinatalapudi, Nalini; Amenta, Francesco

doi:10.3390/jpm10020021

Cited by 239 publications

(129 citation statements)

References 48 publications

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“…Common approaches to identify groups of patients with similar time-dependent characteristics include time-series analysis methods 16 – 18 and clustering techniques 19 , 20 . Other work has focused on the application of patient similarity to specific use cases: e.g., for predicting disease onset risk 21 , 22 , drug effectiveness 23 , identifying disease sub-phenotypes 24 , personalized risk factors 25 , personalizing blood glucose prediction 26 , and diagnosing chronic diseases 27 . There has also been work on clinical decision support (CDS) approaches that leverage patient similarity.…”

Section: Introductionmentioning

confidence: 99%

Personalized treatment options for chronic diseases using precision cohort analytics

Ng¹,

Kartoun²,

Stavropoulos³

et al. 2021

Sci Rep

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To support point-of-care decision making by presenting outcomes of past treatment choices for cohorts of similar patients based on observational data from electronic health records (EHRs), a machine-learning precision cohort treatment option (PCTO) workflow consisting of (1) data extraction, (2) similarity model training, (3) precision cohort identification, and (4) treatment options analysis was developed. The similarity model is used to dynamically create a cohort of similar patients, to inform clinical decisions about an individual patient. The workflow was implemented using EHR data from a large health care provider for three different highly prevalent chronic diseases: hypertension (HTN), type 2 diabetes mellitus (T2DM), and hyperlipidemia (HL). A retrospective analysis demonstrated that treatment options with better outcomes were available for a majority of cases (75%, 74%, 85% for HTN, T2DM, HL, respectively). The models for HTN and T2DM were deployed in a pilot study with primary care physicians using it during clinic visits. A novel data-analytic workflow was developed to create patient-similarity models that dynamically generate personalized treatment insights at the point-of-care. By leveraging both knowledge-driven treatment guidelines and data-driven EHR data, physicians can incorporate real-world evidence in their medical decision-making process when considering treatment options for individual patients.

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Section: Introductionmentioning

confidence: 99%

Personalized treatment options for chronic diseases using precision cohort analytics

Ng¹,

Kartoun²,

Stavropoulos³

et al. 2021

Sci Rep

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show abstract

“…Finally, it seems clear that complex and multidimensional classification problems could take advantage of machine learning techniques applied to clinical data for supporting clinical decisions [ 47 , 48 , 49 , 50 , 51 ]. In the context of pain and pain chronification, machine learning approaches have recently been applied to several pain syndromes [ 24 , 52 , 53 , 54 ], including fibromyalgia [ 55 , 56 , 57 ] and chronic lower back pain [ 58 , 59 , 60 , 61 ]. While traditional statistical analyses commonly make some a priori assumptions about the data model (e.g., normality) and about the relationships among variables (e.g., linearity), machine learning prioritizes a “distribution-free” context.…”

Section: Introductionmentioning

confidence: 99%

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

Santana

Montoya

2020

Diagnostics

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In the last decade, machine learning has been widely used in different fields, especially because of its capacity to work with complex data. With the support of machine learning techniques, different studies have been using data-driven approaches to better understand some syndromes like mild cognitive impairment, Alzheimer’s disease, schizophrenia, and chronic pain. Chronic pain is a complex disease that can recurrently be misdiagnosed due to its comorbidities with other syndromes with which it shares symptoms. Within that context, several studies have been suggesting different machine learning algorithms to classify or predict chronic pain conditions. Those algorithms were fed with a diversity of data types, from self-report data based on questionnaires to the most advanced brain imaging techniques. In this study, we assessed the sensitivity of different algorithms and datasets classifying chronic pain syndromes. Together with this assessment, we highlighted important methodological steps that should be taken into account when an experiment using machine learning is conducted. The best results were obtained by ensemble-based algorithms and the dataset containing the greatest diversity of information, resulting in area under the receiver operating curve (AUC) values of around 0.85. In addition, the performance of the algorithms is strongly related to the hyper-parameters. Thus, a good strategy for hyper-parameter optimization should be used to extract the most from the algorithm. These findings support the notion that machine learning can be a powerful tool to better understand chronic pain conditions.

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“…ML models are highly acknowledged in real-time clinical practice and also in diagnosis and AD treatment selection [ 41 ]. Several MRI works have been integrated into ML models to make AD predictions [ 12 , 17 , 42 ], but there has been no comprehensive model to amplify model accuracy.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

Battineni

Chintalapudi

Amenta

et al. 2020

JCM

Self Cite

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Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

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Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Cited by 239 publications

References 48 publications

Personalized treatment options for chronic diseases using precision cohort analytics

Personalized treatment options for chronic diseases using precision cohort analytics

Chronic Pain Diagnosis Using Machine Learning, Questionnaires, and QST: A Sensitivity Experiment

A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

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