Review of Interpretable Machine Learning Models for Disease Prognosis
In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decision making.
Shen, Jinzhi, Ma, Ke
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Revealing GRK5 Activation Features by Interpretable Machine Learning and Molecular Dynamics Simulation. [PDF]
Int J Mol SciSong Y, Kong M, Zhang F, Pu X.
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Interpretable and explainable machine learning via optimisation
As machine learning becomes increasingly embedded in decision-making processes across science, industry, and public services, the need for interpretable and explainable models has grown.
Liapis, Georgios
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Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions. [PDF]
Health Care Manag SciBohlen L +6 more
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Anomaly detection of fermi surface morphology in Co2MnGaxGe1-x via interpretable machine learning. [PDF]
Sci RepIshikawa D +10 more
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Interpretable machine learning for in-hospital mortality prediction in ICU patients with traumatic brain injury. [PDF]
Front NeurolLiu N, Li C.
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Interpretable Machine Learning for Feature-Based Classification of Platelet Activation in Rotary Blood Pumps. [PDF]
Cardiovasc Eng TechnolBlum C, Neidlin M.
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Retraction: Interpretable machine learning framework for predicting Urban air quality. [PDF]
PLoS OnePLOS One Editors.
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Interpretable machine learning-based real-time sepsis diagnosis. [PDF]
Sci RepMahmud F +4 more
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Development and external validation of an interpretable machine learning model for obesity-depression comorbidity in Korean and US adults. [PDF]
Int J Public HealthShangguan Y +9 more
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