Methodologies of Legacy Clinical Decision Support System -A Review

Information technology playing a prominent role in the field of medical by incorporating the Clinical Decision Support System(CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at the right time. Now a day's Clinical decision support system is a dynamic research area in the field of computer, but the lack of the knowledge of the understanding as well as the functioning of the system ,make the adoption slow by the physician and patient. The literature review of this paper will focus on the overview of legacy CDSS, the kind of methodologies and classifier employed to prepare such decision support system using a non-technical approach to the physician and the strategy- makers . This study will provide the scope of understanding the clinical decision support along with the gateway to physician ,policy-makers to develop and deploy the decision support system as a healthcare service to make the quick, agile and right decision. Future direction to handle the uncertainties along with the challenges of clinical decision support system are also enlightened in this study

Behind the screens: Clinical decision support methodologies - A review

Clinical decision support systems (CDSSs) are interactive software systems designed to assist clinicians with decision making tasks, such as determining diagnosis of patient data. CDSSs are a widely researched topic in the Computer Science community but their workings are less well understood by clinicians. The purpose of this review is to introduce clinicians and policy makers to the most commonly computer-based methodologies employed to construct decision models to compute clinical decisions in a non-technical manner. We hope that a better understanding of CDSSs will open up discussion about the future of CDSSs as a part of healthcare delivery as well as engage clinicians and policy makers in the development and deployment of CDSSs that can meaningfully help with decision making tasks

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Faculty Publications and Creative Works 1997

One of the ways we recognize our faculty at the University of New Mexico is through this annual publication which highlights our faculty\u27s scholarly and creative activities and achievements and serves as a compendium of UNM faculty efforts during the 1997 calendar year. Faculty Publications and Creative Works strives to illustrate the depth and breadth of research activities performed throughout our University\u27s laboratories, studios and classrooms. We believe that the communication of individual research is a significant method of sharing concepts and thoughts and ultimately inspiring the birth of new of ideas. In support of this, UNM faculty during 1997 produced over 2,770 works, including 2,398 scholarly papers and articles, 72 books, 63 book chapters, 82 reviews, 151 creative works and 4 patents. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico. Nasir Ahmed Interim Associate Provost for Research and Dean of Graduate Studie

Data science for health-care: Patient condition recognition

>Magister Scientiae - MScThe emergence of the Internet of Things (IoT) and Artificial Intelligence (AI) have elicited increased interest in many areas of our daily lives. These include health, agriculture, aviation, manufacturing, cities management and many others. In the health sector, portable vital sign monitoring devices are being developed using the IoT technology to collect patients’ vital signs in real-time. The vital sign data acquired by wearable devices is quantitative and machine learning techniques can be applied to find hidden patterns in the dataset and help the medical practitioner with decision making. There are about 30000 diseases known to man and no human being can possibly remember all of them, their relations to other diseases, their symptoms and whether the symptoms exhibited by the patients are early warnings of a fatal disease. In light of this, Medical Decision Support Systems (MDSS) can provide assistance in making these crucial assessments. In most decision support systems factors a ect each other; they can be contradictory, competitive, and complementary. All these factors contribute to the overall decision and have di erent degrees of influence [85]. However, while there is more need for automated processes to improve the health-care sector, most of MDSS and the associated devices are still under clinical trials. This thesis revisits cyber physical health systems (CPHS) with the objective of designing and implementing a data analytics platform that provides patient condition monitoring services in terms of patient prioritisation and disease identification [1]. Di erent machine learning algorithms are investigated by the platform as potential candidate for achieving patient prioritisation. These include multiple linear regression, multiple logistic regression, classification and regression decision trees, single hidden layer neural networks and deep neural networks. Graph theory concepts are used to design and implement disease identification. The data analytics platform analyses data from biomedical sensors and other descriptive data provided by the patients (this can be recent data or historical data) stored in a cloud which can be private local health Information organisation (LHIO) or belonging to a regional health information organisation (RHIO). Users of the data analytics platform consisting of medical practitioners and patients are assumed to interact with the platform through cities’ pharmacies , rural E-Health kiosks end user applications

Систем за подршку одлучивању, евалуацију и праћење стања пацијената оболелих од неуродегенеративних болести

Системи за подршку клиничком одлучивању представљају рачунарске алате који применом напредних технологија могу утицати на доношење одлука у вези са пацијентима. У овој дисертацији представљени су истраживање и развој новог система за подршку одлучивању, евалуацију и праћење стања пацијената оболелих од неуродегенеративних болести. Анализа клинички релевантних и свакодневних покрета чини основу овог система. Обрасци ових покрета снимљени су помоћу бежичних, носивих сензора малих димензија и тежине, који не захтевају компликовану поставку и могу се једноставно применити у било ком окружењу. Први део система намењен је (раном) препознавању Паркинсонове болести (ПБ) на основу анализе хода и алгоритама дубоког учења. Резултати су показали да је ПБ пацијенте могуће препознати са високом тачношћу. Други део система посвећен је праћењу симптома ПБ брадикинезије применом резоновања који се базира на знању. Представљена је метода за анализу покрета који се користе за евалуацију брадикинезије. Поред тога, применом различитих метода обраде сигнала развијена је нова метрика за квантификацију важних карактеристика ових покрета. Предикција степена развоја симптома се заснива на новом експертском систему који у потпуности објективизује клиничке евалуационе критеријуме. Валидација је урађена на примеру покрета тапкања прстију, који је снимљен на пацијенатима са типичним и атипичним паркинсонизимом. Показана је висока усаглашеност у поређењу са клиничким подацима. Развијени систем је објективан, аутоматизован, једноставно се користи, садржи интуитиван графички и параметарски приказ резултата и значајно доприноси унапређењу клиничких процедура за евалуацију и праћење стања пацијената са неуродегенеративним болестима.Clinical decision support system represents a computer-aided tool that utilizes advanced technologies for influencing clinical decisions about patients. This dissertation presents research and development of a new decision support system for the assessment of patients with neurodegenerative diseases. The analysis of movements that are part of standard clinical scales or everyday activities represents the basis of the system. These movements are recorded using small and lightweight wearable, wireless sensors, which do not require complicated setup and can be easily applied in any environment. The first part of system is dedicated to the (early) recognition of Parkinson’s disease (PD) based on gait analysis and deep learning algorithms. PD patients could be identified with a high accuracy. The other part of the system is dedicated to the assessment of PD symptoms, more specifically, bradykinesia, utilizing the knowledge-based reasoning. A method for analysis of bradykinesia related movements is defined and presented. Moreover, by applying different signal processing techniques, new metrics have been developed to quantify the essential characteristics of these movements. The prediction of symptom severity was performed using new expert system that completely objectified the clinical evaluation criteria. Validation was performed on the example of the finger-tapping movement of patients with typical and atypical parkinsonism. A high compliance rate was obtained compared to clinical data. The developed system is objective, automated, easy to use, contains an intuitive graphical and parametric presentation of results, and significantly contributes to the improvement of clinical assessment of patients with neurodegenerative diseases

Open research issues on multi-models for complex technological systems

Abstract -We are going to report here about state of the art works on multi-models for complex technological systems both from the theoretical and practical point of view. A variety of algorithmic approaches (k-mean, dss, etc.) and applicative domains (wind farms, neurological diseases, etc.) are reported to illustrate the extension of the research area