Increased utilization of
antibiotics prior to the diagnosis of pediatric Crohn’s disease and
ulcerative colitis compared with matched controls: A nationwide study from the
epi-IIRN
Friss, C.,
Lujan, R., Greenfeld, S., Kariv, R., Loewenberg-Weisband,
Y., Ledderman, N., Matz, E., Turner, D., Lerner, B.
The 55th European Society for Paediatric Gastroenterology, Hepatology and Nutrition Annual Meeting (ESPGHAN), Vienna, May, 2023.
Mann, H., Bar
Hillel, A., Lev-Tzion,
R., Greenfeld,
S., Kariv,
R., Lederman, N., Matz, E., Dotan, I., Turner, D., and Lerner, B.
Artificial
Intelligence in Medicine,
145, 102684. 2023
Empowering interpretable, explainable machine learning using Bayesian network classifiers
B Lerner
In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-24628-9_7, 2023
Increased utilization of antibiotics prior to the diagnosis of pediatric Crohn’s disease and ulcerative colitis compared with matched controls: A nationwide study from the epi-IIRNC Friss, R Lujan, S Greenfeld, R Kariv, Y Loewenberg-Weisband, N Ledderman, E Matz, D Turner, B Lerner55th European Society for Paediatric Gastroenterology, Hepatology and Nutrition Annual Meeting (ESPGHAN), Vienna, Austria, May, 2023
Predicting pediatric IBD years before diagnosis using routine blood tests R Lev-Tzion, AS Dolev, S Greenfeld, R Kariv, N Ledderman, E Matz, I Dotan, U Kopylov, D Turner, B Lerner55th European Society for Paediatric Gastroenterology, Hepatology and Nutrition Annual Meeting (ESPGHAN), Vienna, Austria, May, 2023
Predicting IBD years before diagnosis using routine blood tests R Lev-Tzion, AS Dolev, S Greenfeld, R Kariv, N Ledderman, E Matz, I Dotan, U Kopylov, D Turner, B Lerner18th Congress of European Crohn's and Colitis Organisation (ECCO), Copenhagen, Denmark, March 2023
Predicting pediatric IBD years before diagnosis using routine blood testsR Lev-Tzion, AS Dolev, S Greenfeld, R Kariv, N Ledderman, E Matz, I Dotan, D Turner, B Lerner6th International Symposium on Paediatric Inflammatory Bowel Disease, Edinburgh, Scotland, September 2022
Engaging patients in identifying risk factors for ALS AE Raz, I Schneid, E Carmi, O Kedem, and B LernerSSM - Qualitative Research in Health, 2022
Levodopa responsiveness in Parkinson’s disease – Harnessing real-life experience with machine-learning analysis R Djaldetti, B Hadad, J Reiner, B Ashkenazi Kharash, and B Lerner Journal of Neural Transmission, 2022Mapping of social functions in a smart city when considering sparse knowledge O Zinman and B LernerUbiquitous and Pervasive Computing - New Trends and Opportunities, IntechOpen, 2022
SIGN: Statistical inference graphs based on probabilistic network activity interpretation
Y Konforti, A Shpigler, B Lerner, and A Bar Hillel IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3783-3797, 2022Lipid level alteration in human and cellular models of alpha synuclein mutations
H Avisar, C Guardia-Laguarta, M Surface, N Papagiannakis, M Maniati, R Antonellou, D Papadimitriou, C Koros, A Athanassiadou,S Przedborski, B Lerner, L Stefanis, E Area-Gomez, and RN Alcalaynpj Parkinson's Disease, 52, 2022
Lipidomics prediction of Parkinson’s disease severity: A machine-learning analysis
H Avisar, C Guardia-Laguarta, E Area-Gomez, M Surface, AK Chan, RN Alcalay, and B LernerJournal of Parkinson's Disease, 11, 1141-1155, 2021
A machine-learning model for automatic
detection of movement compensations in stroke patients
S Kashi, R Feingold-Polak, B Lerner, L Rokach, and S Levy-Tzedek
IEEE
Transactions on Emerging Topics in Computing, 9, 1234-1247, 2021
Domain adaptation from clinical trials data to the tertiary care clinic – Application to ALS
B Hadad and B Lerner
19th International Conference on Machine Learning and Applications (ICMLA 2020), Miami, FL., December, 2020
Online cluster drift detection for novelty detection in data streams
S Mendelson and B Lerner
19th International Conference on Machine Learning and Applications (ICMLA 2020), Miami, FL., December, 2020
Inference graphs for CNN
interpretation
Y Konforti, A Shpigler, B Lerner, and A Bar Hillel.
16th European Conference on Computer Vision (ECCV), August 2020
Transfer learning of photometric phenotypes in agriculture using metadata
D Halbersberg, A Bar Hillel, S Mendelson, D Koster, L Karol, and B Lerner
Computer Vision for Global Challenges (CV4GC) Workshop in
8th International Conference on Learning Representation (ICLR 2020), Addis Ababa, Ethiopia, April 2020 https://arxiv.org/abs/2004.00303
D Halbersberg, M Wienreb, and B Lerner
Machine Learning, 109, 1039-1099, 2020
(available
code and data sets)
Local to global learning of a latent dynamic Bayesian network
D Halbersberg and B Lerner
24th European Conference on Artificial Intelligence (ECAI 2020), Santiago de Compostela, Spain, 29 August–8 September, 2020
Utilizing
digital traces of mobile phones for understanding social dynamics in urban
areas
O Zinman and B Lerner
Personal
and Ubiquitous Computing, vol. 24, pp. 535-549, 2020
Temporal modeling of deterioration
patterns and clustering for disease prediction of ALS patients
D Halbersberg and B Lerner
18th International Conference on Machine
Learning and Applications (ICMLA 2019), Boca Raton,
Florida, USA, December 16-19, 2019
Insights into ALS from a machine
learning perspective
J Gordon and B Lerner
Journal of Clinical Medicine, vol. 8(10), pp. 1578, 2019
Young driver fatal motorcycle accident analysis by jointly maximizing accuracy and information
D Halbersberg and B Lerner
Accident
Analysis and Prevention, vol. 129, pp. 350-361, 2019
(available
code and data sets)
Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU
G Hever, L Cohen, MF O’Connor, I Matot, B Lerner, and Y Bitan
Journal
of Clinical Monitoring and Computing, 2019
https://doi.org/10.1007/s10877-019-00307-x
Identifying and predicting social lifestyles in people's trajectories by neural networks
E Ben Zion and
B Lerner
EPJ Data Science, vol. 7(45), pp. 1-27, 2018
(available
code and data sets)
Analyzing
large-scale human mobility data: A survey of machine learning methods and
applications
E Toch, B
Lerner, E Ben-Zion, and I Ben-Gal
Knowledge
and Information System,
pp.1–23, March 2018
Temporal modeling of ALS using longitudinal data and long-short term memory-based algorithm
A Nahon and B Lerner
European Symposium on Artificial Networks, Computational Intelligence and Machine Learning (ESANN2018), Bruges, Belgium, 2018
Stratifying ALS patients by disease progression patterns
J Gordon and B Lerner
28th International Symposium on ALS/MND, Boston, USA, 2017
Modeling of ALS progression using a temporal
machine-learning algorithm
J
Gordon, A Nahon, and B Lerner
28th International Symposium on ALS/MND, Boston, USA, 2017
Dynamic weighting of old and new information
for predicting future condition of ALS patients
A
Nahon and B Lerner
28th International Symposium on ALS/MND, Boston, USA, 2017
Learning
human behaviors and lifestyle by capturing temporal relations in mobility
patterns
E Ben Zion and B Lerner
European Symposium on Artificial Networks,
Computational Intelligence and Machine Learning (ESANN2017), Bruges, Belgium, 2017
Learning latent variable models
by pairwise cluster
comparison: Part I − Theory and
overview
N Asbeh and B Lerner
Journal of Machine Learning Research (JMLR), vol. 17(224), pp. 1–52, 2016
(available
code and data sets)
Learning latent variable models
by pairwise cluster
comparison: Part II
− Algorithm
and evaluation
N Asbeh and B Lerner
Journal of Machine Learning Research (JMLR), vol. 17(233), pp. 1–45, 2016
(available
code and data sets)
Human
mobility-pattern discovery and next-place prediction from GPS data
F Khoroshevsky and B Lerner
In: Schwenker F., Scherer S.
(eds) (2017) Multimodal Pattern Recognition of Social Signals in
Human-Computer-Interaction. MPRSS 2016. Lecture Notes in Computer
Science, vol
10183. Springer, Cham
(23rd International Conference on Pattern Recognition, ICPR,
Cancun, Mexico, 2016. The 4th International Workshop on Multimodal
Pattern Recognition of Social Signals in Human Computer Interaction, MPRSS)
Exposing and modeling underlying mechanisms in ALS
with machine learning
J Gordon and B Lerner
23rd International Conference on Pattern
Recognition (ICPR), Cancun, Mexico, 2016
Learning
a Bayesian network classifier by jointly maximizing accuracy and information
D
Halbersberg and B Lerner
22nd European Conference on Artificial
Intelligence (ECAI), The Hague, Holland, 2016
Disease state prediction, knowledge representation, and heterogeneity decomposition for ALS
J Gordon and B Lerner
Uncertainty in Artificial Intelligence (UAI) Workshop on Machine Learning for Health: Learning to Understand Human Disease, Jersey City, NJ, 2016.
Insights
into amyotrophic lateral sclerosis from a machine learning perspective
J Gordon and B Lerner
14th European Network for the Cure of ALS (ENCALS) Meeting, Milan, Italy, 2016
Pairwise cluster comparison for learning latent variable models
N Asbeh and B Lerner
Uncertainty in Artificial Intelligence (UAI) Workshop on Causation: Foundation to Application, Jersey City, NJ, 2016.
Adaptive
thresholding in structure learning of a Bayesian network
B Lerner, M Afek and R Bojmel
23rd International Joint Conference on Artificial Intelligence (IJCAI2013), Beijing, pp. 1458-1464, 2013.
(available
code and data sets)
Learning latent
variable models by pairwise cluster comparison
N Asbeh and B Lerner
Fourth Asian Conference on Machine Learning (ACML2012), Singapore
JMLR Workshop & Conference
Proceedings, vol. 25, pp.
33-48, 2012. (available
code)
Learning
Bayesian network classifiers by risk minimization
R Kelner and B Lerner
International Journal of
Approximate Reasoning, vol. 53, pp. 248-272, 2012.
Machine learning in predicting and explaining failure using class-imbalance FAB data
H Belyavin and B Lerner
21st
Inter. Conf. on Production Research (ICPR21), Stuttgart, Germany,
2011.
Trading
between classification accuracy and information in production
M
Wienreb, B Lerner and G Rabinowitz
21st
Inter. Conf. on Production Research (ICPR21), Stuttgart, Germany,
2011.
Structure-based
identification of catalytic residues
R
Yahalom, D Reshef, A Wiener, S Frankel, N Kalisman, B
Lerner and C Keasar
Proteins: Structure, Function, and Bioinformatics, vol. 79, pp. 1952-1963, 2011.
Y
Meidan, B Lerner, G Rabinowitz and M Hassoun
IEEE Transactions on Semiconductor Manufacturing, vol. 24, pp. 237-248, 2011.
Investigation of
the K2 algorithm in learning Bayesian network classifiers
B Lerner and R Malka
Applied Artificial
Intelligence,
vol. 25, pp. 74-96, 2011.
Bayesian
Network Structure Learning by Recursive Autonomy Identification
R Yehezkel and B Lerner
The Journal of Machine
Learning Research,
vol. 10, pp. 1527-1570, 2009.
Advanced
Developments and Applications of the Fuzzy ARTMAP
Neural Network in Pattern Classification
B Lerner and H Guterman
Computational Intelligence
Paradigms - Innovative Applications, (eds.) L C Jain and Sato, Springer-Verlag, pp. 77-107,
2008.
B Vigdor and B Lerner
IEEE Transactions on Neural
Networks,
vol. 18(6), pp. 1628-1644, 2007.
Localization and
Magnetic Moment Estimation of a Ferromagnetic Target by Simmulated
Annealing
A Sheinker,
B Lerner, N Salomonski, B Ginzburg, L Frumkis and BZ Kaplan
Measurement Science and
Technology,
vol. 18, pp. 3451-3457, 2007.
B Lerner, L Koushnir and J Yeshaya
IEEE Transactions on
Information Technology in Biomedicine, vol. 11(4), pp. 443-449, 2007.
On the
Classification of a Small Imbalanced Cytogenetic Image Database
B Lerner, J Yeshaya and L Koushnir
IEEE Transactions on
Computational Biology & Bioinformatics, vol. 4(2), pp. 204-215, 2007.
Bayesian Class-Matched
Multinet Classifier
Y Gurwicz and B Lerner
SSPR/SPR, ser. Lecture Notes in
Computer Science,
vol. 4109, pp. 145-153, 2006.
Bayesian Network
Structure Learning by Recursive Autonomy Identification
R Yehezkel and B Lerner
SSPR/SPR, ser. Lecture Notes in
Computer Science,
vol. 4109, pp. 154-162, 2006.
Learning Bayesian
Networks for Pattern Classification
B Lerner
Invited tutorial at the 18th
International Conference on Pattern Recognition (ICPR2006), Hong-Kong,
August 2006.
B Vigdor and B Lerner
IEEE Transactions on Neural
Networks,
vol. 17(5), pp. 1288-1300, 2006.
Bayesian
Network Classification using Spline-Approximated Kernel Density Estimation
Y Gurwicz and B Lerner
Pattern Recognition Letters, vol. 26(11), pp. 1761-1771,
2005.
Recursive
Autonomy Identification for Bayesian Network Structure Learning
R Yehezkel and B Lerner
The 10th International
Workshop on Artificial Intelligence & Statistics, AISTATS
2005, 6-8
January, 2005, Barbados, pages 429-436.
Support Vector
Machine-based Image Classification for Genetic Syndrome Diagnosis
A David and B Lerner
Pattern Recognition Letters, vol. 26(8), pp. 1029-1038,
2005.
Classification
of Fluorescence In-Situ Hybridization Images using Belief Networks
R Malka and B Lerner
Pattern Recognition Letters, vol. 25(16), pp. 1777-1785,
2004.
Bayesian Fluorescence
in-situ Hybridization Signal Classification
B Lerner
Artificial Intelligence in
Medicine,
vol. 30(3), A special issue on Bayesian Models in Medicine, pp. 301-316, 2004.
Signal
Discrimination Using a Support Vector Machine for Genetic Syndrome Diagnosis
A David and B Lerner
17th International Conference
on Pattern Recognition
(ICPR2004), 23-26 August, 2004, Cambridge, UK, Vol. 3, pp. 490-493.
Rapid Spline-based
Kernel Density Estimation for Bayesian Networks
Y Gurwicz and B Lerner
17th International Conference
on Pattern Recognition
(ICPR2004), 23-26 August, 2004, Cambridge, UK, Vol. 3, pp. 700-703.
An Empirical Study
of Fuzzy ARTMAP Applied to Cytogenetics
B Lerner and B Vigdor
23rd IEEE Convention of
Electrical & Electronics Engineers in Israel, 6-7 September, 2004 (IEEEI2004),
Tel-Aviv, Israel, pp. 301-304.
Belief Networks for
Cytogenetic Image Categorization
B Lerner and R Malka
23rd IEEE Convention of
Electrical & Electronics Engineers in Israel, 6-7 September, 2004 (IEEEI2004),
Tel-Aviv, Israel, pp. 297-300.
Fluorescence In-Situ
Hybridization Signal Discrimination in Medical Genetics
B Lerner
NNESMED 2003/CIMED
2003,
Sheffield, pp. 29-34, 2003.
Introduction
to Learning Probabilistic Graphical Models
B Lerner
Invited tutorial at Computational
Intelligence: Methods & Applications (CIMA'2001), ICSC
Academic Press, NL, 2001.
A Comparison of
State-of-the-Art Classification Techniques with Application to Cytogenetics
B Lerner and N D Lawrence
Neural Computing &
Applications,
vol. 10(1), pp. 39-47, 2001.
B Lerner, W F Clocksin, S
Dhanjal, M A Hult'en and C M Bishop
IEEE Transactions on Systems,
Man and Cybernetics, Part A: Systems & Humans, vol. 31(6), pp. 655-665,
2001.
Automatic Signal
Classification in Fluorescence in-situ Hybridization Images
B Lerner, W F Clocksin, S
Dhanjal, M A Hult'en and C M Bishop
Cytometry, vol. 43(2), pp. 87-93,
2001.
GELFISH
- Graphical Environment for Labelling Fluorescence in-situ Hybridization Images
B Lerner, S Dhanjal and M A Hult'en
Journal of Microscopy, vol. 203(3), pp. 258-268,
2001.
On the Initialisation of Sammon's Nonlinear Mapping
B Lerner, H Guterman, M Aladjem and I Dinstein
Pattern Analysis and
Applications,
3(1), 61-68, 2000.
A Comparative Study
of Neural Network based Feature Extraction Paradigms
B Lerner, H Guterman, M Aladjem and I Dinstein
Pattern Recognition Letters, vol. 20(1), pp. 7-14, 1999.
On Pattern
Classification with Sammon's Nonlinear Mapping - An Experimental Study
B Lerner, H Guterman, M Aladjem, I Dinstein and Y Romem
Pattern Recognition, vol. 31(4), pp. 371-381,
1998.
Toward a Completely
Automatic Neural Network based Human Chromosome Analysis
B Lerner
IEEE Transactions on Systems,
Man and Cybernetics. Special issue on Artificial Neural Networks, vol.
28(4), Part B, pp. 544-552, 1998.
B Lerner, H Guterman and I Dinstein
IEEE Transactions on Signal
Processing,
vol. 46(10), pp. 2841-2847, 1998.
Feature Extraction by
Neural Network Nonlinear Mapping for Pattern Classification
B Lerner, H Guterman, M Aladjem, I Dinstein and Y Romem
The 13th International
Conference on Pattern Recognition, ICPR13, Vienna, vol. 4, pp 320-324, 1996.
Human Chromosome
Classification using Multilayer Perceptron Neural Network
B Lerner, H Guterman, I Dinstein and Y Romem
International Journal of
Neural Systems,
vol. 6(3), pp 359-370, 1995.
Medial Axis Transform based Features and a
Neural Network for Human Chromosome Classification
B Lerner, H Guterman, I Dinstein and Y Romem
Pattern Recognition, vol. 28(11), pp 1673-1683,
1995.
B Lerner, H Guterman, M Aladjem and I Dinstein
The International Conference
on Neural Information Processing, ICONIP95, Beijing, vol. 1, pp 279-284, 1995.
Feature Selection and
Learning Curves of a Multilayer Perceptron Chromosome Classifier
B Lerner, H Guterman, I Dinstein and Y Romem
The 12th International
Conference on Pattern Recognition, 12ICPR, Jerusalem, vol. 2, pp 497-499, 1994.
Medial Axis Transform based Features
and a Neural Network for Human Chromosome Classification
B Lerner, B Rosenberg, M
Levinstein, H Guterman, I Dinstein and Y Romem
World Congress on Neural
Networks, WCNN94,
San Diego, vol. 3, pp 173-178, 1994.
Feature Selection and
Chromosome Classification using a Multilayer Perceptron Neural Network
B Lerner, M Levinstein, B
Rosenberg, H Guterman, I Dinstein and Y Romem
World Congress on
Computational Intelligence, WCCI'94, Orlando, vol. 6, pp 3540-3545, 1994.
B Lerner, H Guterman, I Dinstein and Y Romem
World Congress on
Computational Intelligence, WCCI'94, Orlando, vol. 6, pp 3472-3477, 1994.
B Lerner, H Guterman, I Dinstein and Y Romem
World Congress on Neural
Networks, WCCN'94,
San Diego, vol. 3, pp 248-253, 1994.
`Tailored' Neural Networks to Improve
Image Classification
B Lerner, H Guterman, I Dinstein and Y Romem
World Congress on Neural
Networks, WCNN94,
San Diego, vol. 4. pp 327-331, 1994.
Classification of Human
Chromosomes by Two-Dimensional Fourier Transform Components
B Lerner, H Guterman and I Dinstein
World Congress on Neural
Networks, WCNN93,
Portland, vol. 3, pp 793-796, 1993.
Multilayer Perceptron as a
Human Chromosome Classifier
B Lerner, B Rosenberg, M
Levinstein, H Guterman, I Dinstein and Y Romem
Artificial Intelligence,
Computer Vision and Neural Networks, AICVNN'93, Kfar-Maccabiah, 207-216,
1993.
On Classification of Human
Chromosomes
B Lerner, H Guterman and I Dinstein
Neural Networks for Learning,
Recognition and Control,
Boston University, 1992.
Two-Frequency Intensity
Fluctuations in a Random Medium
B Lerner, M Tur and Z Azar
SPIE, vol. 1038, 569-576,
Tel-Aviv, 1988.
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This page is
maintained by Boaz Lerner (boaz@bgu.ac.il)