Filters








336 Hits in 11.1 sec

The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System [article]

Der-Chen Chang, Ophir Frieder, Chi-Feng Hung, Hao-Ren Yao
2021 arXiv   pre-print
Distance metrics and their nonlinear variant play a crucial role in machine learning based real-world problem solving.  ...  We demonstrated how Euclidean and cosine distance measures differ not only theoretically but also in real-world medical application, namely, outcome prediction of drug prescription.  ...  Acknowledgments The author is grateful to the reviewers for useful suggestions which improved the contents of this paper.  ... 
arXiv:2102.02446v2 fatcat:ifksqjlmmbfllalhj4qwhknup4

The analysis from nonlinear distance metric to kernel-based prescription prediction system

2021 Journal of Nonlinear and Variational Analysis  
Recently, a distance-derived graph kernel approach was commercially licensed for drug prescription efficacy prediction.  ...  Specifically, within the domain of drug efficacy prediction, distance measures must account for time that varies based on disease duration, short to chronic.  ...  Acknowledgments The authors are grateful to the reviewers for useful suggestions which improved the contents of this paper.  ... 
doi:10.23952/jnva.5.2021.2.01 fatcat:klmhbcq4zneebofglbscxn2hmu

Towards for Designing Intelligent Health Care System Based on Machine Learning

Nada Noori, Ali Yassin
2021 Iraqi Journal for Electrical And Electronic Engineering  
In this paper, we propose an intelligent health care system based on ML methods as a real-time monitoring system to detect diabetes mellitus and examine other health issues such as food and drug allergies  ...  In this regard, machine learning (ML) techniques show promising results in using medical data to predict diabetes at an early stage to save people's lives.  ...  Step 3: The report is sent to the doctor for final treatment based on the following:  Upon receiving the patient report, the doctor (di) writes the prescription.  Our system checks whether the drug prescribed  ... 
doi:10.37917/ijeee.17.2.14 fatcat:y2cp3ezznrcydlzve6qfeuppmm

From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic

Risto Miikkulainen, Olivier Francon, Elliot Meyerson, Xin Qiu, Darren Sargent, Elisa Canzani, Babak Hodjat
2021 IEEE Transactions on Evolutionary Computation  
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with nonpharmaceutical interventions, such as social distancing restrictions and school and  ...  Through evolutionary surrogate-assisted prescription, it is possible to generate a large number of candidate strategies and evaluate them with predictive models.  ...  that best matches their preferences. step, i.e., to extend the models from prediction to prescription.  ... 
doi:10.1109/tevc.2021.3063217 fatcat:kz2jqi24fbghpdxthjqpm3azmm

A Drug Administration Decision Support System

Wenqi You, Alena Simalatsar, Nicolas Widmer, Giovanni De Micheli
2012 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops  
The system is based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is  ...  This paper proposes a Drug Administration Decision Support System (DADSS) to help clinicians/patients with the initial dose computing.  ...  ACKNOWLEDGMENT The authors would like to thank Carlotta Guiducci from EPFL for the help in manuscript revision and T. Buclin and V.  ... 
doi:10.1109/bibmw.2012.6470292 dblp:conf/bibm/YouSWM12 fatcat:whlueaodiva43fngoyw2rpbx6y

A supervised machine learning approach to generate the auto rule for clinical decision support system

Sanjib Raj Pandey, Jixin Ma, Choi-Hong Lai, Prakash Raj Regmi
2020 Trends in Medicine  
 Supervised learning, which trains a model on known inputs and output data to predict future outputs  Unsupervised learning, which finds hidden patterns or intrinsic structures in the input data  Semi-supervised  ...  Practices  383,592 patients free from CVD registered 1 st of January 2005 followed up for years  Two-fold cross validation (similar to other epidemiological studies): n = 295,267 "training set"; n =  ...  on the classes of their nearest neighbours in the dataset • Assume that objects near each other are similar • Distance metrics used to determine nearness (e.g.  ... 
doi:10.15761/tim.1000232 fatcat:yeph7fvwr5bf5helhtelrf4i64

From Prediction to Prescription: Evolutionary Optimization of Non-Pharmaceutical Interventions in the COVID-19 Pandemic [article]

Risto Miikkulainen, Olivier Francon, Elliot Meyerson, Xin Qiu, Elisa Canzani, Babak Hodjat
2020 arXiv   pre-print
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school  ...  Through evolutionary surrogate-assisted prescription (ESP), it is possible to generate a large number of candidate strategies and evaluate them with predictive models.  ...  In contrast to epidemiological models that make predictions based on today's state only, this datadriven model predicts based on data from the preceding three weeks.  ... 
arXiv:2005.13766v3 fatcat:yfuwti4p65gcxib5le4fvl35pq

A Predicted Chemo-Polypharmacophoric Agent Comprising (Propeptide-Fc)/Mgf Peptide Mimicking Interactive Of High Free Binding Energy Properties Towards Wnt7A/Fzd7 Signalling Akt/Mtor Anabolic Growth Igf-I/Pi3K/Akt -I/Mapk/Erk Pathways

Ioannis Grigoriadis
2015 Zenodo  
Ligands selected from phage-displayed random peptide libraries tend to be directed to biologically relevant sites on the surface of the target protein.  ...  Consequently, peptides derived from library screenings often modulate the target protein's activity in vitro and in vivo and can be used as lead compounds in drug design and as alternatives to antibodies  ...  Drugs that are not connected, directly or indirectly, to the seed drugs are not included in our analysis.  ... 
doi:10.5281/zenodo.31276 fatcat:toytdjsdtja75hwvobxw6nsiiq

An In Silico Chemoproteomic Prediction-Scan For The Generation Of A Tyrosinase Aa95-104Fmgfncgnck Antigenic Patternlfa-3/Igg Fusion Polypeptide Ilealaargargpheleuoh (Kinetensin) Mimetic Pharmacophore On Conserved Vitiligo Post-Trancripts Domains

Ioannis Grigoriadis
2015 Zenodo  
Fragment-based lead discovery is a method used for finding lead compounds as part of the drug discovery proc [...]  ...  It has been perviously reported that the tyrosinase autoantigen was immunorecognized with the same molecular pattern by sera from vitiligo and melanoma patients.  ...  ACKNOWLEDGMENTS I Grigoriadis the author, would like to thank my brother, Dr.  ... 
doi:10.5281/zenodo.31284 fatcat:zcqxuxbesbgjhmsq6ug5us4jgi

A Mechanistic In Silico Molecular Recognized Approach For The Ligand Based Generation Of A Dual N-Formyl-Met-Leu-Phe (Fmlp), And Mmk-1Peptide Mimetic Hyper-Agonist Fmlp Targeted Receptor Against The Pge2 Ep4 Pathway Chemotherapy-Induced Alopecia

Ioannis Grigoriadis
2015 Zenodo  
to design improved lead molecules for the [...]  ...  It has been shown that the Oral administration for 6 days of 100 mg/kg MMK-1, of an agonist peptide selective for the FPRL1 receptor, suppressed alopecia induced by the anticancer drug etoposide in neonatal  ...  ACKNOWLEDGMENTS I Grigoriadis the author, would like to thank my brother, Dr.  ... 
doi:10.5281/zenodo.31283 fatcat:hja6yfz2w5ejdpj3cfonctykc4

Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs

Jiajia Liu, Zhihui Zhou, Shanshan Kong, Zezhong Ma
2022 Frontiers in Oncology  
a most suitable algorithm to predict the IC50 and pIC50 values.  ...  The optimization of drug properties in the process of cancer drug development is very important to save research and development time and cost.  ...  error events from drug prescriptions.  ... 
doi:10.3389/fonc.2022.956705 pmid:35936743 pmcid:PMC9353770 fatcat:dqk2jjx2n5bpzcvm46e2zrrvm4

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Frederic E. Bock, Roland C. Aydin, Christian J. Cyron, Norbert Huber, Surya R. Kalidindi, Benjamin Klusemann
2019 Frontiers in Materials  
They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential  ...  Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining.  ...  The extracted kernels were insensitive to details of the initial microstructure, enabling the application of the kernel to any initial microstructure within the material system selected for that kernel  ... 
doi:10.3389/fmats.2019.00110 fatcat:ks755u6g2rae3jokc7nwxghunu

A practical approach to Hohenberg-Kohn maps based on many-body correlations: learning the electronic density [article]

Edgar Josué Landinez Borda, Amit Samanta
2020 arXiv   pre-print
Commun, 8, 872 (2017)) and involves two training models: one, to predict the ground state charge density, ρ(r), directly from the atomic structure, and another to predict the total energy from ρ(r).  ...  To predict ρ(r), we use many-body correlation descriptors to accurately describe the neighborhood of a grid point and to predict the total energy we use amplitudes of these many-body correlation descriptors  ...  Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request.  ... 
arXiv:2004.14442v2 fatcat:s6pmfiqrvram3bchdtj2kroe5y

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Changbo Zhao, Guo-Zheng Li, Chengjun Wang, Jinling Niu
2015 Evidence-Based Complementary and Alternative Medicine  
Patient classification is to divide patients into several classes based on different criteria.  ...  given to facilitate the further research for TCM patient classification.  ...  Acknowledgments This work was supported by the Natural Science Foundation of China under Grants nos. 61105053 and 61273305 as well as the Fundamental Research Funds for the Central Universities.  ... 
doi:10.1155/2015/376716 pmid:26246834 pmcid:PMC4515265 fatcat:tkr3oyc5tbhthasaej2neeyjf4

Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)

Robert A. Sowah, Marcellinus Kuuboore, Abdul Ofoli, Samuel Kwofie, Louis Asiedu, Koudjo M. Koumadi, Kwaku O. Apeadu
2019 Journal of Engineering  
The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated  ...  The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers.  ...  organizing Write Shops that led to the timely completion of this paper.  ... 
doi:10.1155/2019/1432597 fatcat:oclpqwn6pnaalprywotasmhejq
« Previous Showing results 1 — 15 out of 336 results