Decision making with fair ranking

Abstract and Figures Ranking is a responsible process because it involves working with sensitive attributes that can discriminate alternatives. Due to the availability of a large amount of data for automated processing, ranking is increasingly in use in decision making. Therefore, concepts of algorithmic fairness in the field of classification in machine learning find their place in fair ranking methods. This paper provides an overview of fair ranking terms, fair ranking challenges, and fair ranking algorithms from the state-of-the-art literature

Effect of argon plasma abutment activation on soft tissue healing: RCT with histological assessment.

OBJECTIVE To assess the peri-implant soft tissue profiles between argon plasma treatment (PT) and non-treated (NPT) healing abutments by comparing clinical and histological parameters 2 months following abutment placement. MATERIALS AND METHODS Thirty participants were randomly assigned to argon-plasma treatment abutments group (PT) or non-treated abutments (NPT) group. Two months after healing abutment placement, soft peri-implant tissues and abutment were harvested, and histological and clinical parameters including plaque index, bleeding on probing, and keratinized mucosa diameter (KM) were assessed. Specialized stainings (hematoxylin-eosin and picrocirious red) coupled with immunohistochemistry (vimentin, collagen, and CK10) were performed to assess soft tissue inflammation and healing, and the collagen content keratinization. In addition to standard statistical methods, machine learning algorithms were applied for advanced soft tissue profiling between the test and control groups. RESULTS PT group showed lower plaque accumulation and inflammation grade (6.71% vs. 13.25%, respectively; p-value 0.02), and more advanced connective tissue healing and integration compared to NPT (31.77% vs. 23.3%, respectively; p = 0.009). In the control group, more expressed keratinization was found compared to the PT group, showing significantly higher CK10 (>47.5%). No differences in KM were found between the groups. SIGNIFICANCE PT seems to be a promising protocol for guided peri-implant soft tissue morphogenesis reducing plaque accumulation and inflammation, and stimulating collagen and soft tissue but without effects on epithelial tissues and keratinization

Diagnostic value of VEGF in peri-implantitis and its correlation with titanium particles: A controlled clinical study.

OBJECTIVES VEGF is prototypic marker of neovascularization, repeatedly proposed as intrinsic characteristic of peri-implantitis. This study aimed to assess pattern of VEGF in peri-implantitis, its correlation with titanium particles (TPs) and capacity as respective biomarker. MATERIAL AND METHODS Pathological specificity of VEGF was assessed in peri-implant granulations using immunohistochemistry, periodontal granulations represented Ti-free positive controls. VEGF was correlated to TPs, identified using scanning electron microscopy coupled with dispersive x-ray spectrometry. Diagnostic accuracy, sensitivity and specificity of VEGF were estimated in PICF specimens from peri-implantitis, peri-implant mucositis (PIM) and healthy peri-implant tissues (HI) using machine learning algorithms. RESULTS Peri-implantitis exhibited rich neovascular network with expressed density in contact zones toward neutrophil infiltrates without specific pattern variations around TPs, identified in all peri-implantitis specimens (mean particle size 8.9 ± 24.8 µm2; Ti-mass (%) 0.380 ± 0.163). VEGF was significantly more expressed in peri-implantitis (47,065 ± 24.2) compared to periodontitis (31,14 ± 9.15), and positively correlated with its soluble concentrations in PICF (p = 0.01). VEGF was positively correlated to all clinical endpoints and significantly increased in peri-implantitis compared to both PIM and HI, but despite high specificity (96%), its overall diagnostic capacity was average. Two patient clusters were identified in peri-implantitis, one with 8-fold higher VEGF values compared to HI, and second with lower values comparable to PIM. SIGNIFICANCE VEGF accurately reflects neovascularization in peri-implantitis that was expressed in contact zones toward implant surface without specific histopathological patter variation around TPs. VEGF answered requests for biomarker of peri-implantitis but further research is necessary to decrypt its exact underlying cause

IDPpi:Protein-protein interaction analyses of human intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency. © 2018 The Author(s)

Towards a Collaborative Platform for Advanced Meta-Learning in Health care Predictive Analytics

Modern medical research and clinical practice are more dependent than ever on multi-factorial data sets originating from various sources, such as medical imaging, DNA analysis, patient health records and contextual factors. This data drives research, facilitates correct diagnoses and ultimately helps to develop and select the appropriate treatments. The volume and impact of this data has increased tremendously through technological developments such as highthroughput genomics and high-resolution medical imaging techniques. Additionally, the availability and popularity of different wearable health care devices has allowed the collection and monitoring of fine-grained personal health care data. The fusion and combination of these heterogeneous data sources has already led to many breakthroughs in health research and shows high potential for the development of methods that will push current reactive practices towards predictive, personalized and preventive health care. This potential is recognized and has led to the development of many platforms for the collection and statistical analysis of health care data (e.g. Apple Health, Microsoft Health Vault, Oracle Health Management, Philips HealthSuite, and EMC Health care Analytics). However, the heterogeneity of the data, privacy concerns, and the complexity and multiplicity of health care processes (e.g. diagnoses, therapy control, and risk prediction) creates significant challenges for data fusion, algorithm selection and tuning. These challenges leave a gap between the actual and the potential data usage in health care, which prevents a paradigm shift from delayed generalized medicine to predictive personalized medicine In this work we present an extensions of the OpenML platform that will be addressed in our future work in order to meet the needs of meta-learning in health care predictive analytics: privacy preserving sharing of data, workflows and evaluations, reproducibility of the results, and rich meta-data spaces about both data and algorithms. OpenML.org [2] is a collaboration platform which is designed to organize datasets, machine learning workflows, models and their evaluations. Currently, OpenML is not fully distributed but can be installed on local instances which can communicate with the main OpenML database using mirroring techniques. The downside of this approach is that code (machine learning workflows), datasets, experiments (models and evaluations) are physically kept on local instances, so users cannot communicate and share. We plan to turn OpenML into a fully distributed machine learning platform, which will be accessible from different data mining and machine learning platforms such as RapidMiner, R, WEKA, KNIME, or similar. Such a distributed platform would allow the ease of sharing data and knowledge. Currently, regulations and privacy concerns often prevent hospitals to learn from each other's approaches (e.g. machine learning workflows), reproduce work done by others (data version control, preprocessing and statistical analysis), and build models collaboratively. On the other hand, meta-data such as type of the hospital, percentage of readmitted patients or indicator of emergency treatment, as well as the learned models and their evaluations can be shared and have great potential for the development of a cutting edge meta-learning system for ranking, selection and tuning of machine learning algorithms. The success of meta-learning systems is greatly influenced by the size of problem (data) and algorithm spaces, but also by the quality of the data and algorithm descriptions (meta-features). Thus, we plan to employ domain knowledge provided by expert and formal sources (e.g. ontologies) in order to extend the meta-feature space for meta-learning in health care applications. For example, in meta-analyses of gene expression microarray data, the type of chip is very important in predicting algorithm performance. Further, in fused data sources it would be useful to know which type of data contributed to the performance (electronic health records, laboratory tests, data from wearables etc.). In contrast to data descriptions, algorithm descriptions are much less analyzed and applied in the meta-learning process. Recent result

How frequent does peri-implantitis occur? A systematic review and meta-analysis.

OBJECTIVES The objective of this study is to estimate the overall prevalence of peri-implantitis (PI) and the effect of different study designs, function times, and implant surfaces on prevalence rate reported by the studies adhering to the case definition of Sanz & Chapple 2012. MATERIAL AND METHODS Following electronic and manual searches of the literature published up to February 2016, data were extracted from the studies fitting the study criteria. Meta-analysis was performed for estimation of overall prevalence of PI while the effects of the study design, function time, and implant surface type on prevalence rate were investigated using meta-regression method. RESULTS Twenty-nine articles were included in this study. The prevalence rate in all subset meta-analyses was always higher at patient level when compared to the prevalence rate at the implant level. Prevalence of PI was 18.5% at the patient level and 12.8% at the implant level. Meta-regression analysis did not identify any association for different study designs and function times while it was demonstrated the significant association between moderately rough surfaces with lower prevalence rate of PI (p = 0.011). CONCLUSIONS The prevalence rate of PI remains highly variable even following restriction to the clinical case definition and it seems to be affected by local factors such as implant surface characteristics. The identification of adjuvant diagnostic markers seems necessary for more accurate disease classification. CLINICAL RELEVANCE The occurrence of PI is affected by local factors such as implant surface characteristics hence the careful assessment of the local factors should be performed within treatment planning

Distinguishing predictive profiles for patient-based risk assessment and diagnostics of plaque induced, surgically and prosthetically triggered peri-implantitis

International audienceObjective: To investigate whether specific predictive profiles for patient-based risk assessment/ diagnostics can be applied in different subtypes of peri-implantitis.Materials and methods: This study included patients with at least two implants (one or more presenting signs of peri-implantitis). Anamnestic, clinical, and implant-related parameters were collected and scored into a single database. Dental implant was chosen as the unit of analysis, and a complete screening protocol was established. The implants affected by peri-implantitis were then clustered into three subtypes in relation to the identified triggering factor: purely plaque-induced or prosthetically or surgically triggered peri-implantitis. Statistical analyses were performed to compare the characteristics and risk factors between peri-implantitis and healthy implants, as well as to compare clinical parameters and distribution of risk factors between plaque, prosthetically and surgically triggered peri-implantitis. The predictive profiles for subtypes of peri-implantitis were estimated using data mining tools including regression methods and C4.5 decision trees.Results: A total of 926 patients previously treated with 2812 dental implants were screened for eligibility. Fifty-six patients (6.04%) with 332 implants (4.44%) met the study criteria. Data from 125 peri-implantitis and 207 healthy implants were therefore analyzed and included in the statistical analysis. Within peri-implantitis group, 51 were classified as surgically triggered (40.8%), 38 as prosthetically triggered (30.4%), and 36 as plaque-induced (28.8%) peri-implantitis. For peri-implantitis, 51 were associated with surgical risk factor (40.8%), 38 with prosthetic risk factor (30.4%), 36 with purely plaque-induced risk factor (28.8%). The variables identified as predictors of peri-implantitis were female sex (OR = 1.60), malpositioning (OR = 48.2), overloading (OR = 18.70), and bone reconstruction (OR = 2.35). The predictive model showed 82.35% of accuracy and identified distinguishing predictive profiles for plaque, prosthetically and surgically triggered peri-implantitis. The model was in accordance with the results of risk analysis being the external validation for model accuracy.Conclusions: It can be concluded that plaque induced and prosthetically and surgically triggered peri-implantitis are different entities associated with distinguishing predictive profiles; hence, the appropriate causal treatment approach remains necessary. The advanced data mining model developed in this study seems to be a promising tool for diagnostics of peri-implantitis subtypes

Analysis of obese patients' medical conditions in the pre and postoperative periods of bariatric surgery

ABSTRACT Objective: to compare the clinical conditions of obese patients in the pre and postoperative period of bariatric surgery. Methods: we carried out a descriptive, retrospective, quantitative study by consulting the charts of 134 patients who underwent bariatric surgery in the period from 2009 to 2014. We collected the data between September and November 2015. We performed a descriptive statistical analysis and comparative analysis of anthropometric, metabolic, biochemical and clinical variables, considering six months before and after surgery. Results: the majority of the patients were female (91.8%), with a higher prevalence (35%) in the age group 18-29 years old, complete high-school education (65.6%) and grade III obesity (60.4%). Six months after surgery, weight and lipid profile reduction were significant in both genders, but the impact on biochemical, anthropometric, metabolic and clinical parameters was significant only in female subjects, with a reduction in morbidities associated with obesity, such as arterial hypertension, diabetes mellitus, dyslipidemia and metabolic syndrome and in the use of drugs to control them. Conclusion: bariatric surgery was effective in weight loss, with improvements in anthropometric, metabolic and biochemical parameters and in the reduction of morbidities associated with obesity