Results 151 to 160 of about 66,762 (195)

Identifying early key influencing factors of positive results in the early screening for postpartum depression with interpretable machine learning. [PDF]

Front Public Health
Lin S, Wang Y, Han X, Wang X, Zhang Y, Xu W, Chen M, Xu X, Li H.   +8 more
europepmc   +1 more source

Helix 1.0: An open-source framework for reproducible and interpretable machine learning on tabular scientific data. [PDF]

Patterns (N Y)
Aguilar-Bejarano E, Lea D, Sivakumar K, Mase JM, Omidvar R, Li R, Kettle T, Mitchell-White J, Alexander MR, Winkler DA, Figueredo G.   +10 more
europepmc   +1 more source

Development and interpretable machine learning models for classification of pancreatic pseudocyst risk in acute pancreatitis. [PDF]

Front Digit Health
Feng H, Wang P, He W, Shang L, Cui M, Wang K, An K, Zhang Y.   +7 more
europepmc   +1 more source

Automated Detection of Carotid Artery Stenosis Using a Sensitive Accelerometer Wearable Sensor and Interpretable Machine Learning. [PDF]

Biosensors (Basel)
Majditehran H, Sang B, Desai N, Nahab F, Kvantaliani N, Blanke D, Starnes D, Christopher H, Park JW, Ayazi F.   +9 more
europepmc   +1 more source

An interpretable machine learning-based approach: development, validation, and clinical utility for distant metastasis prediction in PTC. [PDF]

Front Endocrinol (Lausanne)
Sun R, Ma Y, Jiang Y, Li X.
europepmc   +1 more source

Early prediction of incident delirium in traumatic brain injury: a multicenter validated and interpretable machine learning approach. [PDF]

Front Neurol
Li C, Zhang T, Chen H, Wang S, Wang L, Huan Y, Liu J, Liu L.   +7 more
europepmc   +1 more source

Interpretable Machine Learning-Based Concentric Regional Analysis of OCTA Images for Enhanced Diabetic Retinopathy Detection. [PDF]

Bioengineering (Basel)
Osman SM, Alksas A, Balaha HM, Mahmoud A, Gamal A, Abdel-Hady ME, Abdelsalam MM, Khalil AT, Sewelam A, El-Baz A.   +9 more
europepmc   +1 more source

Interpretable machine learning with reject option

at - Automatisierungstechnik, 2018
Abstract Classification by means of machine learning models constitutes one relevant technology in process automation and predictive maintenance. However, common techniques such as deep networks or random forests suffer from their black box characteristics and possible adversarial examples.
Brinkrolf, Johannes, Hammer, Barbara
openaire   +3 more sources