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In this work, automated abnormality detection using keypoint information from Speeded-Up Robust feature (SURF) and Scale Invariant Feature Transform (SIFT) descriptors in chest Radiographic (CR) images is investigated and compared. Computerized image analysis using artificial intelligence is crucial to detect subtle and non-specific alterations of Tuberculosis (TB). For this, the healthy and TB CRs are subjected to lung field segmentation. SURF and SIFT keypoints are extracted from the segmented lung images. Statistical features from keypoints, its scale and orientation are computed. Discrimination of TB from healthy is performed using SVM. Results show that the SURF and SIFT methods are able to extract local keypoint information in CRs. Linear SVM is found to perform better with precision of 88.9% and AUC of 91% in TB detection for combined features. Hence, the application of keypoint techniques is found to have clinical relevance in the automated screening of non-specific TB abnormalities using CRs.In this, study, we have investigated to identify the muscle fatigue using spatial maps of High-Density Electromyography (HDEMG). The experiment involves subjects performing plantar flexion at 40% maximum voluntary contraction until fatigue. During the experiment, HDEMG signal was recorded from the tibialis anterior muscle. The monopolar and bipolar spatial intensity maps were extracted from the HDEMG signal. The random forest classifier with different tree configurations was tested to distinguish nonfatigue and fatigue condition. The results indicate that selected electrodes from the differential intensity map results in an accuracy of 83.3% with the number of trees set at 17. This method of spatial analysis of HDEMG signals may be extended to assess fatigue in real life scenarios.i2b2 data-warehouse could be a useful tool to support the enrollment phase of clinical studies. The aim of this work is to evaluate its performance on two clinical trials. We developed also an i2b2 extension to help in suggesting eligible patients for a study. The work showed good results in terms of ability to implement inclusion/exclusion criteria, but also in terms of identified patients actually enrolled and high number of patients suggested as potentially enrollable.This paper presents a scoping review of federated learning for the Internet of Medical Things (IoMT) and demonstrates the limited amount of research work in an area which has potential to improve patient care. Federated Learning and IoMT - as standalone technologies - have already proved to be highly disruptive but there is a need for further research to apply federated learning to the IoMT.The decisions derived from AI-based clinical decision support systems should be explainable and transparent so that the healthcare professionals can understand the rationale behind the predictions. To improve the explanations, knowledge graphs are a well-suited choice to be integrated into eXplainable AI. In this paper, we introduce a knowledge graph-based explainable framework for AI-based clinical decision support systems to increase their level of explainability.The study aims at generating initial and directional insights in the applicability of conditional recurrent generative adversarial nets for the imputation and forecasting of medical time series data. Our experiment with blood pressure series showed that a generative recurrent autoencoder exhibits significant individual learning progress but needs further tuning to benefit from joint training.Tumor-associated autoantibodies can be used as biomarkers for detecting different types of cancers. Our objective was to use machine learning techniques to predict high-risk cases of oral squamous cell carcinoma (OSCC) with salivary autoantibodies. The optimal model was using eXtreme Gradient Boosting (XGBoost) with the area under the receiver operating characteristic curve (AUC) of 0.765 (p less then 0.01). Thus, applying machine learning model to early detect high-risk cases of OSCC could assist the clinic treatment and prognosis.There is a demand for a pseudonymization service by a Trusted Third Party (TTP), that features clientside pseudonymization. We propose a system using modern web technology, which requires no installation but can handle data preprocessing and pseudonymization safely on the client.The aim of this work is to shortly provide the public with an overview about fake news and artificial intelligence (AI) technology. Especially in our days, where there is a high speed of spreading news, the impact of fake news on public health is crucial and the development of valid and effective means of technology to support the provision of safe and trustworthy information about public health issues is vital. The role of informatics in health area is profoundly important and AI in public health, so people will be able to distinguish the genuine information from the fake one.We developed a clinical named entity recognition model to predict clinical relevance of pharmacist interventions (PIs) by identifying and labelling expressions from unstructured comments of PIs. Three labels, drug, kidney and dosage, had a great inter-annotator agreement (>60%) and could be used as reference labelization. These labels also showed a high precision (>70%) and a variable recall (50-90 %).The problem list is a key facet of the digital patient record that has historically been difficult to curate. This paper presents an implementation of a contextual problem list using openEHR. It describes the modelling approach, key model elements, and how these are assembled to underpin a Problem Oriented Medical Record. Finally, it discusses issues associated with how problem lists may be used.The Portal of Medical Data Models has been developed since 2011 by the University of Münster. Its main goals are transparency, standardization and secondary use of medical metadata. check details Via two online surveys feedback from stakeholders of German health research was collected regarding the portal's contents. The surveys confirmed great interest in secondary use of medical forms.Recognition of the emotions demonstrated by human beings plays a crucial role in healthcare and human-machine interface. This paper reports an attempt to classify emotions using a spectral feature from facial electromyography (facial EMG) signals in the valence affective dimension. For this purpose, the facial EMG signals are obtained from the DEAP dataset. The signals are subjected to Short-Time Fourier Transform, and the peak frequency values are extracted from the signal in intervals of one second. Support vector machine (SVM) classifier is used for the classification of the features extracted. The extracted feature can classify the signals in the valence dimension with an accuracy of 61.37%. The proposed feature could be used as an added feature for emotion recognition, and this method of analysis could be extended to myoelectric control applications.
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