Tapio Pahikkala

Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets (2020)

International Journal of Medical Informatics

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

Negative Predictive Value of Biparametric Prostate Magnetic Resonance Imaging in Excluding Significant Prostate Cancer: A Pooled Data Analysis Based on Clinical Data from Four Prospective, Registered Studies (2020)

European Urology Focus

Juha Knaapila, Ivan Jambor, Otto Ettala, Pekka Taimen, Janne Verho, Ileana Montoya Perez, Aida Kiviniemi, Tapio Pahikkala, Harri Merisaari, Tarja Lamminen, Jani Saunavaara, Hannu J.Aronen, Kari T.Syvänen, Peter J.Boström

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

Effect of oat β-glucan of different molecular weights on fecal bile acids, urine metabolites and pressure in the digestive tract – A human cross over trial (2020)

Food Chemistry

Hakkola S., Nylund L., Rosa-Sibakov N., Yang B., Nordlund E., Pahikkala T., Kalliomäki M., Aura A.M., Linderborg K.M.

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

Optimized reference spectrum for rating the facade sound insulation (2020)

Journal of the Acoustical Society of America

Virjonen P, Hongisto V, Mäkelä MM, Pahikkala T

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology (2020)

Forestry

Aura Salmivaara, Samuli Launiainen, Jari Perttunen, Paavo Nevalainen, Jonne Pohjankukka, Jari Ala-Ilomäki, Matti Sirén, Ari Laurén, Sakari Tuominen, Jori Uusitalo, Tapio Pahikkala, Jukka Heikkonen, Leena Finér

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

Synthetic minority oversampling of vital statistics data with generative adversarial networks (2020)

Journal of the American Medical Informatics Association

Aki Koivu, Mikko Sairanen, Antti Airola, Tapio Pahikkala

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

Bayesian Inference for Predicting the Monetization Percentage in Free-to-Play Games (2020)

IEEE Transactions on Games

Numminen R, Viljanen MJ, Pahikkala T

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography (2020)

PLoS ONE

Mieronkoski R, Syrjälä E, Jiang M, Rahmani A, Pahikkala T, Liljeberg P, Salanterä S

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

GeFeS: A generalized wrapper feature selection approach for optimizing classification performance (2020)

Computers in Biology and Medicine

Golnaz Sahebi, Parisa Movahedi, Masoumeh Ebrahimi, Tapio Pahikkala, Juha Plosila, Hannu Tenhunen

(A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä)

Predicting Unemployment with Machine Learning Based on Registry Data (2020)

Lecture Notes in Business Information Processing

Markus Viljanen, Tapio Pahikkala

(A4 Artikkeli konferenssijulkaisussa)

Contact

Links

Publications

Sort by:

Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets (2020)

Negative Predictive Value of Biparametric Prostate Magnetic Resonance Imaging in Excluding Significant Prostate Cancer: A Pooled Data Analysis Based on Clinical Data from Four Prospective, Registered Studies (2020)

Effect of oat β-glucan of different molecular weights on fecal bile acids, urine metabolites and pressure in the digestive tract – A human cross over trial (2020)

Optimized reference spectrum for rating the facade sound insulation (2020)

Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology (2020)

Synthetic minority oversampling of vital statistics data with generative adversarial networks (2020)

Bayesian Inference for Predicting the Monetization Percentage in Free-to-Play Games (2020)

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography (2020)

GeFeS: A generalized wrapper feature selection approach for optimizing classification performance (2020)

Predicting Unemployment with Machine Learning Based on Registry Data (2020)