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830 results on '"OKSALA, NIKU"'

1. Forecasting mortality associated emergency department crowding

Author

Nevanlinna, Jalmari, Eidstø, Anna, Ylä-Mattila, Jari, Koivistoinen, Teemu, Oksala, Niku, Kanniainen, Juho, Palomäki, Ari, and Roine, Antti

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Applications
Abstract

Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with it's detrimental effects. Recent findings in our ED indicate that occupancy ratios exceeding 90% are associated with increased 10-day mortality. In this paper, we aim to predict these crisis periods using retrospective data from a large Nordic ED with a LightGBM model. We provide predictions for the whole ED and individually for it's different operational sections. We demonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of 0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI 0.75-0.83). Consequently we show that forecasting mortality-associated crowding using anonymous administrative data is feasible.

Published
2024

2. Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input

Author

Tuominen, Jalmari, Pulkkinen, Eetu, Peltonen, Jaakko, Kanniainen, Juho, Oksala, Niku, Palomäki, Ari, and Roine, Antti

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Emergency department (ED) crowding is a significant threat to patient safety and it has been repeatedly associated with increased mortality. Forecasting future service demand has the potential patient outcomes. Despite active research on the subject, several gaps remain: 1) proposed forecasting models have become outdated due to quick influx of advanced machine learning models (ML), 2) amount of multivariable input data has been limited and 3) discrete performance metrics have been rarely reported. In this study, we document the performance of a set of advanced ML models in forecasting ED occupancy 24 hours ahead. We use electronic health record data from a large, combined ED with an extensive set of explanatory variables, including the availability of beds in catchment area hospitals, traffic data from local observation stations, weather variables, etc. We show that N-BEATS and LightGBM outpeform benchmarks with 11 % and 9 % respective improvements and that DeepAR predicts next day crowding with an AUC of 0.76 (95 % CI 0.69-0.84). To the best of our knowledge, this is the first study to document the superiority of LightGBM and N-BEATS over statistical benchmarks in the context of ED forecasting.

Published
2023

3. Early Warning Software for Emergency Department Crowding

Author

Tuominen, Jalmari, Koivistoinen, Teemu, Kanniainen, Juho, Oksala, Niku, Palomäki, Ari, and Roine, Antti

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead to better resource management and has the potential to improve treatment outcomes. This logic has motivated an increasing number of research articles but there has been little to no effort to move these findings from theory to practice. In this article, we present first results of a prospective crowding early warning software, that was integrated to hospital databases to create real-time predictions every hour over the course of 5 months in a Nordic combined ED using Holt-Winters' seasonal methods. We showed that the software could predict next hour crowding with a nominal AUC of 0.98 and 24 hour crowding with an AUC of 0.79 using simple statistical models. Moreover, we suggest that afternoon crowding can be predicted at 1 p.m. with an AUC of 0.84., Comment: 15 pages, 6 figures

Published
2023

4. Towards Wearable Continuous Point-of-Care Monitoring for Deep Vein Thrombosis of the Lower Limb

Author

the ThrombUS+ Consortium, Kaldoudi, Eleni, Marozas, Vaidotas, Rytis, Jurkonis, Pousset, Nicolas, Legros, Mathieu, Kircher, Marco, Novikov, Dmitry, Sakalauskas, Andrius, Moustakidis, Pavlos, Ayinde, Babajide, Moltani, Lara Alessia, Balling, Susann, Vehkaoja, Antti, Oksala, Niku, Macas, Andrius, Balciuniene, Neringa, Bigaki, Maria, Potoupnis, Michail, Papadopoulou, Stella-Lida, Grandone, Elvira, Gautier, Maxime, Bouda, Sabrina, Schloetelburg, Cord, Prinz, Thorsten, Dionisio, Pietro, Anagnostopoulos, Spyridon, Drougka, Ioanna, Folkvord, Frans, Drosatos, George, Didaskalou, Stylianos, Magjarević, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Jarm, Tomaž, editor, Šmerc, Rok, editor, and Mahnič-Kalamiza, Samo, editor

Published
2024
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17. Identification of Phospholipids Relevant to Cancer Tissue Using Differential Ion Mobility Spectrometry.

Author

Sioris, Patrik, Mäkelä, Meri, Kontunen, Anton, Karjalainen, Markus, Vehkaoja, Antti, Oksala, Niku, and Roine, Antti

Abstract

Phospholipids are the main building components of cell membranes and are also used for cell signaling and as energy storages. Cancer cells alter their lipid metabolism, which ultimately leads to an increase in phospholipids in cancer tissue. Surgical energy instruments use electrical or vibrational energy to heat tissues, which causes intra- and extracellular water to expand rapidly and degrade cell structures, bursting the cells, which causes the formation of a tissue aerosol or smoke depending on the amount of energy used. This gas phase analyte can then be analyzed via gas analysis methods. Differential mobility spectrometry (DMS) is a method that can be used to differentiate malignant tissue from benign tissues in real time via the analysis of surgical smoke produced by energy instruments. Previously, the DMS identification of cancer tissue was based on a 'black box method' by differentiating the 2D dispersion plots of samples. This study sets out to find datapoints from the DMS dispersion plots that represent relevant target molecules. We studied the ability of DMS to differentiate three subclasses of phospholipids (phosphatidylcholine, phosphatidylinositol, and phosphatidylethanolamine) from a control sample using a bovine skeletal muscle matrix with a 5 mg addition of each phospholipid subclass to the sample matrix. We trained binary classifiers using linear discriminant analysis (LDA) and support vector machines (SVM) for sample classification. We were able to identify phosphatidylcholine, -inositol, and -ethanolamine with SVM binary classification accuracies of 91%, 73%, and 66% and with LDA binary classification accuracies of 82%, 74%, and 72%, respectively. Phosphatidylcholine was detected with a reliable classification accuracy, but ion separation setups should be adjusted in future studies to reliably detect other relevant phospholipids such as phosphatidylinositol and phosphatidylethanolamine and improve DMS as a microanalysis method and identify other phospholipids relevant to cancer tissue. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Short-term stability of combined finger and toe photoplethysmogram analysis

Author

Peltokangas, Mikko, Huotari, Matti, Verho, Jarmo, Mattila, Ville M., Röning, Juha, Romsi, Pekka, Lekkala, Jukka, Vehkaoja, Antti, Oksala, Niku, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Eskola, Hannu, editor, Väisänen, Outi, editor, Viik, Jari, editor, and Hyttinen, Jari, editor

Published
2018
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38. Cohort Description for MADDEC – Mass Data in Detection and Prevention of Serious Adverse Events in Cardiovascular Disease

Author

Hernesniemi, Jussi A., Mahdiani, Shadi, Lyytikäinen, Leo-Pekka, Lehtimäki, Terho, Eskola, Markku, Nikus, Kjell, Antila, Kari, Oksala, Niku, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Eskola, Hannu, editor, Väisänen, Outi, editor, Viik, Jari, editor, and Hyttinen, Jari, editor

Published
2018
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39. Day-to-day repeatability of the results of the finger-toe-plot analysis

Author

Peltokangas, Mikko, Telembeci, Anca A., Verho, Jarmo, Lekkala, Jukka, Vehkaoja, Antti, Oksala, Niku, Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Eskola, Hannu, editor, Väisänen, Outi, editor, Viik, Jari, editor, and Hyttinen, Jari, editor

Published
2018
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45. Rapid identification of pediatric brain tumors with differential mobility spectrometry.

Author

Haapala, Ilkka, Rauhameri, Anton, Mäkelä, Meri, Karjalainen, Markus, Kontunen, Anton, Mieskolainen, Markus, Haapasalo, Hannu, Roine, Antti, Oksala, Niku, Vehkaoja, Antti, Haapasalo, Joonas, and Nordfors, Kristiina

Subjects
CEREBELLAR tumors, BRAIN tumors, FROZEN tissue sections, FISHER discriminant analysis, SPECTROMETRY, CHILD patients, EPENDYMOMA
Abstract

Introduction: Brain tumors are a major source of disease burden in pediatric population, with the most common tumor types being pilocytic astrocytoma, ependymoma and medulloblastoma. In every tumor entity, surgery is the cornerstone of treatment, but the importance of gross-total resection and the corresponding patient prognosis is highly variant. However, real-time identification of pediatric CNS malignancies based on the histology of the frozen sections alone is especially troublesome. We propose a novel method based on differential mobility spectrometry (DMS) analysis for rapid identification of pediatric brain tumors. Methods: We prospectively obtained tumor samples from 15 pediatric patients (5 pilocytic astrocytomas, 5 ependymomas and 5 medulloblastomas). The samples were cut into 36 smaller specimens that were analyzed with the DMS. Results: With linear discriminant analysis algorithm, a classification accuracy (CA) of 70% was reached. Additionally, a 75% CA was achieved in a pooled analysis of medulloblastoma vs. gliomas. Discussion: Our results show that the DMS is able to differentiate most common pediatric brain tumor samples, thus making it a promising additional instrument for real-time brain tumor diagnostics. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Thrombus Aspirates From Patients With Acute Ischemic Stroke Are Infiltrated by Viridans Streptococci

Author

Patrakka, Olli, primary, Tuomisto, Sari, additional, Pienimäki, Juha‐Pekka, additional, Ollikainen, Jyrki, additional, Oksala, Niku, additional, Lampinen, Vili, additional, Ojanen, Markus J. T., additional, Huhtala, Heini, additional, Hytönen, Vesa P., additional, Lehtimäki, Terho, additional, Martiskainen, Mika, additional, and Karhunen, Pekka J., additional

Published
2023
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