Quantifying The Effects of Biomarkers and Comorbidities in Predicting SARS Cov-2 Associated Mortality in Hospitalized Patients in Mexico

In this retrospective quasi-experimental, cohort study, the biomarkers, demographics, and clinical characteristics of the adult inpatients with laboratory-confirmed COVID-19 from Hospital Regional 110 (Guadalajara, Mexico) were analyzed who were hospitalized over the year 2020, between April 15 (i.e. when the first patient was admitted) to December 31 and had a definite outcome (discharged or dead), to establish the most important variables for the models. In this study, 5 different Classifiers were used: Random Forest, Support Vector Machine, XGBoost, Naïves Bayes, and Symbolic Classifier to classify the outcome of the patients and also to quantify the effect of biomarkers and comorbidities in predicting SARS-CoV-2 positive associated mortality in hospitalized patients. Also, the Symbolic Transofmer was implemented to try to improve the performance of our model. As the dataset includes a big percentage of missing values, we proposed two models, one excluding the missing values and the other including all the missing values. The Random Forest was implemented to obtain the variable importance, and also to the capacity of the model to handle the missing values. The metrics ROC AUC and Accuracy were used to train the models, along with the Bayesian Optimization to tune the hyperparameters and to measure the performance

On the development of intelligent medical systems for pre-operative anaesthesia assessment

This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo

Aerospace Medicine and Biology: Cumulative index, 1979

This publication is a cumulative index to the abstracts contained in the Supplements 190 through 201 of 'Aerospace Medicine and Biology: A Continuing Bibliography.' It includes three indexes-subject, personal author, and corporate source

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 144

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in July 1975

Developing novel fluorescent probe for peroxynitrite: implication for understanding the roles of peroxynitrite and drug discovery in cerebral ischemia reperfusion injury

Session 7 - Oral PresentationsSTUDY GOAL: Peroxynitrite (ONOO‐) is a cytotoxic factor. As its short lifetime, ONOO‐ is hard to be detected in biological systems. This study aims to develop novel probe for detecting ONOO‐ and understand the roles of ONOO‐ in ischemic brains and drug discovery ABSTRACT: MitoPN‐1 was found to be a ONOO‐ specific probe with no toxicity. With MitoPN‐1, we studied the roles of ONOO‐ in hypoxic neuronal cells in vitro and MCAO …postprin