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Adapting Animal Models to Assess Sufficiency of Fluid Resuscitation in Humans (Student Abstract)
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Transfers random-forest models trained on pig hemorrhage data to human trauma patients to assess sufficiency of fluid resuscitation, using transfer learning (MIX) and SMOTE, evaluated by overlap score.
Problem
Assessing when fluid resuscitation is sufficient in critically ill trauma patients is risky to get wrong, but human data is limited and collecting it via harmful protocols is infeasible; prior validation was only in pigs.
Approach
Use a random forest trained on pig hemorrhage experiments as a source model, adapt it to two human trauma datasets via transfer learning (MIX algorithm) with and without SMOTE, and convert sufficiency probabilities into start/stop resuscitation decisions calibrated on pig data.
Key results
- For patients with an arterial line, transfer learning without SMOTE achieved the highest resuscitation overlap of 0.368 ± 0.033.
- For patients without an arterial line, human-only random forest without SMOTE achieved the highest resuscitation overlap of 0.593 ± 0.049.
- Optimal conversion parameters were a decision threshold of 0.77 and zero consecutive sufficiencies/insufficiencies.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Adapt the pig model to human data with MIX | Resuscitation decision model · Humans with arterial line | Resuscitation overlap: 0.002 ± 0.001 -> 0.368 ± 0.033 | Helped |
| Adapt the pig model to human data with MIX | Resuscitation decision model · Humans with arterial line | No resuscitation overlap: 0.023 ± 0.018 -> 0.493 ± 0.014 | Helped |
| Adapt the pig model to human data with MIX | Resuscitation decision model · Humans without arterial line | Resuscitation overlap: 0.593 ± 0.049 -> 0.186 ± 0.028 | Hurt |
| Adapt the pig model to human data with MIX | Resuscitation decision model · Humans without arterial line | No resuscitation overlap: 0.089 ± 0.027 -> 0.556 ± 0.018 | Helped |
| Apply SMOTE to the human-only model (ablation) | Human-only resuscitation decision model · Humans with arterial line | No resuscitation overlap: 0.023 ± 0.018 -> 0.598 ± 0.023 | Helped |
| Apply SMOTE to the human-only model (ablation) | Human-only resuscitation decision model · Humans with arterial line | Resuscitation overlap: 0.002 ± 0.001 -> 0.272 ± 0.028 | Helped |
| Apply SMOTE to the human-only model (ablation) | Human-only resuscitation decision model · Humans without arterial line | No resuscitation overlap: 0.089 ± 0.027 -> 0.587 ± 0.02 | Helped |
| Apply SMOTE to the human-only model (ablation) | Human-only resuscitation decision model · Humans without arterial line | Resuscitation overlap: 0.593 ± 0.049 -> 0.214 ± 0.031 | Hurt |
| Apply SMOTE to the transfer learning model (ablation) | Transfer learning resuscitation decision model · Humans with arterial line | No resuscitation overlap: 0.493 ± 0.014 -> 0.63 ± 0.027 | Helped |
| Apply SMOTE to the transfer learning model (ablation) | Transfer learning resuscitation decision model · Humans with arterial line | Resuscitation overlap: 0.368 ± 0.033 -> 0.243 ± 0.028 | Hurt |
| Apply SMOTE to the transfer learning model (ablation) | Transfer learning resuscitation decision model · Humans without arterial line | No resuscitation overlap: 0.556 ± 0.018 -> 0.564 ± 0.019 | Helped |
| Apply SMOTE to the transfer learning model (ablation) | Transfer learning resuscitation decision model · Humans without arterial line | Resuscitation overlap: 0.186 ± 0.028 -> 0.182 ± 0.028 | Hurt |
Surprising findings
- Transfer learning from pig data did not help on the human dataset lacking arterial line MAP features, where human-only models performed best.
Limitations
- Transfer benefit depends on availability of features such as MAP; absent in the second human dataset.
- Class imbalance makes accuracy unsuitable; overlap metric used instead of standard classification metrics.
- Small human cohorts (62 and 98 patients).
Experimental setup
Datasets: Human trauma patient vital sign data (62 arterial line patients) Human trauma patient vital sign data (98 patients without arterial line) Pig induced hemorrhage dataset (30 pigs)
Benchmarks: Human trauma patient datasets with leave-one-subject-out cross-validation
Metrics: resuscitation overlap no resuscitation overlap
Baselines: Human only random forest Human only with SMOTE
Builds on: MIX transfer learning (Segev et al. 2016) SMOTE Li, Pinsky, and Dubrawski 2022 pig sufficiency model
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