Integrated Diagnosis and Prognosis Model for High Pressure LNG Pump

In condition-based maintenance (CBM), effective diagnostics and prognostics are essential tools for maintenance engineers to identify imminent fault and to predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedules production if necessary. This paper presents a technique for accurate assessment of the remnant life of machines based on historical failure knowledge embedded in the closed loop diagnostic and prognostic system. The technique uses the Support Vector Machine (SVM) classifier for both fault diagnosis and evaluation of health stages of machine degradation. To validate the feasibility of the proposed model, the five different level data of typical four faults from High Pressure Liquefied Natural Gas (HP-LNG) pumps were used for multi-class fault diagnosis. In addition, two sets of impeller-rub data were analysed and employed to predict the remnant life of pump based on estimation of health state. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications.

Raman microscopy in the diagnosis and prognosis of surgically resected nonsmall cell lung cancer

The main curative therapy for patients with nonsmall cell lung cancer is surgery. Despite this, the survival rate is only 50%, therefore it is important to more efficiently diagnose and predict prognosis for lung cancer patients. Raman spectroscopy is useful in the diagnosis of malignant and premalignant lesions. The aim of this study is to investigate the ability of Raman microscopy to diagnose lung cancer from surgically resected tissue sections, and predict the prognosis of these patients. Tumor tissue sections from curative resections are mapped by Raman microscopy and the spectra analzsed using multivariate techniques. Spectra from the tumor samples are also compared with their outcome data to define their prognostic significance. Using principal component analysis and random forest classification, Raman microscopy differentiates malignant from normal lung tissue. Principal component analysis of 34 tumor spectra predicts early postoperative cancer recurrence with a sensitivity of 73% and specificity of 74%. Spectral analysis reveals elevated porphyrin levels in the normal samples and more DNA in the tumor samples. Raman microscopy can be a useful technique for the diagnosis and prognosis of lung cancer patients receiving surgery, and for elucidating the biochemical properties of lung tumors. (C) 2010 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.3323088]

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

The individual delinquent : a text-book of diagnosis and prognosis for all concerned in understanding offenders /

Bibliography: p.791-808.

MFPG : detection, diagnosis, and prognosis : proceedings of the 26th meeting of the Mechanical Failures Prevention Group, held at the IIT Research Institute, Chicago, Illinois, May 17-19, 1977 /

"Sponsored by the Institute for Materials Research of the National Bureau of Standards ... [et al.]"

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Discovery and validation of serologic biomarkers for the diagnosis and prognosis of invasive candidiasis by computational immunomics

Invasive candidiasis (IC) is an opportunistic systemic mycosis caused by Candida species (commonly Candida albicans) that continues to pose a significant public health problem worldwide. Despite great advances in antifungal therapy and changes in clinical practices, IC remains a major infectious cause of morbidity and mortality in severely immunocompromised or critically ill patients, and further accounts for substantial healthcare costs. Its impact on patient clinical outcome and economic burden could be ameliorated by timely initiation of appropriate antifungal therapy. However, early detection of IC is extremely difficult because of its unspecific clinical signs and symptoms, and the inadequate accuracy and time delay of the currently available diagnostic or risk stratification methods. In consequence, the diagnosis of IC is often attained in advanced stages of infection (leading to delayed therapeutic interventions and ensuing poor clinical outcomes) or, unfortunately, at autopsy. In addition to the difficulties encountered in diagnosing IC at an early stage, the initial therapeutic decision-making process is also hindered by the insufficient accuracy of the currently available tools for predicting clinical outcomes in individual IC patients at presentation. Therefore, it is not surprising that clinicians are generally unable to early detect IC, and identify those IC patients who are most likely to suffer fatal clinical outcomes and may benefit from more personalized therapeutic strategies at presentation. Better diagnostic and prognostic biomarkers for IC are thus needed to improve the clinical management of this life-threatening and costly opportunistic fungal infection...

Applications of medical image processing in the diagnosis and treatment of spinal deformity

In Chapter 10, Adam and Dougherty describe the application of medical image processing to the assessment and treatment of spinal deformity, with a focus on the surgical treatment of idiopathic scoliosis. The natural history of spinal deformity and current approaches to surgical and non-surgical treatment are briefly described, followed by an overview of current clinically used imaging modalities. The key metrics currently used to assess the severity and progression of spinal deformities from medical images are presented, followed by a discussion of the errors and uncertainties involved in manual measurements. This provides the context for an analysis of automated and semi-automated image processing approaches to measure spinal curve shape and severity in two and three dimensions.

Hypothesis Formation and Evaluation in Medical Diagnosis

This thesis describes some aspects of a computer system for doing medical diagnosis in the specialized field of kidney disease. Because such a system faces the spectre of combinatorial explosion, this discussion concentrates on heuristics which control the number of concurrent hypotheses and efficient "compiled" representations of medical knowledge. In particular, the differential diagnosis of hematuria (blood in the urine) is discussed in detail. A protocol of a simulated doctor/patient interaction is presented and analyzed to determine the crucial structures and processes involved in the diagnosis procedure. The data structure proposed for representing medical information revolves around elementary hypotheses which are activated when certain disposing of findings, activating hypotheses, evaluating hypotheses locally and combining hypotheses globally is examined for its heuristic implications. The thesis attempts to fit the problem of medical diagnosis into the framework of other Artifcial Intelligence problems and paradigms and in particular explores the notions of pure search vs. heuristic methods, linearity and interaction, local vs. global knowledge and the structure of hypotheses within the world of kidney disease.

Incorporation of spiritual care as a component of healthcare and medical education: comparison of viewpoints of healthcare providers from a rural community hospital in Uganda with those of medical students from United Kingdrom

This study addresses cultural differences regarding views on the place for spirituality within healthcare training and delivery. A questionnaire was devised using a 5-point ordinal scale, with additional free text comments assessed by thematic analysis, to compare the views of Ugandan healthcare staff and students with those of (1) visiting international colleagues at the same hospital; (2) medical faculty and students in United Kingdom. Ugandan healthcare personnel were more favourably disposed towards addressing spiritual issues, their incorporation within compulsory healthcare training, and were more willing to contribute themselves to delivery than their European counterparts. Those from a nursing background also attached a greater importance to spiritual health and provision of spiritual care than their medical colleagues. Although those from a medical background recognised that a patient’s religiosity and spirituality can affect their response to their diagnosis and prognosis, they were more reticent to become directly involved in provision of such care, preferring to delegate this to others with greater expertise. Thus, differences in background, culture and healthcare organisation are important, and indicate that the wide range of views expressed in the current literature, the majority of which has originated in North America, are not necessarily transferable between locations; assessment of these issues locally may be the best way to plan such training and incorporation of spiritual care into clinical practice.

Brain activity and medical diagnosis: an EEG study

Abstract Background Despite new brain imaging techniques that have improved the study of the underlying processes of human decision-making, to the best of our knowledge, there have been very few studies that have attempted to investigate brain activity during medical diagnostic processing. We investigated brain electroencephalography (EEG) activity associated with diagnostic decision-making in the realm of veterinary medicine using X-rays as a fundamental auxiliary test. EEG signals were analysed using Principal Components (PCA) and Logistic Regression Analysis Results The principal component analysis revealed three patterns that accounted for 85% of the total variance in the EEG activity recorded while veterinary doctors read a clinical history, examined an X-ray image pertinent to a medical case, and selected among alternative diagnostic hypotheses. Two of these patterns are proposed to be associated with visual processing and the executive control of the task. The other two patterns are proposed to be related to the reasoning process that occurs during diagnostic decision-making. Conclusions PCA analysis was successful in disclosing the different patterns of brain activity associated with hypothesis triggering and handling (pattern P1); identification uncertainty and prevalence assessment (pattern P3), and hypothesis plausibility calculation (pattern P2); Logistic regression analysis was successful in disclosing the brain activity associated with clinical reasoning success, and together with regression analysis showed that clinical practice reorganizes the neural circuits supporting clinical reasoning.