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Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease. [ABSTRACT FROM AUTHOR]

Abstract: Electronic patient records (EPRs) contain a wealth of patient-related data and capture clinical problem-solving experiences and decisions. Excelicare is such a system which is also a platform for the national generic clinical system in the UK. Objective: This paper presents, ExcelicareCBR, a case-based reasoning (CBR) system which has been developed to complement Excelicare. Objective of this work is to integrate CBR to support clinical decision making by harnessing electronic patient records for clinical experience reuse. Methods: CBR is a proven problem solving methodology in which past solutions are reused to solve new problems. A key challenge that we address in this paper is how to extract and represent a case from an EPR. Using an example from the lung cancer domain we demonstrate our generic case representation approach where Excelicare fields are mapped to case features. Once the case base is populated with cases containing data from the EPRs database a standard weighted k-nearest neighbour algorithm combined with a genetic algorithm based feature weighting mechanism is used for case retrieval and reuse. Conclusions: We conclude that incorporating case authoring functionality and a generic retrieval mechanism were key to successful integration of ExcelicareCBR. This paper also demonstrates how the application of CBR can enable sharing of lessons learned through the retrieval and reuse of EPRs captured as cases in a case base. [Copyright &y& Elsevier]

Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. Cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modelling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This paper explores the application of intelligent data analysis techniques for building reliable models of disease progression from both cross-sectional and longitudinal studies. The aim is to learn disease 'trajectories' from cross-sectional data by building realistic trajectories from healthy patients to those with advanced disease. We focus on exploring whether we can 'calibrate' models learnt from these trajectories with real longitudinal data using Baum-Welch re-estimation so that the dynamic parameters reflect the true underlying processes more closely. We use Kullback-Leibler distance and Wilcoxon rank metrics to assess how calibration improves the models to better reflect the underlying dynamics. [ABSTRACT FROM AUTHOR]

Objective: Patients emerging from cardiac surgery can display varying degrees of cardiovascular instability arising from potentially complex, multi-factorial and interlinked causes. Stabilization and control of the cardiovascular system are currently managed by healthcare experts using experiential knowledge, and, in some centers, manually inputted decision pathway algorithms. This paper describes a clinical trial undertaken to determine the basic functioning of a clinical decision support system (CDSS) designed and constructed by the authors to facilitate the control of the major cardiovascular components in the early post-operative phase. Part II follows Part I's description of the software and simulation testing of the CDSS, and describes the hardware setup of a patient monitoring and CDSS. The system is evaluated on three post-cardiac surgery intensive care patients whom had all undergone cardio-pulmonary bypass., Methods: The study was approved by the Sheffield Teaching Hospitals National Health Service (NHS) Foundation Trust Research Ethics Committee and conducted at the North Trent cardio-thoracic surgical unit and cardiac intensive care unit (CICU), Northern General Hospital, Sheffield (UK). Patients considered as 'very likely' to require active intervention to support the cardiovascular function following routine cardiac surgery were recruited during pre-operative surgical and anesthetic assessment, giving written informed consent when admitted for their operation. These patients underwent routine induction and maintenance of anesthesia by a non-study consultant anesthetist and the operation performed. There were no restrictions placed on the types of invasive monitoring used, on the use of trans-oesophageal echocardiography, drug selection, or the anesthetic agents selected by the clinicians performing the operations. All patients had full, routine invasive and non-invasive monitoring applied, including electrocardiography, central venous and peripheral arterial catheterisation, urine outputs and central temperature. After chest closure the patients were transferred to the CICU, sedated and ventilated, and the study commenced by the study anesthetist (1st author). The patients were in a clinically stable condition when admitted to the unit, and were attended by the treating clinicians until the handover to the study anesthetist occurred. The LiDCOplus (lithium dilution cardiac output) monitor (LiDCO Limited, Flowers Building, Granta Park, Cambridge CB1 6GU, United Kingdom) was calibrated after attachment to the patient's arterial line, and the patient's beat-to-beat hemodynamic data transferred to the host laptop computer. The CDSS graphical interface displays the patient's clinical details and specific cardiovascular data and prompts the anesthetist to input the target ranges for each parameter, and select a suitable advisor frequency. This is the frequency with which the therapeutic advice is displayed on screen with an audible prompt for a control inputs from the anesthetist. In each case this was selected to be 30s. When the study anesthetist agreed with the CDSS advice (administration of fluid, commencing a drug, altering the drug infusion rate) the syringe motif on the "Advisor Infusion Rates" panel of the graphical interface was 'clicked' on and the infusion rate immediately and manually inputted to Graseby 3400 pumps. If any disagreement between the anesthetist and the computer's advice arose, the syringe motif on the "Expert Infusion Rates" panel of the preferred drug was 'clicked' on and the expert's therapeutic decision (e.g. infusion rate) was entered in the corresponding data field and then applied to the pump. During all trials, data was stored for off-line analysis., Results: The CDSS successfully selected suitable drug therapies for each case and advised reasonable and appropriate infusion rates such that the study anesthetist did not have to override the suggested CDSS instructions and infusion rates. Under differing clinical conditions the system was able to maintain clinically appropriate and stable control of the cardiovascular system (CVS), with good profiles under noisy physiological measurements, and was readily able to regain control following transient deterioration of the patient hemodynamic parameters (coughing, or during blood sampling).