Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

Development of a 3D Collagen Model for the In Vitro Evaluation of Magnetic-assisted Osteogenesis

Abstract Magnetic stimulation has been applied to bone regeneration, however, the cellular and molecular mechanisms of repair still require a better understanding. A three-dimensional (3D) collagen model was developed using plastic compression, which produces dense, cellular, mechanically strong native collagen structures. Osteoblast cells (MG-63) and magnetic iron oxide nanoparticles (IONPs) were incorporated into collagen gels to produce a range of cell-laden models. A magnetic bio-reactor to support cell growth under static magnetic fields (SMFs) was designed and fabricated by 3D printing. The influences of SMFs on cell proliferation, differentiation, extracellular matrix production, mineralisation and gene expression were evaluated. Polymerase chain reaction (PCR) further determined the effects of SMFs on the expression of runt-related transcription factor 2 (Runx2), osteonectin (ON), and bone morphogenic proteins 2 and 4 (BMP-2 and BMP-4). Results demonstrate that SMFs, IONPs and the collagen matrix can stimulate the proliferation, alkaline phosphatase production and mineralisation of MG-63 cells, by influencing matrix/cell interactions and encouraging the expression of Runx2, ON, BMP-2 and BMP-4. Therefore, the collagen model developed here not only offers a novel 3D bone model to better understand the effect of magnetic stimulation on osteogenesis, but also paves the way for further applications in tissue engineering and regenerative medicine

Prevention and management of pain after endodontic treatment

Aim. Medical therapy optimization for the prevention and management of pain after endodontic treatment.Materials and methods. A clinical study, in which 68 patients with sequelae of dental caries were examined. The following methods were used:clinical method, roentgenological method, pharmacoepidemiological method, expert judgements, mathematical statistics.Results. A spectrum of analgesics for self-medication of dental pain included 8 medications; 92.6% of which are nonsteroidal anti-inflammatory drugs. Pathology of gastritis in the past medical history was identified in 39.7 ± 5.9% of the patients. Pain was recorded in 79.4 ± 5.4% of the patients. After having finished the treatment analgesic was prescribed in 42.6 ± 4.2% of the cases. Analgesics were taken by 73.5 ± 5.4% of the patients with postendodontic pain. The connection between the analgesic's “strength” and the subjective assessment of its efficacy was identified: r = +0.31 p = 0.035.Conclusions. In most cases pain severity before and after endodontic treatment can be evaluated as moderate. Ketorolac (51,9%) is used as a preferred medication for self-help by the patients with dental pain. Ibuprofen (56,4%) and ketorolac (33,3%) are prescribed by doctors as an analgesic after endodontic treatment in most cases.However, patients often do not follow doctors' orders and choose other nonsteroidal anti-inflammatory drugs as a selfmedicating for preventing the pain. A prescription pattern of analgesic drugs for the prevention and management of pain after endodontic treatment is introduced: for moderate pain - ibuprofen or paracetamol, for severe pain - ketoprofen for the patients with somatic system disorder or ketorolac for the patients without somatic symptom disorder