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Prostate cancer is awidespread and global disease which affects adultmales - it is said that key causes of the cancer include age, family history, and ethnicity. In this study, the Kaggle prostate cancer dataset, comprising of data from 100 patients with a mixture that both had cancer and did not have cancer, was used alongsidemachine learning predictionmodels for the design of unsupervised and automated intelligent systems for the prediction of prostate cancer. Two intelligent systems were designed and underpinned by unsupervised learning algorithms, namely fuzzy c-means and agglomerative hierarchical clustering, where the various intelligent systems were able to make a prostate cancer prediction with accuracies of over 80% for the various classification metrics, alongside being able to predict an associated stage of the prostate cancer. Both designed intelligent systems offer a complimentary alternative to each other, and their relative merits are discussed in the paper. [ABSTRACT FROM AUTHOR]

Cancers are prevalent worldwide, affecting a substantial amount of the global population, while early and proactive diagnosis of the disease continues to be a global medical challenge. Endometrial cancer represents a gynecological variant which is not only difficult to diagnose but also produces symptoms that are not distinct or exclusive to just the cancer itself. Blood spectroscopy has recently prevailed as a means towards a high-throughput and largely inexpensive method of diagnosing endometrial cancer. Using this method, and with the postprocessing of the accompanying spectra alongside the use of multivariate statistics, an inference can be formed which gives an indication of the presence and extent of the cancer. Previous work in this area has shown that the prediction results for this cancer could be improved with the use of signal decomposition models alongside machine learning prediction models, thus demonstrating the potential appeal of decomposition models in the processing pipeline of the spectroscopy data. As part of this exploratory study, we employ for the first time the use of deep learning, in the form of deep wavelet scattering, for the processing of acquired Fourier transform infrared (FTIR) spectra, which allows for a fully unsupervised decomposition and feature extraction of the resulting spectra, coupled with prediction machines capable of predicting the presence of cancer. The obtained results show that the use of deep learning allows for enhanced predictions of endometrial cancer, whilst allowing for a clinical decision-support platform which carries a greater degree of autonomy and, therein, diagnosis throughput. [ABSTRACT FROM AUTHOR]

Malaria continues to be a major cause of death worldwide, with a broad range of people spread over 90 countries being at risk of contracting the disease, and a significant cause of death in children under the age of 5. Due to this, there continues to be substantial investment towards not just the treatment of the disease, but also a more rapid and accurate means towards its diagnosis. In this work, we look to explore how measurements obtained from the complete blood count (CBC) technique from patients' blood, alongside artificial intelligence (AI) methods, could form an affordable analytical pipeline that could be adopted in hospital settings in both developed and developing countries. As part of this work, we utilize patient blood measurements acquired from paediatric patients from Ghana, West Africa, alongside various configurations of AI models towards distinguishing between malaria vs. non-malaria cases in a sample set comprising over 2000 patients. Class balancing algorithms are utilized to first balance the classes for the various patient groups, followed by the use of AI algorithms to train machine learning models to differentiate between a malaria vs. a non-malaria patient. The results showcased a generally high prediction accuracy, especially in the case of models with nonlinear decision boundaries, therein showing how the proposed analytic pipeline can serve as a high-throughput approach towards tackling the malaria epidemic from a diagnostics perspective and ultimately enhancing patient care strategies. [ABSTRACT FROM AUTHOR]

The effective prediction of preterm labor continues to be a topic of interest for research within pregnancy medicine, where uterine muscle contraction signals have shown to be insightful to predict a potential preterm birth. Magnetomyography (MMG) is a physiological-measurement-based tool which measures the orthogonal offset of bioelectrical manifestations from uterine contractions and may serve to predict potential premature deliveries with an enhanced accuracy. The decoding of the physiological signal is an area of substantial research where classical signal processing approaches and metaheuristics optimization routines have been utilized in the postprocessing and decomposition of MMG signals. This work requires a degree of expert knowledge and an understanding of tuning and parameter initialization. As a stride towards creating a more automated clinical decision support platform for predictions of preterm labor, we employ the use of a deep wavelet scattering (DWS) model. This methodology allows for a deep multiresolution analysis alongside unsupervised feature learning for the postprocessing of candidate MMG signals. DWS is combined with select pattern-recognition-based prediction machines in order to assemble a clinical decision pipeline for the prediction of the states of various pregnancies, with a greater degree of machine intelligence. The patient cohort consisted of a multi-ethnic demographic population composed of preterm and term pregnancies, where births occurred both under and over 48 h after labor commenced. Contrasting results were found between the various methods from the literature and DWS using the logistic regression algorithm. It was seen that DWS produced a slightly lower accuracy in comparison, as a trade-off for its streamlined unsupervised feature extraction process. Further work will now involve the application of various other machine learning methods in an attempt to assess and identify the most appropriate machine learning method with DWS that proves to be the most accurate. [ABSTRACT FROM AUTHOR]

The ranking and evaluation of a surgeon's surgical skills is an important factor in order to be able to appropriately assign patient cases according to the necessary level of surgeon competence in addition to helping us in the process of pinpointing the specific clinicians within the surgical cohort who require further developmental training. One of the more frequent means of surgical skills evaluation is through a qualitative assessment of a surgeon's portfolio alongside other supporting pieces of information, a process which is rather subjective. The contribution presented as part of this paper involves the use of a set of Delsys Trigno EMG wearable sensors, which track and record the muscular activation patterns of a surgeon during a surgical procedure, alongside computationally driven artificial intelligence (AI) methods towards the differentiation and ranking of the surgical skills of a clinician in a quantitative fashion. The participants in the research involved novice-level surgeons, intermediate-level surgeons and expert-level surgeons in various simulated surgical cases. A comparison of different signal processing approaches has shown that the proposed approach can prove beneficial in monitoring and differentiating the skillsets of various surgeons for various kinds of surgical cases. The presented method could also be used to track the evolution of the surgical competencies of various trainee surgeons at various stages during their training. [ABSTRACT FROM AUTHOR]

Background: Cardiorespiratory coupling (CRC) is a physiological phenomenon that reflects the mutual interaction between the cardiac and respiratory control systems. It is mainly associated with efferent vagal activity from the central autonomic network. Few studies have explored the autonomic changes of CRC in preeclampsia, a critical obstetric complication related to possible autonomic dysfunctions and inflammatory disturbances. This study examined the autonomic mechanisms of CRC in women with severe and moderate preeclampsia and healthy controls by applying nonlinear methods based on information theory, such as mutual information (MI) and Renyi's mutual information (RMI) and the linear and nonlinear analysis of the Pulse-Respiration Quotient (PRQ). Methods: We studied three groups of parturient women in the third trimester of pregnancy with a clinical diagnosis of preeclampsia without severe symptoms (P, 38.5 ± 1.4 weeks of pregnancy, n=19), preeclampsia with severe symptoms (SP, 37.5 ± 0.9 weeks of pregnancy, n=22), and normotensive control women (C, 39.1 ± 1.3 weeks of pregnancy, n=20). 10-minutes of abdominal electrocardiograms (ECG) and respiratory signals (RESP) were recorded in all the participants. Subsequently, we obtained the maternal beat-to-beat (RR) and breath-tobreath (BB) time series from ECG and RESP, respectively. The CRC between RR and BB was quantified by nonlinear methods based on information theory, such as MI and RMI, along with the analysis of the novel index of PRQ. Subsequently, we computed the mean PRQ (mPRQ) and the normalized permutation entropy (nPermEn_PRQ) from the PRQ time series generated from BB and RR. In addition, we examined the vagal activity in the three groups by the logarithm of the median of the distribution of the absolute values of successive RR differences (logRSA). Results: The MI and RMI values were significantly lower (p<0.05) in the preeclamptic groups compared to the control group. However, no significant differences were found between the preeclamptic groups. The logRSA and nPermEn_PRQ indices were significantly lower (p<0.05) in SP compared to C and P. Conclusion: Our data suggest that parturient women with severe and mild preeclampsia may manifest an altered cardiorespiratory coupling compared with normotensive control women. Disrupted CRC in severe preeclampsia could be associated with vagal withdrawal and less complex cardiorespiratory dynamics. The difference in vagal activity between the preeclamptic groups may suggest a further reduction in vagal activity associated with the severity of the disease. [ABSTRACT FROM AUTHOR]