Machine Learning In Medical Applications Health And Social Care Essay

Machine Learning In Medical Applications Health And Social Cargon testMachine Learning (ML) aims at providing computational methods for accumulating, changing and updating knowledge in intelligent systems, and in limited cultusivation mechanisms that will help us to induce knowledgefrom examples or data. Machine erudition methods are useful in cases where algorithmic solutions are not avail satisfactory, there is lack of formal models, or the knowledge virtually the application domain is poorly defined.The fact that various scientific communities are conglomerate in ML research led this scientific field to incorporate compositions from different areas, such as computational scholarship theory, artificial neural net break aways, statistics, stochastic modeling, genetic algorithms and pattern recognition. Therefore, ML includes a freehanded class of methods that scum bag be roughly classified in symbolic and subsymbolic (numeric) according to the nature of the handling wh ich takes place whilst learning.2.Technical discussionMachine Learning provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. ML is cosmos used for the analysis of the importance of clinical parameters and of their combinations for prognosis, e.g. fortune telling of disease progression, for the extraction of medical knowledge for bring outcomes research, for therapy planning and support, and for e trulywhe legitimatel affected role management. ML is as head being used for data analysis, such as detection of regularities in the data by clutchly transaction with imperfect data, interpretation of continuous data used in the Intensive Care Unit, and for intelligent stately resulting in effective and efficient monitoring. It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and e nhance the work of medical experts and ultimately to improve the capability and tone of voice of medical care. Below, we summarize some major ML application areas in medicine. Medical diagnostic abstract thought is a in truth important application area of computer-based systems (Kralj and Kuka, 1998 Strausberg and Person, 1999 Zupan et al., 1998). In this framework, expert systems and modelbased schemes provide mechanisms for the generation of hypotheses from patient data. For example, rules are extracted from the knowledge of experts in the expert systems. Unfortunately, in many cases, experts whitethorn not know, or may not be able to formulate, what knowledge they actually use in solving their problems. Symbolic learning techniques (e.g. inductive learning by examples) are used to add learning, and knowledge management capabilities to expert systems (Bourlas et al., 1996). habituated a set of clinical cases that act as examples, learning in intelligent systems can be achieved using ML methods that are able to produce a systematic description of those clinical features that unequivocally characterize the clinical conditions. This knowledge can be expressed in the form of simple rules, or often as a decision tree. A classic example of this type of system is KARDIO, which was veritable to interpret ECGs (Bratko et al., 1989).This approach can be extended to handle cases where there is no previous be intimate in the interpretation and reason of medical data. For example, in the work of Hau and Coiera (Hau and Coiera, 1997) an intelligent system, which takes real-time patient data obtained during cardiac bypass surgery and then creates models of normal and abnormal cardiac physiology, for detection of changes in a patients condition is described. Additionally, in a research setting, these models can serve as initial hypotheses that can exact further experimentation.2.1 MethodologyIn this section we propose a invigorated algorithm called REMED (Rule Extr action for aesculapian Diagnostic). The REMED algorithm includes three main steps 1) attributes portion oution, 2) selection of initial partitions, and in the long run 3) rule construction.2.1.1 Attributes SelectionFor the starting line step we consider that in medical exercise the collection of datasets is often expensive and time consuming. Then, it is worthy to have a classifier that is able to reliably diagnose with a small amount of data about the patients. In the first part of REMED we use simple logistic regression to quantify the risk of pain the disease with respect to the increase or decrement of an 574attribute. We al expressive styles use high confidence levels (99%) to select attributes that are really significant and to guarantee the construction of more precise rules. Other important formula to mention is that depending on the kind of association established ( unequivocal or negative) done the odds ratio metric, we give the syntax with which each(prenomin al) attributes partition will appear in the rules system. This part of the algorithm is shown in the pull in of figure 1.2.1.2 Partitions SelectionThe second part of REMED comes from the fact that if an attribute x has been statistically significant in the prediction of a disease, then its mean x (mean of the treasures of the attribute) is a good candidate as initial partition of the attribute. We sort the examples by the attributes value and from the initial partition of each attribute, we search the neighboring positive example (class = 1) in the direction of the established association. Then, we calculate a new partition through the average between the value of the found example and the value of its predecessor or successor. This displacement is carried out only once for each attribute. This can be seen in the middle part of figure 1.2.1.3 Rules windingIn the last part of the algorithm, we build a simple rule system of the following way if (ei,1 p1) and (ei,j pj ) and and (ei,m pm) then class = 1 else class = 0 where ei,j denotes the value of attribute j for example i, pj denotes the partition for attribute j and the relation or depends on the association attribute-disease.With this rule system we make a first classification. We then try to improve the accuracy of our system by increasing or diminish the value of each partition as much as possible. For this we obligate the bisection method and calculate possible new partitions starting with the current partition of each attribute and the maximum or minimum value of the examples for this attribute. We build a temporal rule system changing the current partition by each new partition and classify the examples again. We only consider a new partition if it diminishes the number of monstrous positives (FP) but does not diminish the number of true positives (TP). This step is repeated for each attribute until we shoot down the established convergence level for the bisection method or the current rul e system is not able to decrease the number of FP (healthy persons diagnosed incorrectly). This part of the algorithm is exemplified at the bottom of figure 1.We can appreciate that the final stage of REMED is to maximize the minority class accuracy at each step, first selecting the attributes that are strongly associated with the positive class. Then stopping the search of the partition that better discriminates both classes in the first positive example, and finally trying to improve the accuracy of the rule system but without diminishing the number of TP (sick persons diagnosed correctly).3. Machine learning in complementary medicine3.1 Kirlian effect a scientific tool for canvas subtle energiesThe recital of the so called Kirlian effect, also known as the Gas Discharge Visualization (GDV) technique (a wider term that includes also some other techniques is bioelectrography), goes back to 1777 when G.C. Lihtenberg in Germany recorded electrographs of sliding make out in dust created by static electricity and electric sparks. Later various researches contributed to the instruction of the technique (Korotkov, 1998b) Nikola Tesla in the USA, J.J. Narkiewich-Jodko in Russia, Pratt and Schlemmer in Prague until the Russian technician Semyon D. Kirlian together with his wife Valentina noticed that through the interaction of electric currents and photograph plates, imprints of living organisms true on film. In 1970 hundreds of enthusiasts started to reproduce Kirlian photos an the research was until 1995 limited to using a photo-paper technique. In 1995 a new approach, based on CCD Video techniques, and computer processing of data was developed by Korotkov (1998ab) and his team in St. Petersburg, Russia. Their instrument Crown-TV can be routinely used which opens practical possibilities to study the effects of GDV.The basic idea of GDV is to create an electromagnetic field using a high voltage and high frequency generator. after a thershold voltage is exceed ed the ionization of hitman around the studied intent takes place and as a side effect the quanta of light photons are emitted. So the discharge can be fixed optically by a photo, photo sensor or TV-camera. Various parameters inuence the ionization process (Korotkov, 1998b) gas properties (gas type, pressure, gas content), voltage parameters (amplitude, frequency, impulse waveform), electrode parameters (configuration, distance, dust and moisture, macro and micro defects, electromagnetic field configuration) and studied object parameters (common impedance, physical fields, skin galvanic response, etc.). So the Kirlian effect is the result of mechanical, chemical, and electromagnetic processes, and field interactions. Gas discharge acts as means of enhancing and visualization of super-weak processes.Due to the large number of parameters that inuence the Kirlian effect it is very dicult or impossible to control them all, so in the development of discharge there is always an divisi on of vagueness or stochastic. This is one of the reasons why the technique has not yet been widely accepted in practice as results did not have a high reproducibility. All explanations of the Kirlian effect apprehended uorescence as the hike of a biological object. Due to the low reproducibility, in academic circles there was a widely disperse opinion that all observed phenomena are nothing else but uctuation of the crown discharge without any corporation to the studied object. With modern technology, the reproducibility became sucent to enable serious scientific studies.Besides studying non-living objects, such as water and various liquids (Korotkov, 1998b), minerals, the most widely studied are living organisms plants (leafs, seeds, etc. (Korotkov and Kouznetsov, 1997 Korotkov, 1998b)), animals (Krashenuk et al., 1998), and of cut through humans. For humans, most widely recorded are coronas of fingers (Kraweck, 1994 Korotkov, 1998b), and GDV records of blood excerpts (Voeikov , 1998). Principal among these are studies of the psycho-physiological state and energy of a human, diagnosis (Gurvits and Korotkov, 1998), reactions to some medicines, reactions to various substances, food (Kraweck, 1994), dental treatment (Lee, 1998), alternative healing treatment, such as acupuncture, bioenergy, homeopathy, various relaxation and massage techniques (Korotkov, 1998b), GEM therapy, applied kineziology and ower essence treatment (Hein, 1999), leech therapy, etc., and even studying the GDV images after death (Korotkov, 1998a). There are many studiescurrently going on all over the world and there is no doubt that the human subtle energy field, as vizualized using the GDV technique, is extremely cor relate to the humans psycho-physiological state, and can be used for diagnostics, prognostics, theraphy selection, and controling the effects of the therapy.4.LimitationM. Schurr, from the Section for Minimal Invasive functioning of the Eberhard-Karls-University of Tuebin gen, gave an invited talk on endoscopic techniques and the role of ML methods in this context. He referred to current limitations of endoscopic techniques, which are related to the restrictions of access to the human body, associated to endoscopy. In this regard, the technical limitations include restrictions of manual capabilities to manipulate human organs through a small access, limitations in visualizing tissues and restrictions in getting diagnostic information about tissues. To exempt these problems, international technology developments focus on the creation of new manipulation techniques involving robotics and intelligent sensor devices for more precise endoscopic interventions. It is acknowledged that this new generation of sensor devices contributes to the development and spread of intelligent systems in medicine by providing ML methods with data for further processing. Current applications include suturing in cardiac surgery, and other clinical fields. It was mentioned t hat particular focus is put by several research groups on the development of new endoscopic visualizing and diagnostic tools. In this context, the potentials of new imaging principles, such as fluorescence imaging or laser scanning microscopy, and machine learning methods are very high. The clinical idea behind these developments is early(a) detection of malignant lesions in stages were local endoscopic therapy is possible. Technical developments in this field are very promising, however, clinical results are still pending and ongoing research will have to clarify the real potential of these technologies for clinical use.Moustakis and Charissis work (Moustakis and Charissis, 1999) surveyed the role of ML in medical decision making and provided an lengthened literature review on various ML applications in medicine that could be useful to practitioners elicit in applying ML methods to improve the efficiency and quality of medical decision making systems. In this work the point of g etting away from the accuracy measures as sole evaluation criteria of learning algorithms was stressed. The issue of comprehensibility, i.e. how well the medical expert can understand and thus use the results from a system that applies ML methods, is very important and should be carefully considered in the evaluation.5.Improvement ConclusionThe workshop gave the opportunity to researchers working in the ML field to get an overview of current work of ML in medical applications and/or gain understanding and experience in this area. Furthermore, young researchers had the opportunity to present their ideas, and received feedback from other workers in the area. The participants acknowledged that the diffusion of ML methods in medical applications can be very effective in improving the efficiency and the quality of medical care, but it still presents problems that are related to both theory and applications.From a divinatory point of view, it is important to enhance our understanding o f ML algorithms as well as to provide numeric justifications for their properties, in order to answer fundamental questions and acquire useful insight in the performance and doings of ML methods.On the other hand, some major issues which concern the process of learning knowledge in practice are the visualization of the learned knowledge, the collect for algorithms that will extract understandable rules from neural networks, as well as algorithms for identifying noise and outliers in the data. The participants also mentioned some other problems that arise in ML applications and should be addressed, like the control of over fitting and the scaling properties of the ML methods so that they can apply to problems with large datasets, and high-dimensional input (feature) and output (classes-categories) spaces.A recurring theme in the recommendations made by the participants was the need for comprehensibility of the learning outcome, relevance of rules, criteria for selecting the ML app lications in the medical context, the integration with the patient records and the description of the appropriate level and role of intelligent systems in healthcare. These issues are very complex, as technical, organizational and sociable issues arrive intertwined. Previous research and experience suggests that the successful implementation of information systems (e.g., (Anderson, 1997 Pouloudi, 1999)), and decision support systems in particular (e.g., (Lane et al., 1996Ridderikhoff and van Herk, 1999)), in the area of healthcare relies on the successful integration of the technology with the organizational and social context within which it is applied. Medical information is vital for the diagnosis and treatment of patients and therefore the ethical issues presented during its breeding cycle are critical. Understanding these issues becomes imperative as such technologies become pervasive. Some of these issues are system-centered, i.e., related to the inherent problems of the ML research. However, it is humans, not systems, who can act as moral agents. This means that it is humans that can identify and deal with ethical issues. Therefore, it is important to study the emerging challenges and ethical issues from a human-centered sentiment by considering the motivations and ethical dilemmas of researchers, developers and medical users of ML methods in medical applications.