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9b1756a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 | """Shared runtime effects for observation noise and episode time pressure."""
from __future__ import annotations
from dataclasses import dataclass
import random
from typing import Any
from .patient_state import PatientState
def _clip(value: float, lower: float, upper: float) -> float:
return max(lower, min(value, upper))
@dataclass(frozen=True)
class ObservationNoiseConfig:
"""Configures noisy and partially observed bedside monitor readings."""
noise_level: float = 0.0
@property
def enabled(self) -> bool:
return self.noise_level > 0.0
@property
def normalized_level(self) -> float:
return _clip(float(self.noise_level), 0.0, 1.0)
class NoisyObservation:
"""Applies configurable noise and dropouts to observed patient state."""
def __init__(self, noise_level: float = 0.0) -> None:
self._config = ObservationNoiseConfig(noise_level=noise_level)
@property
def config(self) -> ObservationNoiseConfig:
return self._config
def apply(
self,
state: PatientState,
*,
rng: random.Random,
) -> tuple[PatientState, dict[str, Any]]:
if not self._config.enabled:
return state.model_copy(deep=True), {
"enabled": False,
"noise_level": 0.0,
"masked_fields": [],
"perturbed_fields": [],
}
level = self._config.normalized_level
updates = state.model_dump()
masked_fields: list[str] = []
perturbed_fields: list[str] = []
self._perturb_numeric(updates, "heart_rate_bpm", rng, std_dev=5.0 * level, lower=0.0, upper=240.0, perturbed_fields=perturbed_fields)
self._perturb_numeric(updates, "systolic_bp_mmhg", rng, std_dev=4.0 * level, lower=40.0, upper=260.0, perturbed_fields=perturbed_fields)
self._perturb_numeric(updates, "diastolic_bp_mmhg", rng, std_dev=3.0 * level, lower=20.0, upper=180.0, perturbed_fields=perturbed_fields)
self._perturb_numeric(updates, "spo2", rng, std_dev=0.02 * level, lower=0.5, upper=1.0, perturbed_fields=perturbed_fields)
self._perturb_numeric(updates, "respiration_rate_bpm", rng, std_dev=2.0 * level, lower=4.0, upper=60.0, perturbed_fields=perturbed_fields)
self._perturb_numeric(updates, "etco2_mmhg", rng, std_dev=2.5 * level, lower=5.0, upper=90.0, perturbed_fields=perturbed_fields)
self._perturb_numeric(updates, "core_temperature_c", rng, std_dev=0.2 * level, lower=30.0, upper=43.0, perturbed_fields=perturbed_fields)
if rng.random() < 0.05 * level:
updates["spo2"] = None
masked_fields.append("spo2")
if rng.random() < 0.035 * level:
updates["systolic_bp_mmhg"] = None
updates["diastolic_bp_mmhg"] = None
masked_fields.extend(["systolic_bp_mmhg", "diastolic_bp_mmhg"])
if rng.random() < 0.03 * level:
updates["respiration_rate_bpm"] = None
masked_fields.append("respiration_rate_bpm")
if rng.random() < 0.02 * level:
updates["etco2_mmhg"] = None
masked_fields.append("etco2_mmhg")
if updates.get("systolic_bp_mmhg") is None or updates.get("diastolic_bp_mmhg") is None:
updates["mean_arterial_pressure_mmhg"] = None
updates["shock_index"] = None
else:
systolic = float(updates["systolic_bp_mmhg"])
diastolic = float(updates["diastolic_bp_mmhg"])
updates["mean_arterial_pressure_mmhg"] = round((systolic + 2.0 * diastolic) / 3.0, 3)
if updates.get("heart_rate_bpm") not in (None, 0):
updates["shock_index"] = round(float(updates["heart_rate_bpm"]) / systolic, 3)
if rng.random() < 0.02 * level:
updates["breath_sounds"] = "unclear"
perturbed_fields.append("breath_sounds")
observed_state = PatientState(**updates)
metadata = {
"enabled": True,
"noise_level": round(level, 3),
"masked_fields": sorted(set(masked_fields)),
"perturbed_fields": sorted(set(perturbed_fields)),
}
return observed_state, metadata
@staticmethod
def _perturb_numeric(
updates: dict[str, Any],
field_name: str,
rng: random.Random,
*,
std_dev: float,
lower: float,
upper: float,
perturbed_fields: list[str],
) -> None:
current_value = updates.get(field_name)
if current_value is None or std_dev <= 0.0:
return
noisy_value = _clip(float(current_value) + rng.gauss(0.0, std_dev), lower, upper)
updates[field_name] = round(noisy_value, 3)
perturbed_fields.append(field_name)
@dataclass(frozen=True)
class TimePressureConfig:
"""Configures the urgency curve for delayed trauma intervention."""
enabled: bool = False
onset_s: float = 180.0
escalation_per_minute: float = 0.15
min_intervention_effectiveness: float = 0.45
class TimePressureMechanic:
"""Computes time-pressure multipliers for delayed trauma management."""
def __init__(
self,
*,
enabled: bool = False,
onset_s: float = 180.0,
escalation_per_minute: float = 0.15,
min_intervention_effectiveness: float = 0.45,
) -> None:
self._config = TimePressureConfig(
enabled=bool(enabled),
onset_s=float(onset_s),
escalation_per_minute=float(escalation_per_minute),
min_intervention_effectiveness=float(min_intervention_effectiveness),
)
@property
def config(self) -> TimePressureConfig:
return self._config
def deterioration_multiplier(
self,
*,
sim_time_s: float,
injury_severity: float,
unstable: bool,
) -> float:
if not self._config.enabled or not unstable or sim_time_s < self._config.onset_s:
return 1.0
severity = _clip(float(injury_severity), 0.0, 1.0)
excess_seconds = max(0.0, float(sim_time_s) - self._config.onset_s)
return 1.0 + (excess_seconds / 60.0) * self._config.escalation_per_minute * severity
def intervention_effectiveness_multiplier(
self,
*,
sim_time_s: float,
injury_severity: float,
unstable: bool,
) -> float:
deterioration = self.deterioration_multiplier(
sim_time_s=sim_time_s,
injury_severity=injury_severity,
unstable=unstable,
)
if deterioration <= 1.0:
return 1.0
loss = (deterioration - 1.0) * 0.5
return max(self._config.min_intervention_effectiveness, 1.0 - loss)
def as_metadata(
self,
*,
sim_time_s: float,
injury_severity: float,
unstable: bool,
) -> dict[str, Any]:
return {
"enabled": self._config.enabled,
"onset_s": self._config.onset_s,
"escalation_per_minute": self._config.escalation_per_minute,
"injury_severity": round(_clip(float(injury_severity), 0.0, 1.0), 3),
"deterioration_multiplier": round(
self.deterioration_multiplier(
sim_time_s=sim_time_s,
injury_severity=injury_severity,
unstable=unstable,
),
3,
),
"intervention_effectiveness_multiplier": round(
self.intervention_effectiveness_multiplier(
sim_time_s=sim_time_s,
injury_severity=injury_severity,
unstable=unstable,
),
3,
),
}
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