Editing AI for Mental Health (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Depression risk prediction from speech acoustics:'''
<syntaxhighlight lang="python">
import librosa
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler

def extract_speech_features(audio_path: str) -> np.ndarray:
    """Extract acoustic features associated with depression markers."""
    y, sr = librosa.load(audio_path, sr=16000)
    features = []
    # MFCCs (vocal tract characteristics)
    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
    features.extend(mfcc.mean(axis=1))
    features.extend(mfcc.std(axis=1))
    # Pitch (fundamental frequency) — reduced variability in depression
    f0, _, _ = librosa.pyin(y, fmin=50, fmax=500)
    f0_clean = f0[~np.isnan(f0)]
    if len(f0_clean) > 0:
        features.extend([np.mean(f0_clean), np.std(f0_clean), np.median(f0_clean)])
    else:
        features.extend([0, 0, 0])
    # Energy (reduced in depression)
    rms = librosa.feature.rms(y=y)[0]
    features.extend([rms.mean(), rms.std()])
    # Speaking rate (slowed in depression)
    tempo, _ = librosa.beat.beat_track(y=y, sr=sr)
    features.append(float(tempo))
    return np.array(features)

# Train on DAIC-WOZ or similar clinical depression audio dataset
X = np.array([extract_speech_features(f) for f in audio_files])
y = np.array(depression_labels)  # PHQ-9 >= 10 = depressed
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
clf = GradientBoostingClassifier(n_estimators=200, max_depth=4)
clf.fit(X_scaled, y)
# IMPORTANT: clinical decision support only — always refer to human clinician
</syntaxhighlight>

; Mental health AI tools and research
: '''Conversational support''' → Woebot, Wysa, Youper (CBT-based chatbots)
: '''Crisis detection''' → Crisis Text Line (NLP triage), 988 Lifeline AI routing
: '''Clinical NLP''' → AWS Comprehend Medical, Google Healthcare NLP, Clinithink
: '''Passive sensing''' → AWARE Framework (research), Mindstrong (digital biomarkers)
: '''Treatment matching''' → STAR*D analysis, pharmacogenomics + ML (GeneSight)
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