Feature selection for gesture recognition in Internet-of-Things for healthcare

Internet of Things is rapidly spreading across several fields, including healthcare, posing relevant questions related to communication capabilities, energy efficiency and sensors unobtrusiveness. Particularly, in the context of recognition of gestures, e.g., grasping of different objects, brain and muscular activity could be simultaneously recorded via EEG and EMG, respectively, and analyzed to identify the gesture that is being accomplished, and the quality of its performance. This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features. This, in turn, gives the opportunity to simplify the recording setup to minimize the data traffic over the communication network, including Internet, and provide physiologically significant features for medical interpretation. The algorithm robustness is ensured both by consensus clustering as a feature selection strategy, and by nested cross-validation scheme to evaluate its classification performance

The predicted probability of live birth in In Vitro Fertilization varies during important stages throughout the treatment: analysis of 114,882 first cycles.

RESEARCH QUESTION How much the variability in patients' response during in vitro fertilization (IVF) may add to the initial predicted prognosis based only on patients' basal characteristics? DESIGN Anonymous data were obtained from the Human Fertilization and Embryology Authority (HFEA). Data involving 114,882 stimulated fresh IVF cycles were retrospectively analyzed. Logistic regression was used to develop the models. RESULTS Prediction of live birth was feasible with moderate accuracy in all of the three models; discrimination of the model based only on basal patients' characteristics (AUROC 0.61) was markedly improved adding information of number of embryos (AUROC 0.65) and, mostly, number of oocytes (AUROC 0.66). CONCLUSIONS The addition to prediction models of parameters such as the number of embryos obtained and especially the number of oocytes retrieved can statistically significantly improve the overall prediction of live birth probabilities when based on only basal patients' characteristics. This seems to be particularly true for women after the first IVF cycle. Since ovarian response affects the probability of live birth in IVF, it is highly recommended to add markers of ovarian response to models based on basal characteristics to increase their predictive ability

The complex relationship between female age and embryo euploidy

BACKGROUND: Female age is the strongest predictor of embryo chromosomal abnormalities and has a nonlinear relationship with the blastocyst euploidy rate: with advancing age there is an acceleration in the reduction of blastocyst euploidy. Aneuploidy was found to significantly increase with maternal age from 30% in embryos from young women to 70% in women older than 40 years old. The association seems mainly due to chromosomal abnormalities occurring in the oocyte. We aimed to elaborate a model for the blastocyst euploid rate for patients undergoing in-vitro fertilization/intra cytoplasmic sperm injection (IVF/ICSI) cycles using advanced machine learning techniques.METHODS: This was a retrospective analysis of IVF/ICSI cycles performed from 2014 to 2016. In total, data of 3879 blastocysts were collected for the analysis. Patients underwent PGT-Aneuploidy analysis (PGT-A) at the Center for Reproductive Medicine of European Hospital (Rome, Italy) have been included in the analysis. The method involved wholegenome amplification followed by array comparative genome hybridization. To model the rate of euploid blastocysts. the data were split into a train set (used to fit and calibrate the models) and a test set (used to assess models' predictive performance). Three different models were calibrated: a classical linear regression; a gradient boosted tree (GBT) machine learning model; a model belonging to the generalized additive models (GAM).RESULTS: The present study confirms that female age, which is the strongest predictor of embryo chromosomal abnormalities, and blastocyst euploidy rate have a nonlinear relationship, well depicted by the GBT and the GAM models. According to this model, the rate of reduction in the percentage of euploid blastocysts increases with age: the yearly relative variation is -10% at the age of 37 and -30% at the age of 45. Other factors including male age, female and male Body Mass Index. fertilization rate and ovarian reserve may only marginally impact on embryo euploidy rateCONCLUSIONS: Female age is the strongest predictor of embryo chromosomal abnormalities and has a non-linear relationship with the blastocyst euploidy rate. Other factors related to both the male and female subjects may only minimally affect this outcome

Red blood cell alloimmunisation in transfusion-dependent thalassaemia: A systematic review

Background. Chronic red blood cell transfusion is the first-line treatment for severe forms of thalassaemia. This therapy is, however, hampered by a number of adverse effects, including red blood cell alloimmunisation. The aim of this systematic review was to collect the current literature data on erythrocyte alloimmunisation. Materials and methods. We performed a systematic search of the literature which identified 41 cohort studies involving 9,256 patients. Results. The prevalence of erythrocyte alloimmunisation was 11.4% (95% CI: 9.3-13.9%) with a higher rate of alloimmunisation against antigens of the Rh (52.4%) and Kell (25.6%) systems. Overall, alloantibodies against antigens belonging to the Rh and Kell systems accounted for 78% of the cases. A higher prevalence of red blood cell alloimmunisation was found in patients with thalassaemia intermedia compared to that among patients with thalassaemia major (15.5 vs 12.8%). Discussion. Matching transfusion-dependent thalassaemia patients and red blood cell units for Rh and Kell antigens should be able to reduce the risk of red blood cell alloimmunisation by about 80%

Early rehabilitation for severe acquired brain injury in intensive care unit: multicenter observational study

The increased survival after a severe acquired brain injury (sABI) raise the problem of making most effective the treatments in Intensive Care Unit (ICU)/Neurointensive Care Unit (NICU), also integrating rehabilitation care. Despite previous studies reported that early mobilization in ICU was effective in preventing complications and reducing hospital stay, few studies addressed the rehabilitative management of sABI patients in ICU/NICU