Associate
professor, Faculty of Information Technology,
University of Jyväskylä & Central Finland Wellbeing Services County, Finland
PhD
(mathematical information technology), MSc (sport sciences)
Head of
Digital Health Intelligence Laboratory at Faculty
of Information Technology, University of
Jyväskylä, Finland
email:
sami.ayramo at jyu.fi
Profile in
LinkedIn
· Mausehund L, Patron A, Äyrämö S & Krosshaug, T. (2024). 475 FO25 – Cluster
analysis of cutting technique: a valuable approach for assessing anterior
cruciate ligament injury risk? British Journal of Sports Medicine
2024;58:A13-A14.
· Niemelä, M., von Bonsdorff,
M., Äyrämö, S., & Kärkkäinen, T. (2024). Classification of dementia from
spoken speech using feature selection and the bag of acoustic words model. Applied
Computing and Intelligence, 4(1), 45-65.
· Niemelä M, von Bonsdorff M,
Äyrämö S, & Kärkkäinen T, (2024). Identification of Cognitive
Decline from Spoken Language through Feature Selection and the Bag of Acoustic
Words Model. arXiv preprint
arXiv:2402.01824.
· Prezja F, Äyrämö S, Pölönen I,
Ojala T, Lahtinen S, Ruusuvuori P & Kuopio T, (2023). Improved accuracy
in colorectal cancer tissue decomposition through refinement of established
deep learning solutions. Scientific Reports 13, 15879.
· Rautiainen, I., Parviainen,
L., Jakoaho, V., Äyrämö, S., & Kauppi, J. (2023). Utilizing the International
Classification of Functioning, Disability and Health (ICF) in forming a
personal health index. ArXiv,
abs/2304.06143.
· Petäinen L, Väyrynen JP, Ruusuvuori
P, Pölönen I, Äyrämö S, Kuopio T (2023) Domain-specific transfer
learning in the automated scoring of tumor-stroma ratio from histopathological
images of colorectal cancer. PLoS ONE 18(5):
e0286270.
· Taipalus, T., Isomöttönen,
V., Erkkilä, H., & Äyrämö, S. (2023). Data
Analytics in Healthcare: A Tertiary Study, SN Computer Science 4 (1), 1-14.
· Prezja,
F., Paloneva, J., Pölönen, I., Niinimäki, E., & Äyrämö, S. (2022). DeepFake
knee osteoarthritis X-rays from generative adversarial neural networks deceive
medical experts and offer augmentation potential to automatic classification.
Scientific Reports 12, 18573 (2022).
· Patron, A., Annala, L.,
Lainiala, O., Paloneva, J. & Äyrämö, S. (2022). Automatic Method for
Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis. Diagnostics
12, no. 11: 2603.
· Sukanen, M., Pajari, J.,
Äyrämö, S., Paloneva, J., Waller, B., Häkkinen, A. & Multanen, J. (2022). Cross-cultural adaptation and
validation of the Kerlan-Jobe Orthopaedic
Clinic shoulder and elbow score in Finnish-speaking overhead athletes. BMC
Sports Sci Med Rehabil 14, 190.
· Jauhiainen, S., Kauppi, JP., Krosshaug, T., Bahr, R., Bartsch, J.,
Äyrämö, S., (2022) Predicting
ACL Injury Using Machine Learning on Data From an Extensive Screening Test
Battery of 880 Female Elite Athletes. The
American Journal of Sports Medicine 50(11):2917-2924.
· Prezja,
F., Pölönen, I., Äyrämö, S., Ruusuvuori, P. & Kuopio, T. (2022). H&E Multi-Laboratory Staining
Variance Exploration with Machine Learning. Applied Sciences, 12,
7511.
· Niinimäki, E., Paloneva, J.,
Pölönen, I., Heinonen, A. & Äyrämö, S. (2022). Validation of Knee
KL-classifying Deep Neural Network with Finnish Patient Data. In: Tuovinen
T., Periaux J., Neittaanmäki P. (eds) Computational
Sciences and Artificial Intelligence in Industry. Intelligent Systems, Control
and Automation: Science and Engineering, vol 76. Springer, Cham.
· Rautiainen, I., Kauppi, J-P., Ruohonen, T., Karhu, E., Lukkarinen, K.
& Äyrämö, S. (2022) Predicting Future Overweight and Obesity from Childhood Growth
Data: A Case Study.
In: Tuovinen T., Periaux J., Neittaanmäki P. (eds) Computational
Sciences and Artificial Intelligence in Industry. Intelligent Systems, Control
and Automation: Science and Engineering, vol 76. Springer, Cham.
· Rautiainen I. & Äyrämö S. (2022) Predicting Overweight and
Obesity in Later Life from Childhood Data: A Review of Predictive Modeling
Approaches. In: Tuovinen T., Periaux J.,
Neittaanmäki P. (eds) Computational Sciences and Artificial Intelligence in
Industry. Intelligent Systems, Control and Automation: Science and Engineering,
vol 76. Springer, Cham.
·
Äyrämö,
S., & Paloneva, J. (2021). Tekoäly
siirtää kokeneiden lääkärien tietoa nuoremmille – polven nivelrikko
tunnistetaan aikaisemmin. Tiedeblogi. JYUnity – JYUMagazine, University of Jyväskylä. English
version.
· Niemelä, M., Äyrämö, S. &
Kärkkäinen, T. (2021). Toolbox for Distance
Estimation and Cluster Validation on Data with Missing Values, IEEE
Access, doi: 10.1109/ACCESS.2021.3136435.
· Niinimäki, E., Paloneva, J., Pölönen, I, Lainiala, O., & Äyrämö, S.
(2021) New
edge detection method for automated feature extraction from knee x-ray images.
ECCOMAS Thematic Conference on Computational Science and AI in Industry
(CSAI 2021). Abstract.
· Prezja, F., Kuopio, T., &
Äyrämö, S. (2021). Unsupervised
analysis of staining quality in histopathological images, ECCOMAS
Thematic Conference on Computational Science and AI in Industry (CSAI 2021).
Abstract.
· Nykänen,
V., Vasankari, N., Kokko, S., Paloniemi. R., Tapio., P., Tuominen, A.,
Järvinen, B., & Äyrämö, S. (2021). Clustering
association rules with an application to physical activity. ECCOMAS
Thematic Conference on Computational Science and AI in Industry (CSAI 2021).
Abstract.
· Jauhiainen, S., Krosshaug,
T., Petushek, E., Kauppi, J-P. & Äyrämö, S. (2021) Information
Extraction from Binary Skill Assessment Data with Machine Learning, International
Journal of Learning Analytics and Artificial Intelligence for Education,
3(1).
· Joensuu, L., Rautiainen, I.,
Äyrämö, S., Syväoja, H.J., Kauppi, J-P, Kujala, U., & Tammelin, T.H.
(2021). Precision
exercise medicine: predicting unfavourable status and
development in the 20-m shuttle run test performance in adolescence with
machine learning, BMJ Open Sport & Exercise Medicine 2021;7:e001053.
· Jauhiainen, S., Kauppi, J-P., Leppänen, M., Pasanen, K., Parkkari, J., Vasankari, T., Kannus, P., Äyrämö, S. (2021). New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes. International Journal of Sports Medicine, 42(02): 175-182.
· Leppänen, M., Parkkari, J.,
Vasankari, T., Äyrämö, S., Kulmala J-P, Krosshaug, T., Kannus, P., Pasanen, K.
(2021). Change of Direction Biomechanics in a
180-Degree Pivot Turn and the Risk for Noncontact Knee Injuries in Youth
Basketball and Floorball Players. Am
J Sports Med. 2021 Aug; 49(10):2651-2658.
· Rossi, M.K., Pasanen, K., Heinonen, A., Äyrämö, S., Leppänen, M.,
Myklebust, G., Vasankari, T., Kannus, P. & Parkkari, J., (2021) The standing knee lift test
is not a useful screening tool for time loss from low back pain in youth
basketball and floorball players, Physical Therapy in Sport, 49, 141-148.
· Annala, L., Äyrämö, S. & Pölönen, I. (2020) Comparison of Machine Learning
Methods in Stochastic Skin Optical Model Inversion. Applied Sciences, 10, 7097.
·
Girka, A., Kulmala, J-P., & Äyrämö, S. (2020). Deep learning approach for
prediction of impact peak appearance at ground reaction force signal of running
activity, Computer Methods in
Biomechanics and Biomedical Engineering, 23:14,
1052-1059.
·
Rossi, M.K., Pasanen, K., Heinonen,
A., Äyrämö, S., Räisänen, A. M., Leppänen, M., Myklebust, G, Vasankari, T.,
Kannus, P. & Parkkari, J. (2020), Performance in dynamic
movement tasks and occurrence of low back pain in youth floorball and
basketball players. BMC Musculoskelet Disord 21, 350.
· Jauhiainen, S., Pohl, A.J.,
Äyrämö, S., Kauppi, J-P, & Ferber, R. (2020). A hierarchical cluster analysis to
determine whether injured runners exhibit similar kinematic gait patterns. Scand J Med Sci Sports. 2020; 30: 732–
740.
· Rahkonen,
S., Koskinen, E., Pölönen, I., Heinonen, T., Ylikomi, T., Äyrämö, S.,
Eskelinen, M.A. (2020). Multilabel segmentation of
cancer cell culture on vascular structures with deep neural networks. Journal of Medical Imaging, 7 (2),
024001.
· Niemelä,
M., Kärkkäinen, T., Äyrämö, S., Ronimus, M., Richardson, U., & Lyytinen, H.
(2020). Game learning analytics for
understanding reading skills in transparent writing system. British
Journal of Educational Technology, 51(6), 2376-2390.
· Leppänen, M., Rossi, M.T., Parkkari, J., Heinonen, A., Äyrämö, S.,
Krosshaug, T., Vasankari, T., Kannus, P. & Pasanen K. (2020), Altered hip control during a
standing knee lift test is associated with increased risk of knee injuries.
Scand J Med Sci
Sports. 2020; 30: 922–
931.
· Rossi., M, Pasanen, K.,
Heinonen, A, Äyrämö, S., Räisänen, A., Leppänen, M., Myklebust, G.,
Vasankari, T., Kannus, P., Parkkari, J. (2020). 007 Performance in dynamic movement tasks
and occurrence of low back pain in youth floorball and basketball players. British Journal of Sports Medicine Mar 2020, 54 (Suppl 1) A4.
· Leppänen, M., Rantala, A.,
Parkkari, J., Vasankari, T., Äyrämö, S., Krosshaug, T., Kannus, P., Heinonen,
A., & Pasanen, K. (2020). 054 Cutting technique and risk
for non-contact knee injuries in youth basketball and floorball players.
· Jauhiainen, S., Äyrämö, S.,
Forsman, H., & Kauppi, J. P. (2019). Talent
identification in soccer using a one-class support vector machine. International Journal of Computer Science in
Sport, 18(3), 125-136.
· Saarela,
M., Ryynänen, O-P. &
Äyrämö, S. (2019), Predicting Hospital Associated
Disability from Imbalanced Data Using Supervised Learning, Artificial Intelligence in Medicine 95, 88-95.
· Leppänen,
M., Rossi, M.. Parkkari, J., Heinonen, A., Äyrämö, S.,
Vasankari, T., Kannus, P., Pasanen, K. (2019). Poor Pelvic Control During A
Knee Lift Test Is Associated With Increased Risk Of Knee Injuries: 555 May 29
2:00 PM - 2:15 PM. Medicine & Science in Sports & Exercise. 51(6)
Supplement:143, June 2019.
· Rossi., M, Pasanen, K., Heinonen, A, Äyrämö, S., Räisänen, A., Leppänen, M.,
Myklebust, G., Vasankari, T., Kannus, P., Parkkari, J. (2019) 5 Frontal plane femoral adduction during single-leg landing and
low back pain in young athletes: a prospective profits cohort study, British Journal of Sports Medicine 2019;53:A2.
· Rautiainen, I. & Äyrämö, S., Predicting overweight and obesity in
later life from childhood data: A review of predictive modeling approaches
(2019). arXiv:1911.08361.
·
Moilanen,
H., Äyrämö, S., & Kankaanranta, M., Fysiikkaa liikkuen – 7-luokkalaisten
oppilaiden ja opettajien kokemuksia kehollisesta opetuksesta fysiikassa, in
Tutkimuksesta luokkahuoneisiin:
Ainedidaktisia tutkimuksia 15, Suomen ainedidaktisen tutkimusseuran julkaisuja,
2019.
· Hakanen,
J., Ojalehto, V., Saarela, M., & Äyrämö, S. (2019). On
Combining Explainable Artificial Intelligence and Interactive Multiobjective Optimization in Data-Driven Decision
Support. In MCDM 2019 : The 25th International
Conference on Multiple Criteria Decision Making. Book of Abstracts (pp.
156-157). International Society on Multiple Criteria Decision Making.
· Moilanen, H., Äyrämö, S., Jauhiainen, S., & Kankaanranta, M., Collecting and Using Students’
Digital Well-Being Data in Multidisciplinary Teaching, Education Research International, vol. 2018, Article ID 3012079, 11
pages, 2018.
· Moilanen, H., Äyrämö, S., & Kankaanranta, M., Detecting pupils’ opinions on
learning physics bodily by unsupervised machine learning, Proceedings of E-Learn 2018 - World
Conference on E-Learning, Association for the Advancement of Computing in
Education, 2018.
· Moilanen, H., Äyrämö, S., & Kankaanranta, M., Learning
physics outside the classroom by combinating use of tablets
and bodily activity, Proceedings of
EdMedia + Innovate Learning Conference,
Association for the Advancement of Computing in Education, 2018.
· Rosso, V., Gastaldi, L., Rapp, W., Lindinger, S., Vanlandewijck,
Y., Äyrämö, S., & Linnamo, V., Balance perturbations as a
measurement tool for trunk impairment in cross country sit skiing, Adapted physical activity quarterly,
2018.
· Niemelä, M., Äyrämö, S., & Kärkkäinen, T., Comparison
of cluster validation indices with missing data, in Proceedings of 26th European Symposium on Artificial Neural Networks
(ESANN2018), Bruges (Belgium), 25-27 April 2018.
·
Äyrämö,
S. & Neittaanmäki, P. (toim.), Koneoppimispohjaiset
tekoälyratkaisut hyvinvointi- ja terveyssovelluksissa, Informaatioteknologian tiedekunnan julkaisuja, No. 42/2017.
· Äyrämö, S., Pölönen, I., and Eskelinen, M. A.: Clustering
incomplete spectral data with robust methods, Int. Arch. Photogramm. Remote Sens. Spatial
Inf. Sci., XLII-3/W3, 13-17, 2017.
·
Niinimäki,
E., Pöyhönen, J., Äyrämö, S., & Neittaanmäki, P., Omadata terveydenhuollon
tietointensiivisessä rakenteessa, Informaatioteknologian
tiedekunnan julkaisuja, No. 40/2017.
· Äyrämö, S., Vilmi, N., Mero, A., Piirainen,
J., Nummela, A., Pullinen, T., Avela, J., & Linnamo, V. (2017). Maturation-related differences in
neuromuscular fatigue after a short-term maximal run., Human Movement, 18(3),
17-25.
· Niemelä, M., Kulmala, JP., Kauppi, JP., Kosonen, J. & Äyrämö, S., Prediction of active
peak force using a multilayer perceptron. Sports Engineering (2017) 20: 213.
· Leppänen M, Pasanen K, Kujala
UM, Vasankari T, Kannus P, Äyrämö S, Krosshaug T, Bahr R, Avela J, Perttunen J,
Parkkari J. Stiff
Landings Are Associated With Increased ACL Injury Risk in Young Female
Basketball and Floorball Players. Am
J Sports Med. 2017. 45(2):386-393.
· Vilmi, N., Äyrämö, S., Nummela, A., Pullinen, T., Linnamo, V.,
Häkkinen, K., & Mero, A., Oxygen
Uptake, Acid-Base Balance and Anaerobic Energy System Contribution in Maximal
300 – 400 m Running in Child, Adolescent and Adult Athletes. Journal of Athletic Enhancement. 2016
5(3).
· Vesterinen, V., Nummela, A., Äyrämö, S., Laine, T., Hynynen, E.,
Mikkola, J., & Häkkinen, K., Monitoring
Training Adaptation With a Submaximal Running Test in Field Conditions, Int J Sports Physiol
Perform, 2016,11(3):393-9.
·
Kulmala,
J-P., Palosaari, K., & Äyrämö, S., 3D-liikeanalyysi määrittää nivelen
kuormituksen. Niveltieto, 3/2014.
· Kulmala J-P, Äyrämö S, Pasanen K, Avela J, & Parkkari J.,
Biomechanical factors underlying different knee loading profiles during
rearfoot running. Extended abstract. Proceedings
of 13th International symposium on 3D Analysis of Human Movement (3D-AHM 2014), 14 – 17 July, 2014, Lausanne, Switzerland. (PDF)
· Kulmala, J-P, Äyrämö, S., and Avela, J., Knee extensor and flexor
dominant gait patterns increase the knee frontal plane moment during walking.
J Orthop Res. 2013 Jul;31(7):1013-9.
·
Wartiainen, P., Kärkkäinen, T., Heimbürger, A., and
Äyrämö, S., Context-sensitive approach to dynamic visual analytics of energy
production processes. Information
Modelling and Knowledge Bases XXIV: Frontiers in Artificial Intelligence
and Applications, v 251. Peter Vojtas, Yasushi Kiyoki,
Takehiro Tokuda, Hannu Jaakkola, and Naofumi Yoshida
eds., IOS Press 2013.
· Äyrämö, S., Vilmi, N., Mero, A., Häkkinen, K,
Piirainen, J., Pullinen, T., Nummela, A. and Linnamo,
V. Neuromuscular fatigue after short-term maximal run in
child, youth, and adult athletes. XIX Congress of the International Society of
Electromyography and Kinesiology. Brisbane, Australia, July 19-21, 2012.
Abstract.
· Kulmala J-P, Äyrämö S, Avela J. Associations between different gait
strategies and knee adduction moment. 17th annual Congress of the VII European
College of Sport Science, 2012:43. Abstract.
· Wartiainen, P.,
Kärkkäinen, T., Heimbürger, A., and Äyrämö, S.,
Position paper: Methods of visual analytics in knowledge mining. In Jaak Henno,
Yasushi Kiyoki, Takehiro Tokuda, and Naofumi Yoshida, eds., 21th
European-Japanese Conference on Information Modelling and Knowledge Bases,
vol. 1. Tallinn University of Technology, 6 2011.
· Tirronen, V., Äyrämö, S. and Weber, M., Study on
the Effects of Pseudorandom Generation Quality on the Performance of
Differential Evolution, Proceedings
of 10th International Conference on Adaptive and Natural Computing
Algorithms, Part I, ICANNGA 2011, LNCS 6593, Springer 2011.
· Balahur, D.,
Hiltunen, L. and Äyrämö, S., Gender and ICT – The Comparative Analysis of
Finland and Romania, chapter in Women and
technological education: A European comparative perspective. The 10 Commends to
the policy makers.
D. Balahur, and P. Fadjukoff
(Eds.), Alexandru Ioan Cuza University Press, 2010.
· Nieminen, P., Rabin, N., Kärkkäinen, T., Averbuch, A., and Äyrämö, S.,
Robust Clustering and Neural Network Training with Dimension Reduction for
Industrial Use, Reports of the Dept. of Math. Inf. Tech. (Series C. Software
and Computational Engineering), 3/2010, University of Jyväskylä.
· Pechenizkiy, M., Ivannikov, A., Äyrämö, S. and
Kärkkäinen, T., Towards Better Understanding and Control of CFB-Boilers: Review
of Recent Research in Mining Time Series Data, Reports of the Dept. of Math.
Inf. Tech. (Series C. Software and Computational Engineering), 2/2010,
University of Jyväskylä.
· Mininno, E.,
Kärkkäinen, T., and Äyrämö, S., Multi-objective Online Optimization using
Evolutionary Algorithms, Reports of the Dept. of Math. Inf. Tech. (Series C.
Software and Computational Engineering), 1/2010, University of Jyväskylä.
· Äyrämö, S., Pirtala, P., Kauttonen, J, Naveed,
K., and Kärkkäinen, T., Mining Road Traffic Accidents, Reports of the Dept.
of Math. Inf. Tech. (Series C. Software and Computational Engineering),
2/2009, University of Jyväskylä. (PDF)
·
Äyrämö,
S. and Kärkkäinen, T., Tiedonlouhinnalla uutta ja yllättävää tietämystä
tieliikenneonnettomuuksista (Finnish), Tie
& Liikenne, 11/2009.
· Ivannikov, A.,
Pechenizkiy, M., Bakker, J., Leino, T., Jegoroff, M.,
Kärkkäinen, T., and Äyrämö, S.: Online Mass Flow Prediction in CFB Boiler, in Advances
in Data Mining. Applications and Theoretical Aspects, 9th Industrial
Conference, ICDM 2009,
Leipzig, Germany, July 20-22, 2009. Proceedings, Petra Perner (ed.), LNCS,
Vol. 5633, Springer 2009, pp. 206-219.
· Pylvänen, M.,
Äyrämö, S., and Kärkkäinen, T., Visualizing
Time Series State Changes with Prototype based Clustering, in Proceedings
of International Conference on Adaptive and Natural Computing Algorithms,
ICANNGA 2009, LNCS, Vol.
5495, Springer 2009.
· Nurminen, M., Suominen, P., Äyrämö,
S., and Kärkkäinen, T.. Applying
Semiautomatic Generation of Conceptual Models to Decision Support Systems
Domain, in Proceedings of IASTED International Conference on Software
Engineering (SE2009),
Innsbruck, Austria, February 17-18 2009.
· Aittokoski, T., Äyrämö, S., and Miettinen, K., Clustering aided approach
for expensive multiobjective design optimization,
Optimization
Methods and Software, 24(2), 2009.
· Leiviskä, K.,
Jämsä-Jounela, S-L, Olli, J., and Äyrämö, S., Computational
methods and techniques, chapter in Operational decision making in the
process industry: Multidisciplinary approach, Mätäsniemi,
Teemu (ed.), VTT Tiedotteita - Research Notes: 2442,
VTT, Espoo, 2008. (PDF)
· Nurminen, M., Suominen, P., Äyrämö, S., and Kärkkäinen, T., Use cases
for operational decision support system, chapter in Operational decision
making in the process industry: Multidisciplinary approach, Mätäsniemi, Teemu (ed.), VTT Tiedotteita
- Research Notes: 2442, VTT, Espoo, 2008 (PDF)
· Äyrämö, S., Kärkkäinen, T., and Majava, K., Robust
refinement of initial prototypes for partitioning-based clustering algorithms,
in Recent Advances in Stochastic Modeling and Data Analysis, C. H.
Skiadas, (eds.), World Scientific, Singapore, 2007, pp. 473-482.__
· Äyrämö, S., Kärkkäinen, T., and Majava, K.,
Robust refinement of initial prototypes for partitioning-based clustering
algorithms, in Proceedings of XIIth International
Conference on Applied Stochastic Models and Data Analysis (ASMDA), C. H.
Skiadas, (eds.), Chania, Crete, Greece, May 29 - June 1, 2007.
· Äyrämö, S., Knowledge Mining
using Robust Clustering, PhD thesis (monograph), University of
Jyväskylä, 2006.
· Äyrämö, S. and Kärkkäinen, T. Introduction to partitioning-based
clustering methods with a robust example, Reports of the Dept. of Math. Inf.
Tech. (Series C. Software and Computational Engineering), 1/2006,
University of Jyväskylä, 2006. (PDF)
· Kärkkäinen, T. and Äyrämö, S., On Computation of Spatial Median for
Robust Data Mining , In Proceedings of Sixth
Conference on Evolutionary and Deterministic Methods for Design, Optimisation and Control with Applications to Industrial
and Societal Problems (EUROGEN 2005), Munich, Germany, September 12 - 14,
2005. (PDF)
· Kärkkäinen, T. and Äyrämö, S., Robust
Clustering Methods for Incomplete and Erroneous Data, in Data Mining V:
Data Mining, Text Mining and their Business Applications, 2004.
· Äyrämö, S., Reaaliaikajärjestelmän
mallintaminen UML–Kuvausmenetelmän avulla (Finnish),
Pro Gradu -tutkielma (MSc thesis), Jyväskylän
yliopisto, 2002. (PDF)