H3 is a new three-hop index that predicts physician referrals using normalized indirect pathways and outperforms heuristics and neural nets on Medicare shared-patient data in both within-period and cross-period settings.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Thermal facial videos yield breathing rate estimates with 3.1 bpm error and electrodermal activity correlation of 0.40 using region tracking and signal separation on a public driver dataset.
ProtoSiTex introduces dual-phase prototype learning with hierarchical consistency loss for semi-interpretable multi-label text classification on a new subsentence-annotated hotel review dataset.
citing papers explorer
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H3: A Healthcare Three-Hop Index for Physician Referral Network Prediction
H3 is a new three-hop index that predicts physician referrals using normalized indirect pathways and outperforms heuristics and neural nets on Medicare shared-patient data in both within-period and cross-period settings.
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Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring
Thermal facial videos yield breathing rate estimates with 3.1 bpm error and electrodermal activity correlation of 0.40 using region tracking and signal separation on a public driver dataset.
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ProtoSiTex: Learning Semi-Interpretable Prototypes for Multi-label Text Classification
ProtoSiTex introduces dual-phase prototype learning with hierarchical consistency loss for semi-interpretable multi-label text classification on a new subsentence-annotated hotel review dataset.