Design and conceptual proposal of an intelligent clinical decision support system for the diagnosis of suspicious obstructive sleep apnea patients from health profile

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient’s health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8–0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.Xunta de Galicia | Ref. ED481A-2020/03

Expert System with an Embedded Imaging Module for Diagnosing Lung Diseases

Lung diseases are one of the major causes of suffering and death in the world. Improved survival rate could be obtained if the diseases can be detected at its early stage. Specialist doctors with the expertise and experience to interpret medical images and diagnose complex lung diseases are scarce. In this work, a rule-based expert system with an embedded imaging module is developed to assist the general physicians in hospitals and clinics to diagnose lung diseases whenever the services of specialist doctors are not available. The rule-based expert system contains a large knowledge base of data from various categories such as patient's personal and medical history, clinical symptoms, clinical test results and radiological information. An imaging module is integrated into the expert system for the enhancement of chest X-Ray images. The goal of this module is to enhance the chest X-Ray images so that it can provide details similar to more expensive methods such as MRl and CT scan. A new algorithm which is a modified morphological grayscale top hat transform is introduced to increase the visibility of lung nodules in chest X-Rays. Fuzzy inference technique is used to predict the probability of malignancy of the nodules. The output generated by the expert system was compared with the diagnosis made by the specialist doctors. The system is able to produce results\ud which are similar to the diagnosis made by the doctors and is acceptable by clinical standards

PREDICTION OF SEPSIS DISEASE BY ARTIFICIAL NEURAL NETWORKS

Sepsis is a fatal condition, which affects at least 26 million people in the world every year that is resulted by an infection. For every 100,000 people, sepsis is seen in 149-240 of them and it has a mortality rate of 30%. The presence of infection in the patient is determined in order to diagnose the sepsis disease. Organ dysfunctions associated with an infection is diagnosed as sepsis. With the increased usage of artificial intelligence in the field of medicine, the early prediction and treatment of many diseases are provided with these methods. Considering the learning, reasoning and decision making abilities of artificial neural networks, which are the sub field of artificial intelligence are inferred to be used in predicting early stages of sepsis disease and determining the sepsis level is assessed. In this study, it is aimed to help sepsis diagnosis by using multi-layered artificial neural network.In construction of artificial neural network model, feed forward back propagation network structure and Levenberg-Marquardt training algorithm were used. The input and output variables of the model were the parameters which doctors use to diagnose the sepsis disease and determine the level of sepsis. The proposed method aims to provide an alternative prediction model for the early detection of sepsis disease

Optimisation of Patient Monitor Alarm Settings Using Annotated Hospital Data

Alarms are a key functionality of any clinical patient monitoring system. When developing a new system, alarm behaviour must be tuned towards maximizing patient safety from all points of view. False alarms are a major problem in patient wards, and produce an effect called alarm stress, which may desensitise care staff and may endanger patients. On the other hand, systems must be sensitive enough to detect any clinically relevant alarm situation. The purpose of this thesis is to study and optimise the alarm behaviour of a patient monitoring system. Annotated monitoring data from hospital tests is used as a reference when tuning system parameters. The platform can be used to rerun hospital test cases in the development environment and produce results. The goal is to have a system that minimises false alarms and does not give false negatives. In this way patient safety is guaranteed from both ends; clinically relevant situations are detected, but care staff is not desensitised by too many false alarms. Main results include optimised alarm configurations for the monitoring system, and information about the subjectivity of alarm relevance classification in a clinical monitoring context

Integration of the Wang & Mendel algorithm into the application of Fuzzy expert systems to intelligent clinical decision support systems

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture.Xunta de Galicia | Ref. ED481A-2020/03

14-02 Developing Public Health Performance Measures to Capture the Effects of Transportation Facilities on Multiple Public Health Outcomes

Increasingly, federal transportation and public health agencies are working together to identify transportation investments that improve public health. Investments in transportation infrastructure represent one method to utilize transportation to improve public health outcomes. The ideal transportation investment is one that not only provides safe access for pedestrians, bicyclists, motorists and transit riders, but it also promotes more utilitarian or recreational trips for walking and biking in an environment of safe air quality. However, public health objectives can be at conflict when designing transportation infrastructure to support active commuting. For example, infrastructure investments may be made that promote physical activity through utilitarian commuting, yet at the same time, the investment may be made in an area that is characterized by poor air quality or creates an unsafe condition. The purpose of the research is to identify potential performance measures that can foster improved decision making around these investments. The key research contribution is the development of performance measures that can be used in the field to evaluate multiple public health concerns and improve decision making. Secondly, it advances strategies to effectively capture the dimension of safety and physical activity in a manner that considers the conditions under which pedestrian and bicycling activity is likely to increase. The objectives of the project are accomplished through the use and integration of multiple methods, including student-based project learning, expert surveys, content analysis and quantitative statistical techniques

Artificial intelligence and statistical techniques to predict probability of injury survival

The aim of this study is to design, develop and evaluate artificial intelligence and statistical techniques to predict the probability of survival in traumas using knowledge acquired from a database of confirmed traumas outcomes (survivors and not survivors). Trauma in this study refers to body injuries from accidents or other means. Quantifying the effects of traumas on individuals is challenging as they have many forms, affect different organs, differ in severity and their consequence could be related to the individual's physiological attributes (e.g. age, fragility, premedical condition etc). It is known that appropriate intervention improves survival and may reduce disabilities in traumas. Determining the probability of survival in traumas is important as it can inform triage, clinical research and audit. A number of methods have been reported for this purpose. These are based on a combination of physiological and anatomical examination scores. However, these methods have shortcomings as for example, combining the scores from injuries for different organs is complicated. A method for predicting probability of survival in traumas needs to be accurate, practical and accommodate broad cases. In this study Sheffield Hallam University, Sheffield Children's Hospital, Sheffield University and the Trauma Audit and Research Network (TARN) collaborated to develop improved means of predicting probability of survival in traumas. The data used in this study were trauma cases and their outcomes provided by the TARN. The data included 47568 adults (age: mean = 59.9 years, standard deviation = 24.7 years) with various injuries. In total, 93.3% of cases had survived and 6.7% of cases had not survived. The data were partitioned into calibration (2/3 of the data) and evaluation (1/3 of the data). The trauma parameters used in the study were: age, respiration rate (RR), systolic blood pressure (SBP), pulse (heart) rate (PR) and the values obtained from two trauma scoring systems called Abbreviated Injury Score (AIS) and Glasgow Coma Score (GCS). Intubation and Pre-exiting Medical Condition (PMC) data were also considered. Initially a detailed statistical exploration of the manner trauma these trauma parameters related to the probability of survival outcomes was carried out and the results were interpreted. The resulting information assisted the development of three methods to predict probability of survival. These were based on Bayesian statistical approach called predictive statistical diagnosis (PSD), a new method called Iterative Random Comparison classification (IRCC) and the third method combined the IRCC with the fuzzy inference system (FIS). The performance of these methods was compared with each other as well as the method of predicating survival used by the TARN called Ps14 (the name refers to probability of survival method reported in 2014). The study primarily focused on Trauma Brain Injury (TBI) as they represented the majority of the cases. For TBI, the developed IRCC performed best amongst all methods including Ps14. It predicted survivors and not survivors with 97.2% and 75.9% accuracies respectively. In comparison, the predication accuracy for Ps14 for survivors and not survivors were 97.4% and 40.2%. The study provided resulted in new findings that indicated the manner trauma parameters affect probability of survival and resulted in new artificial intelligence and statistical methods of determining probability survival in trauma

Development of machine learning schemes for use in non-invasive and continuous patient health monitoring

Stephanie Baker developed machine learning schemes for the non-invasive and continuous measurement of blood pressure and respiratory rate from heart activity waveforms. She also constructed machine learning models for mortality risk assessment from vital sign variations. This research contributes several tools that offer significant advancements in patient monitoring and wearable healthcare

Development of a fuzzy qualitative risk assessment model applied to construction industry

Dissertação para obtenção do Grau de Doutor em Engenharia IndustrialThe construction industry is plagued by occupational risky situations and poor working conditions. Risk Assessment for Occupational Safety (RAOS) is the first and key step to achieve adequate safety levels, particularly to support decision-making in safety programs. Most construction safety efforts are applied informally under the premise that simply allocating more resources to safety management will improve safety on site. Moreover, there are many traditional methods to address RAOS, but few have been adapted and validated for use in the construction industry, thus producing poor results. The contribution of this dissertation is a qualitative fuzzy RAOS model, tailored for the construction industry, named QRAM (Qualitative Risk Assessment Model). QRAM is based on four dimensions: Safety Climate Adequacy, (work accidents) Severity Factors, (work accidents) Possibility Factors and Safety Barriers Effectiveness. The risk assessment is based on real data collected by observation of reality, interviews with workers, foreman and engineers and consultation of site documents (working procedures, reports of work accident investigation, etc.), avoiding the use of data obtained by statistical tecnhiques. To rating each parameter it was defined qualitative evaluators - linguistic variables - which allow to perform a user-friendly knowledge elicitation. QRAM was, firstly evaluated by “peer” review, with 12 safety experts from Brazil (2), Bulgaria (1), Greece (3), Turkey (3) and Portugal (3), and then, evaluated by comparing QRAM with other RAOS tecnhiques and methods. The safety experts , concluded that: a) QRAM is a versatile tool for occupational safety risk assessment on construction sites; b) the specific checklists for knowledge elicitation are a good decision aid and, c) the use of linguistic variables is a better way to make the risk assessments process more objective and reliable.Fundação para a Ciência e Tecnologia - PhD Scholarship SFRH/BD/39610/200