Mining Sequential Patterns with Timelines from Digital Health Data
Bibliographic record
Abstract
Descriptive pattern mining is a useful tool in expansion of knowledge. One such area of descriptive pattern mining is that of sequential pattern mining. In sequential mining, items maintain an order of occurrence. In this paper, we present a digital health solution for mining sequential patterns from real-life healthcare data. Specifically, it is a non-trivial extension to the sequential mining algorithm PrefixSpan. Through an association of time, we find improved relevance of a pattern overall significance relative to a focal point. This is particularly useful in the medical domain, where significance of information varies depending on the time of its occurrence. For example, consider a time of being diagnosed with a disease. A condition occurring 16 years prior to the time of diagnosis provides less information than the same condition occurring 2 years prior to diagnosis. In traditional sequential mining, both conditions would equally contribute to support, despite their unequal value in describing causes of diagnosis. To resolve such issue, we provide an inclusion of two additional user-defined parameters to incorporate time within itemsets—namely, a timeline interval (describing the length of an interval, of which itemsets of different intervals are treated separately by their difference in time to a focal point), as well as a maximal window (denoting the maximal interval that disallows for any greater time difference than such interval). With timelines associated to itemsets, relevance of itemsets have improved interpretability for domain experts.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".