import streamlit as st import numpy as np import random # Initialize constants DOCTOR_ACTIONS = ["Prescribe Medication", "Recommend Tests", "Consult Clinician", "Schedule Surgery"] NURSE_ACTIONS = ["Monitor Vitals", "Administer Medication", "Report to Doctor", "Assist Surgery"] PATIENT_CONDITIONS = ["Healthy", "Mild Illness", "Chronic Illness", "Emergency"] DOCTOR_EMOTIONS = ["Calm", "Stressed", "Overwhelmed"] NURSE_EMOTIONS = ["Focused", "Fatigued", "Panicked"] # Rewards and Penalties REWARDS = { "Prescribe Medication": 12, "Recommend Tests": 7, "Consult Clinician": 9, "Schedule Surgery": 17, "Monitor Vitals": 4, "Administer Medication": 12, } PENALTIES = { "Wrong Medication": -5, "Missed Diagnosis": -10, "Stress-Induced Mistake": -7, } # Initialize session state for metrics and satisfaction if "performance_metrics" not in st.session_state: st.session_state.performance_metrics = { "Doctor": {"successful_treatments": 0, "failed_treatments": 0}, "Nurse": {"successful_assists": 0, "failed_assists": 0} } if "patient_satisfaction" not in st.session_state: st.session_state.patient_satisfaction = 100 # Define the Agent class for Q-learning class Agent: def __init__(self, agent_type): self.agent_type = agent_type self.actions = DOCTOR_ACTIONS if agent_type == "Doctor" else NURSE_ACTIONS self.q_table = np.zeros((len(PATIENT_CONDITIONS), len(self.actions))) def choose_action(self, state, exploration_rate=0.05): if random.uniform(0, 1) < exploration_rate: # Explore return random.randint(0, len(self.actions)-1) else: # Exploit (choose best action) return np.argmax(self.q_table[state]) def update_q_value(self, state, action, reward, learning_rate=0.1, discount_factor=0.9): old_q_value = self.q_table[state, action] best_future_q_value = np.max(self.q_table) new_q_value = old_q_value + learning_rate * (reward + discount_factor * best_future_q_value - old_q_value) self.q_table[state, action] = new_q_value # Instantiate agents doctor_agent = Agent("Doctor") nurse_agent = Agent("Nurse") # Function to simulate a special event def simulate_special_event(): event = random.choice([None, "Disease Outbreak", "Resource Shortage"]) if event == "Disease Outbreak": st.subheader("Special Event: Disease Outbreak") st.write("A sudden disease outbreak has flooded the hospital with new patients. Resources are limited!") elif event == "Resource Shortage": st.subheader("Special Event: Resource Shortage") st.write("A medical supply shortage is impacting the hospital. Staff must prioritize high-risk patients.") return event # Function to handle complications during treatment def handle_complications(): complication = random.choices( [None, "Allergic Reaction", "Unexpected Complication"], weights=[0.6, 0.2, 0.2] )[0] penalty = 0 if complication: st.subheader(f"Complication: {complication}") if complication == "Allergic Reaction": st.write("The patient has developed an allergic reaction to the prescribed medication!") penalty = PENALTIES["Wrong Medication"] st.session_state.patient_satisfaction -= 10 elif complication == "Unexpected Complication": st.write("An unexpected complication occurred during surgery!") penalty = PENALTIES["Stress-Induced Mistake"] st.session_state.patient_satisfaction -= 15 return penalty # Main simulation button logic if st.button("Run Simulation"): # Simulate a special event special_event = simulate_special_event() # Patient condition simulation patient_state = random.choice(PATIENT_CONDITIONS) st.write(f"Simulated Patient Condition: {patient_state}") patient_index = PATIENT_CONDITIONS.index(patient_state) # Doctor and nurse emotions doctor_emotion = random.choice(DOCTOR_EMOTIONS) nurse_emotion = random.choice(NURSE_EMOTIONS) st.write(f"Doctor's Emotional State: {doctor_emotion}") st.write(f"Nurse's Emotional State: {nurse_emotion}") # Doctor Action doctor_action_index = doctor_agent.choose_action(patient_index) doctor_action = DOCTOR_ACTIONS[doctor_action_index] st.write(f"Doctor's Chosen Action: {doctor_action}") # Nurse Action nurse_action_index = nurse_agent.choose_action(patient_index) nurse_action = NURSE_ACTIONS[nurse_action_index] st.write(f"Nurse's Chosen Action: {nurse_action}") # Handle potential complications penalty = handle_complications() # Reward or penalty reward = REWARDS.get(doctor_action, 0) if penalty == 0 else penalty if doctor_emotion in ["Stressed", "Overwhelmed"]: penalty += PENALTIES["Stress-Induced Mistake"] # Update Q-values doctor_agent.update_q_value(patient_index, doctor_action_index, reward) st.write(f"Doctor's Reward/Penalty: {reward}") st.write(f"Patient Satisfaction: {st.session_state.patient_satisfaction}") # Outcome and Performance Metrics Update outcome = random.choices( ["Recovery", "Further Treatment Needed", "Complication"], weights=[0.6, 0.3, 0.1] )[0] st.write(f"Patient Status after Treatment: {outcome}") if outcome == "Recovery": st.session_state.performance_metrics["Doctor"]["successful_treatments"] += 1 st.session_state.performance_metrics["Nurse"]["successful_assists"] += 1 else: st.session_state.performance_metrics["Doctor"]["failed_treatments"] += 1 st.session_state.performance_metrics["Nurse"]["failed_assists"] += 1 st.write("Simulation completed! Run again for different outcomes.") # Display performance metrics st.subheader("Performance Metrics:") st.write(f"Doctor's Successful Treatments: {st.session_state.performance_metrics['Doctor']['successful_treatments']}") st.write(f"Doctor's Failed Treatments: {st.session_state.performance_metrics['Doctor']['failed_treatments']}") st.write(f"Nurse's Successful Assists: {st.session_state.performance_metrics['Nurse']['successful_assists']}") st.write(f"Nurse's Failed Assists: {st.session_state.performance_metrics['Nurse']['failed_assists']}")