Predicting need for intervention in individuals with congestive heart failure using a home-based telecare system
Bibliographic record
Abstract
We have studied how well the need for a medical intervention can be predicted by a telecare monitoring system. During a study period of about 18 months, 45 elderly individuals with congestive heart failure used a home health monitor to enter daily information pertaining to their symptoms and health status. A total of 8576 alerts were generated by the monitoring system, although in most cases, patient and service provider interaction was not required. When system alerts were considered to be serious, or if symptoms persisted, the patient was contacted. A total of 171 key medical events (6 deaths; 28 hospital admissions; 59 changes in medication; 54 cases of advice given; 24 instances where immediate medical attention was recommended) were recorded in the monitoring logs. A multivariate logistic regression model was developed to predict these medical interventions/events. The model correctly predicted key medical events in 75% of cases with a specificity of 74% and an overall cross-validated accuracy of 74% (95% CI, 68-80%). Key predictors included the number of system alerts, self-rated mobility, self-rated health and self-rated anxiety. This suggests that subjective measures are useful in addition to physiological ones for predicting health status. A multivariate decision support model has potential to supplement practitioners and current telecare systems in identifying heart failure patients in need of medical intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".