An Interactive and Predictive Pre-diagnostic Model for Healthcare based on Data Provenance

The future of healthcare may look completely different from the current clinic-center services. Rapidly growing and developing technologies are expected to change clinics throughout the world. However, the healthcare delivered to impaired patients, such as elderly and disabled people, possibly still requires hands-on human expertise. The aim of this study is to propose a predictive model that pre-diagnose illnesses by analyzing symptoms that are interactively taken from patients via several hand gestures during a period of time. This is particularly helpful in assisting clinicians and doctors to gain better understanding and make more accurate decisions about future plans for their patients’ situations. The hand gestures are detected, the time of the gesture is recorded and then they are associated to their designated symptoms. This information is captured in the form of provenance graphs constructed based on the W3C PROV data model. The provenance graph is analyzed by extracting several network metrics and then supervised machine-learning algorithms are used to build a predictive model. The model is used to predict diseases from the symptoms with a maximum accuracy of 84.5%.

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Immune profiling of plasma-derived extracellular vesicles identifies Parkinson disease

Neurology Neuroimmunology & Neuroinflammation ◽

10.1212/nxi.0000000000000866 ◽

2020 ◽

Vol 7 (6) ◽

pp. e866 ◽

Cited By ~ 2

Author(s):

Elena Vacchi ◽

Jacopo Burrello ◽

Dario Di Silvestre ◽

Alessio Burrello ◽

Sara Bolis ◽

...

Keyword(s):

Parkinson Disease ◽

Diagnostic Performance ◽

Characteristic Curve ◽

Area Under The Curve ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Diagnostic Model ◽

Surface Markers ◽

Model Based ◽

Immune Profiling

ObjectiveTo develop a diagnostic model based on plasma-derived extracellular vesicle (EV) subpopulations in Parkinson disease (PD) and atypical parkinsonism (AP), we applied an innovative flow cytometric multiplex bead-based platform.MethodsPlasma-derived EVs were isolated from PD, matched healthy controls, multiple system atrophy (MSA), and AP with tauopathies (AP-Tau). The expression levels of 37 EV surface markers were measured by flow cytometry and correlated with clinical scales. A diagnostic model based on EV surface markers expression was built via supervised machine learning algorithms and validated in an external cohort.ResultsDistinctive pools of EV surface markers related to inflammatory and immune cells stratified patients according to the clinical diagnosis. PD and MSA displayed a greater pool of overexpressed immune markers, suggesting a different immune dysregulation in PD and MSA vs AP-Tau. The receiver operating characteristic curve analysis of a compound EV marker showed optimal diagnostic performance for PD (area under the curve [AUC] 0.908; sensitivity 96.3%, specificity 78.9%) and MSA (AUC 0.974; sensitivity 100%, specificity 94.7%) and good accuracy for AP-Tau (AUC 0.718; sensitivity 77.8%, specificity 89.5%). A diagnostic model based on EV marker expression correctly classified 88.9% of patients with reliable diagnostic performance after internal and external validations.ConclusionsImmune profiling of plasmatic EVs represents a crucial step toward the identification of biomarkers of disease for PD and AP.

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Emergency department admissions during COVID-19: explainable machine learning to characterise data drift and detect emergent health risks

10.1101/2021.05.27.21257713 ◽

2021 ◽

Author(s):

Christopher Duckworth ◽

Francis P Chmiel ◽

Dan K. Burns ◽

Zlatko D Zlatev ◽

Neil M White ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Predictive Model ◽

Clinical Outcomes ◽

Health Risks ◽

Concept Drift ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Healthcare Settings ◽

Acute Healthcare

Supervised machine learning algorithms deployed in acute healthcare settings use data describing historical episodes to predict clinical outcomes. Clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (a phenomenon known as data drift), and so can the relationship between episode characteristics and associated clinical outcomes (so-called, concept drift). We demonstrate how explainable machine learning can be used to monitor data drift in a predictive model deployed within a hospital emergency department. We use the COVID-19 pandemic as an exemplar cause of data drift, which has brought a severe change in operational circumstances. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission to hospital during an emergency department attendance. We evaluate our model's performance on attendances occurring pre-pandemic (AUROC 0.856 95\%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC 0.826 95\%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature's SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.

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Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i02.20025 ◽

2021 ◽

Vol 17 (02) ◽

pp. 90

Author(s):

Inssaf El Guabassi ◽

Zakaria Bousalem ◽

Rim Marah ◽

Aimad Qazdar

Keyword(s):

Machine Learning ◽

Linear Regression ◽

Predictive Model ◽

Learning Algorithms ◽

Research Work ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

The Future ◽

Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

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Development of a Classification Model for CD4 Count of HIV Patients Using Supervised Machine Learning Algorithms

Computational Models for Biomedical Reasoning and Problem Solving - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-7467-5.ch006 ◽

2019 ◽

pp. 149-176

Author(s):

Peter Adebayo Idowu ◽

Jeremiah Ademola Balogun

Keyword(s):

Predictive Model ◽

Decision Trees ◽

Machine Learning Algorithms ◽

Cd4 Count ◽

Classification Model ◽

Supervised Machine Learning ◽

Support Vector ◽

Hiv Patients ◽

The Relationship

This chapter was developed with a view to present a predictive model for the classification of the level of CD4 count of HIV patients receiving ART/HAART treatment in Nigeria. Following the review of literature, the pre-determining factors for determining CD4 count were identified and validated by experts while historical data explaining the relationship between the factors and CD4 count level was collected. The predictive model for CD4 count level was formulated using C4.5 decision trees (DT), support vector machines (SVM), and the multi-layer perceptron (MLP) classifiers based on the identified factors which were formulated using WEKA software and validated. The results showed that decision trees algorithm revealed five (5) important variables, namely age group, white blood cell count, viral load, time of diagnosing HIV, and age of the patient. The MLP had the best performance with a value of 100% followed by the SVM with an accuracy of 91.1%, and both were observed to outperform the DT algorithm used.

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Osteoporosis Risk Predictive Model Using Supervised Machine Learning Algorithms

Science Research ◽

10.11648/j.sr.20170506.11 ◽

2017 ◽

Vol 5 (6) ◽

pp. 78

Author(s):

Egejuru Ngozi Chidozie

Keyword(s):

Machine Learning ◽

Predictive Model ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Osteoporosis Risk

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Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

Fieldwork in Religion ◽

10.1558/firn.40610 ◽

2020 ◽

Vol 14 (2) ◽

pp. 140-159

Author(s):

Anthony-Paul Cooper ◽

Emmanuel Awuni Kolog ◽

Erkki Sutinen

Keyword(s):

Machine Learning ◽

Online Community ◽

High Volume ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Social Media Data ◽

Twitter Data ◽

Resource Intensity ◽

Media Data ◽

Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

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