Handling Irregularly Sampled Longitudinal Data and Prognostic Modeling of Diabetes Using Machine Learning Technique
Bibliographic record
Abstract
Clinical researchers use prognostic modeling techniques to identify a-prior patient health status and characterize progression patterns. It is highly desirable to predict future health condition especially to implement preventive and intervention strategies in pre-diabetic individuals. Hidden Markov Model (HMM) and its variants are a class of models that provide predictions concerning future condition by exploiting sequences of clinical measurements obtained from a longitudinal sample of patients. Despite the advantages of using these models for prognostic modeling, it still face barriers and significant challenges, to effectively learn dynamic interactions, when using irregularly sampled longitudinal Electronic Medical Records (EMRs) data. Newton's divide difference method (NDDM) is a classical approach for handling irregular data in terms of divided difference. However, as it is polynomial approximation technique, it suffers with Runge Phenomenon. The problem can be even more severe when the interval is a bit extended. Therefore, to tackle this problem, we proposed a novel approximation method based on NDDM as a component with HMM in order to estimate the 8 years risk of developing Type 2 Diabetes Mellitus (T2DM) in a particular individual. The proposed method is evaluated on real world clinical data obtained from CPCSSN. The results demonstrated that our proposed technique has the ability to exploit the available irregularly sampled EMRs data for effective approximation and improved prediction accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".