Background: Acute myocardial infarctions are deadly to patients and burdensome to healthcare systems. Most recorded infarctions are patients’ first, occur out of the hospital, and often are not accompanied by cardiac comorbidities.
Cristian Minoccheri +4 more
core +1 more source
Toward Efficient Automation of Interpretable Machine Learning
, 2018 Developing more efficient automated methods for interpretable machine learning (ML) is an important and longterm machine-learning goal. Recent studies show that unintelligible black box models, such as Deep Learning Neural Networks, often outperform ...
Kovalerchuk, Boris +3 more
core +1 more source
Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model [PDF]
PeerJBackground Tuberculous pleural effusion (TPE) is a prevalent tuberculosis complication, with diagnosis presenting considerable challenges. Timely and precise identification of TPE is vital for effective patient management and prognosis, yet existing ...
Bihua Yao +7 more
doaj +2 more sources
Interpretable Multiclass Models for Corporate Credit Rating Capable of Expressing Doubt
Frontiers in Applied Mathematics and Statistics, 2016 Corporate credit rating is a process to classify commercial enterprises based on their creditworthiness. Machine learning algorithms can construct classification models, but in general they do not tend to be 100% accurate.
Lennart Obermann, Stephan Waack
doaj +1 more source
Statistical Approaches for Interpretable Machine Learning
, 2023 New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to process, visualize, and analyze this big data in a wide range of high-stakes ...
Gan, Luqin
core
Improved phrase-based SMT with syntactic reordering patterns learned from lattice scoring [PDF]
, 2010 In this paper, we present a novel approach to incorporate source-side syntactic reordering patterns into phrase-based SMT. The main contribution of this work is to use the lattice scoring approach to exploit and utilize reordering information that is ...
Jie Jiang +5 more
core
Towards an explainable machine learning model to reduce readmission risks for diabetes patients
Informatics in Medicine UnlockedObjective:: Hospital readmission of Diabetes patients is a persistent burden on the healthcare industry. Artificial Intelligence (AI) based Machine Learning (ML) techniques offer the potential to predict readmission rates and related risk features for ...
Changfeng Guo +5 more
doaj +1 more source
Interpretable Machine Learning And Applications
, 2022 Deep neural networks (DNNs) has attracted much attention in machine learning community due to its state-of-the-art performance on various tasks.
Pan, Deng
core
On Leveraging Machine Learning in Sport Science in the Hypothetico-deductive Framework
Sports Medicine - OpenSupervised machine learning (ML) offers an exciting suite of algorithms that could benefit research in sport science. In principle, supervised ML approaches were designed for pure prediction, as opposed to explanation, leading to a rise in powerful, but ...
Jordan Rodu +3 more
doaj +1 more source
Monitoring the Centennial Variation of Heavy Metals in Lake Sediments and Influencing Factors Using Environmental Magnetism and Machine Learning Methods [PDF]
E3S Web of ConferencesThe association between the magnetic properties of lake sediments and heavy metal(loid)s (HMs) is well-documented; however, their correlation with the chemical fractions of HMs remains under-investigated.
Deng Ligang, Li Huiming, Qian Xin
doaj +1 more source