Integrating Machine Learning and Big Data Analytics to Transform Patient Outcomes in Chronic Disease Management
Bibliographic record
Abstract
Chronic diseases such as diabetes, cancer, and cardiovascular diseases account for nearly 70% of the total healthcare costs that can have a much broader negative impact on the quality of life of patients with decreased life expectancies, productivity loss, and increased healthcare costs. Therefore, a concerted effort is required to alleviate the burden of chronic diseases. Machine learning is becoming more ubiquitous in healthcare because of the exponential growth of electronic health records and the substantial advancement of big data analytics capability. In this study, systematic literature review approaches are employed to identify the machine learning and big data analytics technologies that are already implemented in chronic disease management. Each technology is thoroughly examined in terms of its definition, rationale, and types. In addition, deep coverage of implementation studies of the technologies is provided regarding the motivation, objective, methodology, type of chronic disease, findings, and limitations. This study focuses on how ML and BDA-enabled chronic disease management systems facilitate the decisions made by doctors, patients, and policymakers in detecting, predicting, managing, and integrating into the patient-centric care paradigm across disease evolution stages. Based on the analytics need and the proposed BDA architecture, this research offers integrated perspectives on how to transform patient outcomes for chronic disease management by synergistically implementing ML and BDA. This research has crucial academic, technical, and managerial implications and opens up other future research avenues. Despite the enormous potential of machine learning and big data analytics to transform chronic disease management, only a handful of innovations have been subjected to larger-scale trials, hindering a swift translation into patient-centric chronic disease care. Many of the innovations hinge on inaccurate or ambiguous clinical concepts and few have considered the social dynamics involved in chronic disease. Concerns surrounding the responsibility of machines or algorithms for unintended negative consequences and the limited accessibility and equity of AI-based technologies further hinder the adoption of these innovations. Hence, innovations should pay special attention to conveyability and accountability that maintain a balance between complexity and interpretability and engage end-users early in the design phase through participatory design principles to foster trust in technology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".