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1. Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis

Author

Morel, Sandrine, Hostettler, Isabelle, Spinner, Georg, Bourcier, Romain, Pera, Joanna, Meling, Torsten R, Alg, Varinder S, Houlden, H., Bakker, M. K, Van't Hof, Femke, Rinkel, Gabriel J E, Foroud, Tatiana, Lai, Dongbing, Moomaw, Charles J, Worrall, Bradford B, Caroff, Jildaz, Constant-Dits-Beaufils, Pacôme, Karakachoff, Matilde, Cuadrado-Godia, Elisa, Dauvillier, Jerome, Desal, Hubert, Friedrich, Christoph M, Gaal-Paavola, Emilia I, Hirsch, Sven, Ican Study Group, The, Isidor, Nathalie, Jaaskelainen, Juha E, Jahromi, Behnam Rezai, Jiménez-Conde, Jordi, Kaukovalta, Hanna, Kim, Helen, Kivisaari, Riku, Ko, Nerissa U, Kulcsàr, Zolt, Laakso, Aki, Lövblad, Karl O, Lindgren, Antti, Machi, Paolo, Martin, Olivier, Mendes Pereira, Vitor, Millwood, Iona Y, Niemelä, Mika, Rüfenacht, Daniel, Redon, Richard, Rimbert, Antoine, Rouchaud, Aymeric, Schilling, Sabine, Slowik, Agnieszka, Soriano-Tárraga, Carolina, von Und Zu Fraunberg, Mikael, Walters, Robin G, Woo, Daniel, Broderick, Joseph P, Werring, David J, Ruigrok, Ynte M, and Bijlenga, Philippe

Subjects
hypertension, subarachnoid hemorrhage, risk factors, intracranial aneurysm, location, smoking
Abstract

Intracranial aneurysms (IAs) are usually asymptomatic with a low risk of rupture, but consequences of aneurysmal subarachnoid hemorrhage (aSAH) are severe. Identifying IAs at risk of rupture has important clinical and socio-economic consequences. The goal of this study was to assess the effect of patient and IA characteristics on the likelihood of IA being diagnosed incidentally versus ruptured. Patients were recruited at 21 international centers. Seven phenotypic patient characteristics and three IA characteristics were recorded. The analyzed cohort included 7992 patients. Multivariate analysis demonstrated that: (1) IA location is the strongest factor associated with IA rupture status at diagnosis; (2) Risk factor awareness (hypertension, smoking) increases the likelihood of being diagnosed with unruptured IA; (3) Patients with ruptured IAs in high-risk locations tend to be older, and their IAs are smaller; (4) Smokers with ruptured IAs tend to be younger, and their IAs are larger; (5) Female patients with ruptured IAs tend to be older, and their IAs are smaller; (6) IA size and age at rupture correlate. The assessment of associations regarding patient and IA characteristics with IA rupture allows us to refine IA disease models and provide data to develop risk instruments for clinicians to support personalized decision-making., + ID der Publikation: hslu_97752 + Art des Beitrages: Wissenschaftliche Medien + Jahrgang: 12 + Sprache: Englisch + Letzte Aktualisierung: 2023-07-10 16:04:23

Published
2022
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2. Biomedical image analysis competitions: The state of current participation practice

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)
Abstract

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Published
2022
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3. Boosting EfficientNets Ensemble Performance via Pseudo-Labels and Synthetic Images by pix2pixHD for Infection and Ischaemia Classification in Diabetic Foot Ulcers

Author

Bloch, Louise, Brüngel, Raphael, and Friedrich, Christoph M.

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (cs.LG)
Abstract

Diabetic foot ulcers are a common manifestation of lesions on the diabetic foot, a syndrome acquired as a long-term complication of diabetes mellitus. Accompanying neuropathy and vascular damage promote acquisition of pressure injuries and tissue death due to ischaemia. Affected areas are prone to infections, hindering the healing progress. The research at hand investigates an approach on classification of infection and ischaemia, conducted as part of the Diabetic Foot Ulcer Challenge (DFUC) 2021. Different models of the EfficientNet family are utilized in ensembles. An extension strategy for the training data is applied, involving pseudo-labeling for unlabeled images, and extensive generation of synthetic images via pix2pixHD to cope with severe class imbalances. The resulting extended training dataset features $8.68$ times the size of the baseline and shows a real to synthetic image ratio of $1:3$. Performances of models and ensembles trained on the baseline and extended training dataset are compared. Synthetic images featured a broad qualitative variety. Results show that models trained on the extended training dataset as well as their ensemble benefit from the large extension. F1-Scores for rare classes receive outstanding boosts, while those for common classes are either not harmed or boosted moderately. A critical discussion concretizes benefits and identifies limitations, suggesting improvements. The work concludes that classification performance of individual models as well as that of ensembles can be boosted utilizing synthetic images. Especially performance for rare classes benefits notably., Comment: Accepted for Workshop Proceedings of the Diabetic Foot Ulcers Challenge (DFUC) as part of the 2021 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

Published
2021
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4. Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors

Author

Bakker, Mark K, van der Spek, Rick AA, van Rheenen, Wouter, Morel, Sandrine, Bourcier, Romain, Hostettler, Isabel C, Alg, Varinder S, van Eijk, Kristel R, Koido, Masaru, Akiyama, Masato, Terao, Chikashi, Matsuda, Koichi, Walters, Robin G, Lin, Kuang, Li, Liming, Millwood, Iona Y, Chen, Zhengming, Rouleau, Guy A, Zhou, Sirui, Rannikmäe, Kristiina, Sudlow, Cathie LM, Houlden, Henry, van den Berg, Leonard H, Dina, Christian, Naggara, Olivier, Gentric, Jean-Christophe, Shotar, Eimad, Eugène, François, Desal, Hubert, Winsvold, Bendik S, Børte, Sigrid, Johnsen, Marianne Bakke, Brumpton, Ben M, Sandvei, Marie Søfteland, Willer, Cristen J, Hveem, Kristian, Zwart, John-Anker, Verschuren, WM Monique, Friedrich, Christoph M, Hirsch, Sven, Schilling, Sabine, Dauvillier, Jérôme, Martin, Olivier, HUNT All-In Stroke, China Kadoorie Biobank Collaborative Group, BioBank Japan Project Consortium, ICAN Study Group, CADISP Group, Genetics and Observational Subarachnoid Haemorrhage (GOSH) Study investigators, International Stroke Genetics Consortium (ISGC), Jones, Gregory T, Bown, Matthew J, Ko, Nerissa U, Kim, Helen, Coleman, Jonathan RI, Breen, Gerome, Zaroff, Jonathan G, Klijn, Catharina JM, Malik, Rainer, Dichgans, Martin, Sargurupremraj, Muralidharan, Tatlisumak, Turgut, Amouyel, Philippe, Debette, Stéphanie, Rinkel, Gabriel JE, Worrall, Bradford B, Pera, Joanna, Slowik, Agnieszka, Gaál-Paavola, Emília I, Niemelä, Mika, Jääskeläinen, Juha E, von Und Zu Fraunberg, Mikael, Lindgren, Antti, Broderick, Joseph P, Werring, David J, Woo, Daniel, Redon, Richard, Bijlenga, Philippe, Kamatani, Yoichiro, Veldink, Jan H, and Ruigrok, Ynte M

Subjects
BioBank Japan Project Consortium, Blood Pressure, Cardiovascular, Medical and Health Sciences, International Stroke Genetics Consortium, White People, Asian People, Risk Factors, Clinical Research, Genetics, ICAN Study Group, Humans, 2.1 Biological and endogenous factors, Genetic Predisposition to Disease, Polymorphism, Aetiology, China Kadoorie Biobank Collaborative Group, CADISP Group, Prevention, Smoking, Human Genome, Neurosciences, Endothelial Cells, Intracranial Aneurysm, Single Nucleotide, Subarachnoid Hemorrhage, Biological Sciences, Brain Disorders, Stroke, Case-Control Studies, Hypertension, HUNT All-In Stroke, Genetics and Observational Subarachnoid Haemorrhage (GOSH) Study investigators, Genome-Wide Association Study, Developmental Biology
Abstract

Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits.

Published
2020

5. Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications

Author

Ionescu, Bogdan, Müller, Henning, Péteri, Renaud, Abacha, Asma Ben, Datla, Vivek, Hasan, Sadid A., Demner-Fushman, Dina, Kozlovski, Serge, Liauchuk, Vitali, Cid, Yashin Dicente, Kovalev, Vassili, Pelka, Obioma, Friedrich, Christoph M., García Seco de Herrera, Alba, Ninh, Van-Tu, Le, Tu-Khiem, Zhou, Liting, Piras, Luca, Riegler, Michael, Halvorsen, Pål, Tran, Minh-Triet, Lux, Mathias, Gurrin, Cathal, Dang Nguyen, Duc Tien, Chamberlain, Jon, Clark, Adrian, Campello, Antonio, Fichou, Dimitri, Berari, Raul, Brie, Paul, Dogariu, Mihai, Ştefan, Liviu Daniel, and Constantin, Mihai Gabriel

Abstract

This paper presents an overview of the ImageCLEF 2020 lab that was organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks, i.e., caption analysis, tuberculosis prediction, and medical visual question answering and question generation, (ii) a lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (iii) a coral task about segmenting and labeling collections of coral reef images, and (iv) a new Internet task addressing the problems of identifying hand-drawn user interface components. Despite the current pandemic situation, the benchmark campaign received a strong participation with over 40 groups submitting more than 295 runs. acceptedVersion

Published
2020

6. Author Correction: Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors

Author

Bakker, Mark K, van der Spek, Rick A A, Terao, Chikashi, Bulters, Diederik, Kitchen, Neil, Brown, Martin, Grieve, Joan, Matsuda, Koichi, Walters, Robin G, Lin, Kuang, Li, Liming, Millwood, Iona Y, Chen, Zhengming, Rouleau, Guy A, Zhou, Sirui, Rannikmäe, Kristiina, van Rheenen, Wouter, Sudlow, Cathie L M, Houlden, Henry, van den Berg, Leonard H, Dina, Christian, Naggara, Olivier, Gentric, Jean-Christophe, Shotar, Eimad, Eugène, François, Desal, Hubert, Winsvold, Bendik S, Morel, Sandrine, Børte, Sigrid, Johnsen, Marianne Bakke, Brumpton, Ben M, Sandvei, Marie Søfteland, Willer, Cristen J, Hveem, Kristian, Zwart, John-Anker, Verschuren, W M Monique, Friedrich, Christoph M, Hirsch, Sven, Bourcier, Romain, Schilling, Sabine, Dauvillier, Jérôme, Martin, Olivier, Stroke, HUNT All-In, Group, China Kadoorie Biobank Collaborative, Consortium, BioBank Japan Project, Group, ICAN Study, Group, CADISP, Genetics, Haemorrhage, Observational Subarachnoid, Hostettler, Isabel C, Consortium, International Stroke Genetics, Jones, Gregory T, Bown, Matthew J, Ko, Nerissa U, Kim, Helen, Coleman, Jonathan R I, Breen, Gerome, Zaroff, Jonathan G, Klijn, Catharina J M, Malik, Rainer, Alg, Varinder S, Dichgans, Martin, Sargurupremraj, Muralidharan, Tatlisumak, Turgut, Amouyel, Philippe, Debette, Stéphanie, Rinkel, Gabriel J E, Worrall, Bradford B, Pera, Joanna, Slowik, Agnieszka, Gaál-Paavola, Emília I, van Eijk, Kristel R, Niemelä, Mika, Jääskeläinen, Juha E, von Und Zu Fraunberg, Mikael, Lindgren, Antti, Broderick, Joseph P, Werring, David J, Woo, Daniel, Redon, Richard, Bijlenga, Philippe, Kamatani, Yoichiro, Koido, Masaru, Veldink, Jan H, Ruigrok, Ynte M, Bian, Zheng, Chen, Junshi, Chen, Yiping, Clarke, Robert, Collins, Rory, Guo, Yu, Han, Xiao, Hill, Michael, Akiyama, Masato, Liu, Depei, Lv, Jun, Millwood, Iona, Peto, Richard, Sansome, Sam, Walters, Robin, Yang, Xiaoming, Yu, Canqing, Bonner, Stephen, and Walsh, Daniel

Subjects
572: Biochemie, ddc:570, Medizin, Genetics, MEDLINE, Genome-wide association study, Computational biology, Biology, Clinical risk factor, 616: Innere Medizin und Krankheiten
Abstract

In the version of this article initially published, the following statement was missing from the Acknowledgements: “We are grateful to the GenoBiRD core facility (Biogenouest), the Clinical Investigation Center (INSERM CIC1413) and the Center of Biological Resources in Nantes (BB-0033-00040; CHU Nantes, France) for their assistance in managing and genotyping the ICAN and PREGO biobanks. R.R. was supported by the French Regional Council of Pays-de-la-Loire (VaCaRMe program) and the Agence Nationale de la Recherche (ANR-15-CE17-0008-01 to G.L). H.D. and R.B. were supported by the French Ministry of Health (clinical trial NCT02848495 to H.D.), the Genavie Foundation, the Société Française de Radiologie and the Société Française de Neuroradiologie.” The error has been corrected in the HTML and PDF versions of the article. *Lists of authors and their affiliations appear online. Korrektur zu 10.1038/s41588-020-00725-7

Published
2020

7. General sales forecast models for automobile markets and their analysis

Author

Hülsmann, Marco, Borscheid, Detlef, Friedrich, Christoph M., Reith, Dirk, and Publica

Subjects
automobile industry, time series analysis, decision tree, sales forecast, data mining
Abstract

In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its eects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical Data Mining algorithms, whereas the data is composed of absolute and/or relative market-specic exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. Decision Trees are consider ed as the most suitable method in this case, being both accurate and explicable. The German and the US-American automobile market are presented for the evaluation of the forecast models.

Published
2012
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8. The @neurIST project

Author

Iavindrasana, Jimison, Lo Iacono, Luigi, Mueller, Henning, Periz, Ivan, Paul Summers, Wright, Jessica, Friedrich, Christoph M., Dach, Holger, Gattermayer, Tobias, Engelbrecht, Gerhard, Benkner, Siegfried, Hofmann-Apitius, Martin, Dunlop, Robert, Arbona, Antonio, Rajasekaran, Hariharan, Fingberg, Jochen, Chiarini, Alessandro, Moore, Bob, Bijlenga, Philippe, Hose, Rod D., and Frangi, Alexando F.

Subjects
Access to Information, Diagnostic Imaging, Europe, Proteomics, Computer Communication Networks, Databases as Topic, Computer Systems, Medical Informatics Computing, Humans, Genomics, Decision Support Systems, Clinical
Published
2008

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