State Based Hidden Markov Models for Temporal Pattern Discovery in Critical Care
Why this work is in the frame
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Bibliographic record
Abstract
We are studying the challenge of finding a good set of features that represent well the temporal aspects in time series data. We argue that discovery of such features could be crucial to understanding hidden relationships in data. In particular, in critical care where time oriented data is generated every second on patients physiological features, discovery of any hidden relationships could aid in discovery of unknown and potentially life threatening conditions before they happen. Additionally, this discovery could help in better dissemination of healthcare services leading to better outcomes and experiences for patients. To facilitate this process, this research explores two research questions; (a) can discovery of temporal relationships in data help in learning hidden aspects in differing patient cohort and (b) with respect to elderly patients receiving telehealth services, can detection of abnormal patterns help in identifying patients at risk of adverse events before they happen. In this paper, we introduce a model for temporal pattern mining by; (1) applying principles from finite state machines augmented with hidden markov models and temporal abstraction for identifying temporal relations in data, (2) generating temporal patterns by augmenting similar relationships, (3) formulating a process for mining frequently occurring temporal patterns and (4) using the resulting mined patterns to build a temporal classification system. Such a classification system can be effective at characterizing normal and abnormal behaviors in patients data and flag when a patient is at risk of a potential adverse event.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it