Publications

2023

  1. Liedes, H, Mattila, E, Honka, A, Absetz, P, Aittola, K, Manninen, S et al.. Associations Between Engagement with the BitHabit Digital Lifestyle Intervention and Changes in Type 2 Diabetes Risk Factors. Stud Health Technol Inform. 2023;302 :1009-1010. doi: 10.3233/SHTI230328. PubMed PMID:37203555 .
  2. Liedes, H, Pajula, J, Vuorinen, AL, De Pretis, F, van Gils, M, Harno, K et al.. CYP3A4*22 may increase bleeding risk in ticagrelor users. Basic Clin Pharmacol Toxicol. 2023; :. doi: 10.1111/bcpt.13884. PubMed PMID:37162098 .
  3. Niiranen, TJU, Chiollaz, AC, Takala, RSK, Voutilainen, M, Tenovuo, O, Newcombe, VFJ et al.. Trajectories of interleukin 10 and heart fatty acid-binding protein levels in traumatic brain injury patients with or without extracranial injuries. Front Neurol. 2023;14 :1133764. doi: 10.3389/fneur.2023.1133764. PubMed PMID:37082447 PubMed Central PMC10111051.
  4. Bostanci, E, Kocak, E, Unal, M, Guzel, MS, Acici, K, Asuroglu, T et al.. Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer. Sensors (Basel). 2023;23 (6):. doi: 10.3390/s23063080. PubMed PMID:36991790 PubMed Central PMC10052105.
  5. Antikainen, E, Linnosmaa, J, Umer, A, Oksala, N, Eskola, M, van Gils, M et al.. Transformers for cardiac patient mortality risk prediction from heterogeneous electronic health records. Sci Rep. 2023;13 (1):3517. doi: 10.1038/s41598-023-30657-1. PubMed PMID:36864069 PubMed Central PMC9978282.
  6. Sivari, E, Bostanci, E, Guzel, MS, Acici, K, Asuroglu, T, Ercelebi Ayyildiz, T et al.. A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models. Diagnostics (Basel). 2023;13 (4):. doi: 10.3390/diagnostics13040720. PubMed PMID:36832205 PubMed Central PMC9954881.
  7. Hoogendoorn, P, Versluis, A, van Kampen, S, McCay, C, Leahy, M, Bijlsma, M et al.. What Makes a Quality Health App-Developing a Global Research-Based Health App Quality Assessment Framework for CEN-ISO/TS 82304-2: Delphi Study. JMIR Form Res. 2023;7 :e43905. doi: 10.2196/43905. PubMed PMID:36538379 PubMed Central PMC9872976.

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2022

  1. Antikainen, E, Njoum, H, Kudelka, J, Branco, D, Rehman, RZU, Macrae, V et al.. Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study. Front Physiol. 2022;13 :968185. doi: 10.3389/fphys.2022.968185. PubMed PMID:36452041 PubMed Central PMC9702812.
  2. Muuraiskangas, ST, Honka, AM, Junno, UM, Nieminen, HO, Kaartinen, JK. A Technology-Assisted Telephone Intervention for Work-Related Stress Management: Pilot Randomized Controlled Trial. J Med Internet Res. 2022;24 (7):e26569. doi: 10.2196/26569. PubMed PMID:35830233 PubMed Central PMC9330204.
  3. Ruokolainen, J, Haladijan, J, Juutinen, M, Puustinen, J, Holm, A, Vehkaoja, A et al.. Mobilemicroservices Architecture for Remote Monitoring of Patients: A Feasibility Study. Stud Health Technol Inform. 2022;290 :200-204. doi: 10.3233/SHTI220061. PubMed PMID:35673000 .
  4. Loisa, E, Kallonen, A, Hoppu, S, Tirkkonen, J. Trends in the national early warning score are associated with subsequent mortality – A prospective three-centre observational study with 11,331 general ward patients. Resusc Plus. 2022;10 :100251. doi: 10.1016/j.resplu.2022.100251. PubMed PMID:35620180 PubMed Central PMC9127395.
  5. Machal, ML, Värri, A. Development Towards Patient-Centered eHealth Services in Finland. Stud Health Technol Inform. 2022;294 :707-708. doi: 10.3233/SHTI220563. PubMed PMID:35612184 .
  6. De Pretis, F, van Gils, M, Forsberg, MM. A smart hospital-driven approach to precision pharmacovigilance. Trends Pharmacol Sci. 2022;43 (6):473-481. doi: 10.1016/j.tips.2022.03.009. PubMed PMID:35490032 .
  7. Loisa, E, Kallonen, A, Hoppu, S, Tirkkonen, J. Ability of the National Early Warning Score and its respiratory and haemodynamic subcomponents to predict short-term mortality on general wards: a prospective three-centre observational study in Finland. BMJ Open. 2022;12 (4):e055752. doi: 10.1136/bmjopen-2021-055752. PubMed PMID:35473725 PubMed Central PMC9045111.
  8. Lavikainen, P, Mattila, E, Absetz, P, Harjumaa, M, Lindström, J, Järvelä-Reijonen, E et al.. Digitally Supported Lifestyle Intervention to Prevent Type 2 Diabetes Through Healthy Habits: Secondary Analysis of Long-Term User Engagement Trajectories in a Randomized Controlled Trial. J Med Internet Res. 2022;24 (2):e31530. doi: 10.2196/31530. PubMed PMID:35200147 PubMed Central PMC8914749.
  9. Marabita, F, James, T, Karhu, A, Virtanen, H, Kettunen, K, Stenlund, H et al.. Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health. Cell Syst. 2022;13 (3):241-255.e7. doi: 10.1016/j.cels.2021.11.001. PubMed PMID:34856119 .
  10. Lähteenmäki, J, Vuorinen, AL, Pajula, J, Harno, K, Lehto, M, Niemi, M et al.. Integrating data from multiple Finnish biobanks and national health-care registers for retrospective studies: Practical experiences. Scand J Public Health. 2022;50 (4):482-489. doi: 10.1177/14034948211004421. PubMed PMID:33845693 PubMed Central PMC9152591.

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11. Aşuroğlu, T., Oğul, H. A deep learning approach for parkinson’s disease severity assessment, Health and Technology, 2022 https://doi.org/10.1007/s12553-022-00698-z

12. Aşuroğlu, T., “Complex Human Activity Recognition Using a Local Weighted Approach,” IEEE Access, 2022. https://doi.org/10.1109/ACCESS.2022.3208686

2021

  1. Antikainen, E, Cella, P, Tolonen, A, van Gils, M. SPECT Image Features for Early Detection of Parkinson’s Disease using Machine Learning Methods. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021 :2773-2777. doi: 10.1109/EMBC46164.2021.9630272. PubMed PMID:34891824 .
  2. Marabita, F, James, T, Karhu, A, Virtanen, H, Kettunen, K, Stenlund, H et al.. Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health. Cell Syst. 2022;13 (3):241-255.e7. doi: 10.1016/j.cels.2021.11.001. PubMed PMID:34856119 .
  3. Tamminen, J, Kallonen, A, Hoppu, S, Kalliomäki, J. Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland. Resusc Plus. 2021;5 :100089. doi: 10.1016/j.resplu.2021.100089. PubMed PMID:34223354 PubMed Central PMC8244527.
  4. Lähteenmäki, J, Vuorinen, AL, Pajula, J, Harno, K, Lehto, M, Niemi, M et al.. Pharmacogenetics of Bleeding and Thromboembolic Events in Direct Oral Anticoagulant Users. Clin Pharmacol Ther. 2021;110 (3):768-776. doi: 10.1002/cpt.2316. PubMed PMID:34043814 .
  5. Lähteenmäki, J, Vuorinen, AL, Pajula, J, Harno, K, Lehto, M, Niemi, M et al.. Integrating data from multiple Finnish biobanks and national health-care registers for retrospective studies: Practical experiences. Scand J Public Health. 2022;50 (4):482-489. doi: 10.1177/14034948211004421. PubMed PMID:33845693 PubMed Central PMC9152591.
  6. Tohka, J, van Gils, M. Evaluation of machine learning algorithms for health and wellness applications: A tutorial. Comput Biol Med. 2021;132 :104324. doi: 10.1016/j.compbiomed.2021.104324. PubMed PMID:33774270 .
  7. Vuorinen, AL, Lehto, M, Niemi, M, Harno, K, Pajula, J, van Gils, M et al.. Pharmacogenetics of Anticoagulation and Clinical Events in Warfarin-Treated Patients: A Register-Based Cohort Study with Biobank Data and National Health Registries in Finland. Clin Epidemiol. 2021;13 :183-195. doi: 10.2147/CLEP.S289031. PubMed PMID:33727862 PubMed Central PMC7954279.
  8. Aşuroğlu, T, Oğul, H. A deep learning approach for sepsis monitoring via severity score estimation. Comput Methods Programs Biomed. 2021;198 :105816. doi: 10.1016/j.cmpb.2020.105816. PubMed PMID:33157471 .

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2020

  1. Pirneskoski, J, Tamminen, J, Kallonen, A, Nurmi, J, Kuisma, M, Olkkola, KT et al.. Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study. Resusc Plus. 2020;4 :100046. doi: 10.1016/j.resplu.2020.100046. PubMed PMID:34223321 PubMed Central PMC8244434.
  2. Posti, JP, Takala, RSK, Raj, R, Luoto, TM, Azurmendi, L, Lagerstedt, L et al.. Admission Levels of Interleukin 10 and Amyloid β 1-40 Improve the Outcome Prediction Performance of the Helsinki Computed Tomography Score in Traumatic Brain Injury. Front Neurol. 2020;11 :549527. doi: 10.3389/fneur.2020.549527. PubMed PMID:33192979 PubMed Central PMC7661930.
  3. Rhodius-Meester, HFM, Paajanen, T, Koikkalainen, J, Mahdiani, S, Bruun, M, Baroni, M et al.. cCOG: A web-based cognitive test tool for detecting neurodegenerative disorders. Alzheimers Dement (Amst). 2020;12 (1):e12083. doi: 10.1002/dad2.12083. PubMed PMID:32864411 PubMed Central PMC7446945.
  4. Pekkala, T, Hall, A, Ngandu, T, van Gils, M, Helisalmi, S, Hänninen, T et al.. Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline. Front Aging Neurosci. 2020;12 :228. doi: 10.3389/fnagi.2020.00228. PubMed PMID:32848707 PubMed Central PMC7406705.
  5. Vuorinen, V, Aarnio, M, Alava, M, Alopaeus, V, Atanasova, N, Auvinen, M et al.. Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors. Saf Sci. 2020;130 :104866. doi: 10.1016/j.ssci.2020.104866. PubMed PMID:32834511 PubMed Central PMC7428778.
  6. Juutinen, M, Wang, C, Zhu, J, Haladjian, J, Ruokolainen, J, Puustinen, J et al.. Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study. PLoS One. 2020;15 (7):e0236258. doi: 10.1371/journal.pone.0236258. PubMed PMID:32701955 PubMed Central PMC7377496.
  7. Muurling, M, Rhodius-Meester, HFM, Pärkkä, J, van Gils, M, Frederiksen, KS, Bruun, M et al.. Gait Disturbances are Associated with Increased Cognitive Impairment and Cerebrospinal Fluid Tau Levels in a Memory Clinic Cohort. J Alzheimers Dis. 2020;76 (3):1061-1070. doi: 10.3233/JAD-200225. PubMed PMID:32597806 PubMed Central PMC7505008.
  8. Lagerstedt, L, Azurmendi, L, Tenovuo, O, Katila, AJ, Takala, RSK, Blennow, K et al.. Interleukin 10 and Heart Fatty Acid-Binding Protein as Early Outcome Predictors in Patients With Traumatic Brain Injury. Front Neurol. 2020;11 :376. doi: 10.3389/fneur.2020.00376. PubMed PMID:32581990 PubMed Central PMC7280446.
  9. Värri, A, Tiainen, M, Rajalahti, E, Kinnunen, UM, Saarni, L, Ahonen, O et al.. The Definition of Informatics Competencies in Finnish Healthcare and Social Welfare Education. Stud Health Technol Inform. 2020;270 :1143-1147. doi: 10.3233/SHTI200341. PubMed PMID:32570560 .
  10. Raissadati, A, Knihtilä, H, Pätilä, T, Nieminen, H, Jokinen, E. Long-term Social Outcomes After Congenital Heart Surgery. Pediatrics. 2020;146 (1):. doi: 10.1542/peds.2019-3745. PubMed PMID:32503936 .
  11. Hossain, I, Mohammadian, M, Takala, RSK, Tenovuo, O, Azurmendi Gil, L, Frantzén, J et al.. Admission Levels of Total Tau and β-Amyloid Isoforms 1-40 and 1-42 in Predicting the Outcome of Mild Traumatic Brain Injury. Front Neurol. 2020;11 :325. doi: 10.3389/fneur.2020.00325. PubMed PMID:32477238 PubMed Central PMC7237639.
  12. Rytky, SJO, Tiulpin, A, Frondelius, T, Finnilä, MAJ, Karhula, SS, Leino, J et al.. Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography. Osteoarthritis Cartilage. 2020;28 (8):1133-1144. doi: 10.1016/j.joca.2020.05.002. PubMed PMID:32437969 .
  13. Raissadati, A, Haukka, J, Pätilä, T, Nieminen, H, Jokinen, E. Chronic Disease Burden After Congenital Heart Surgery: A 47-Year Population-Based Study With 99% Follow-Up. J Am Heart Assoc. 2020;9 (9):e015354. doi: 10.1161/JAHA.119.015354. PubMed PMID:32316818 PubMed Central PMC7428561.
  14. Salmi, A, Nieminen, HJ, Veira Canle, D, Hæggström, E, Kontiola, A. Non-contact determination of intra-ocular pressure in an ex vivo porcine model. PLoS One. 2020;15 (2):e0227488. doi: 10.1371/journal.pone.0227488. PubMed PMID:32012155 PubMed Central PMC6996824.
  15. Jääskeläinen, O, Hall, A, Tiainen, M, van Gils, M, Lötjönen, J, Kangas, AJ et al.. Metabolic Profiles Help Discriminate Mild Cognitive Impairment from Dementia Stage in Alzheimer’s Disease. J Alzheimers Dis. 2020;74 (1):277-286. doi: 10.3233/JAD-191226. PubMed PMID:32007958 PubMed Central PMC7175942.
  16. Karhula, SS, Finnilä, MAJ, Rytky, SJO, Cooper, DM, Thevenot, J, Valkealahti, M et al.. Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography. Ann Biomed Eng. 2020;48 (2):595-605. doi: 10.1007/s10439-019-02374-2. PubMed PMID:31583552 PubMed Central PMC6949315.

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