Knowledge-light adaptation approaches in case-based reasoning for radiotherapy treatment planning

ObjectiveRadiotherapy treatment planning aims at delivering a sufficient radiation dose to cancerous tumour cells while sparing healthy organs in the tumour-surrounding area. It is a time-consuming trial-and-error process that requires the expertise of a group of medical experts including oncologists and medical physicists and can take from 2 to 3 h to a few days. Our objective is to improve the performance of our previously built case-based reasoning (CBR) system for brain tumour radiotherapy treatment planning. In this system, a treatment plan for a new patient is retrieved from a case base containing patient cases treated in the past and their treatment plans. However, this system does not perform any adaptation, which is needed to account for any difference between the new and retrieved cases. Generally, the adaptation phase is considered to be intrinsically knowledge-intensive and domain-dependent. Therefore, an adaptation often requires a large amount of domain-specific knowledge, which can be difficult to acquire and often is not readily available. In this study, we investigate approaches to adaptation that do not require much domain knowledge, referred to as knowledge-light adaptation.MethodologyWe developed two adaptation approaches: adaptation based on machine-learning tools and adaptation-guided retrieval. They were used to adapt the beam number and beam angles suggested in the retrieved case. Two machine-learning tools, neural networks and naive Bayes classifier, were used in the adaptation to learn how the difference in attribute values between the retrieved and new cases affects the output of these two cases. The adaptation-guided retrieval takes into consideration not only the similarity between the new and retrieved cases, but also how to adapt the retrieved case.ResultsThe research was carried out in collaboration with medical physicists at the Nottingham University Hospitals NHS Trust, City Hospital Campus, UK. All experiments were performed using real-world brain cancer patient cases treated with three-dimensional (3D)-conformal radiotherapy. Neural networks-based adaptation improved the success rate of the CBR system with no adaptation by 12%. However, naive Bayes classifier did not improve the current retrieval results as it did not consider the interplay among attributes. The adaptation-guided retrieval of the case for beam number improved the success rate of the CBR system by 29%. However, it did not demonstrate good performance for the beam angle adaptation. Its success rate was 29% versus 39% when no adaptation was performed.ConclusionsThe obtained empirical results demonstrate that the proposed adaptation methods improve the performance of the existing CBR system in recommending the number of beams to use. However, we also conclude that to be effective, the proposed adaptation of beam angles requires a large number of relevant cases in the case base.     

eXiT*CBR: A framework for case-based medical diagnosis development and experimentation

Objective

Medical applications have special features (interpretation of results in medical metrics, experiment reproducibility and dealing with complex data) that require the development of particular tools. The eXiT*CBR framework is proposed to support the development of and experimentation with new case-based reasoning (CBR) systems for medical diagnosis.

Method

Our framework offers a modular, heterogeneous environment that combines different CBR techniques for different application requirements. The graphical user interface allows easy navigation through a set of experiments that are pre-visualized as plots (receiver operator characteristics (ROC) and accuracy curves). This user-friendly navigation allows easy analysis and replication of experiments. Used as a plug-in on the same interface, eXiT*CBR can work with any data mining technique such as determining feature relevance.

Results

The results show that eXiT*CBR is a user-friendly tool that facilitates medical users to utilize CBR methods to determine diagnoses in the field of breast cancer, dealing with different patterns implicit in the data.

Conclusions

Although several tools have been developed to facilitate the rapid construction of prototypes, none of them has taken into account the particularities of medical applications as an appropriate interface to medical users. eXiT*CBR aims to fill this gap. It uses CBR methods and common medical visualization tools, such as ROC plots, that facilitate the interpretation of the results. The navigation capabilities of this tool allow the tuning of different CBR parameters using experimental results. In addition, the tool allows experiment reproducibility.     

Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system

This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts.     

A case-based reasoning view of thrombophilia risk

Thrombophilia stands for a genetic or an acquired tendency to hypercoagulable states that increase the risk of venous and arterial thromboses. Indeed, venous thromboembolism is often a chronic illness, mainly in deep venous thrombosis and pulmonary embolism, requiring lifelong prevention strategies. Therefore, it is crucial to identify the cause of the disease, the most appropriate treatment, the length of treatment or prevent a thrombotic recurrence. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a logic programming approach to knowledge representation and reasoning, complemented with a case-based approach to computing. The proposed model has been quite accurate in the assessment of thrombophilia predisposition risk, since the overall accuracy is higher than 90% and sensitivity ranging in the interval [86.5%, 88.1%]. The main strength of the proposed solution is the ability to deal explicitly with incomplete, unknown, or even self-contradictory information.     

A hybrid diagnosis model for determining the types of the liver disease

The symptoms of liver diseases are not apparent in the initial stage, and the condition is usually quite serious when the symptoms are obvious enough. Most studies on liver disease diagnosis focus mainly on identifying the presence of liver disease in a patient. Not many diagnosis models have been developed to move beyond the detection of liver disease. The study accordingly aims to construct an intelligent liver diagnosis model which integrates artificial neural networks, analytic hierarchy process, and case-based reasoning methods to examine if patients suffer from liver disease and to determine the types of the liver disease.     

Sensory supplementation system based on electrotactile tongue biofeedback of head position for balance control

The present study aimed at investigating the effects of an artificial head position-based tongue-placed electrotactile biofeedback on postural control during quiet standing under different somatosensory conditions from the support surface. Eight young healthy adults were asked to stand as immobile as possible with their eyes closed on two Firm and Foam support surface conditions executed in two conditions of No-biofeedback and Biofeedback. In the Foam condition, a 6-cm thick foam support surface was placed under the subjects’ feet to alter the quality and/or quantity of somatosensory information at the plantar sole and the ankle. The underlying principle of the biofeedback consisted of providing supplementary information about the head orientation with respect to gravitational vertical through electrical stimulation of the tongue. Centre of foot pressure (CoP) displacements were recorded using a force platform. Larger CoP displacements were observed in the Foam than Firm conditions in the two conditions of No-biofeedback and Biofeedback. Interestingly, this destabilizing effect was less accentuated in the Biofeedback than No-biofeedback condition. In accordance with the sensory re-weighting hypothesis for balance control, the present findings evidence that the availability of the central nervous system to integrate an artificial head orientation information delivered through electrical stimulation of the tongue to limit the postural perturbation induced by alteration of somatosensory input from the support surface.     

An intelligent model for liver disease diagnosis

Objectives: Liver disease, the most common disease in Taiwan, is not easily discovered in its initial stage; early diagnosis of this leading cause of mortality is therefore highly important. The design of an effective diagnosis model is therefore an important issue in liver disease treatment. This study accordingly employs classification and regression tree (CART) and case-based reasoning (CBR) techniques to structure an intelligent diagnosis model aiming to provide a comprehensive analytic framework to raise the accuracy of liver disease diagnosis. Methods: Based on the advice and assistance of doctors and medical specialists of liver conditions, 510 outpatient visitors using ICD-9 (International Classification of Diseases, 9th Revision) codes at a medical center in Taiwan from 2005 to 2006 were selected as the cases in the data set for liver disease diagnosis. Data on 340 patients was utilized for the development of the model and on 170 patients utilized to perform comparative analysis of the models. This paper accordingly suggests an intelligent model for the diagnosis of liver diseases which integrates CART and CBR. The major steps in applying the model include: (1) adopting CART to diagnose whether a patient suffers from liver disease; (2) for patients diagnosed with liver disease in the first step, employing CBR to diagnose the types of liver diseases. Results: In the first phase, CART is used to extract rules from health examination data to show whether the patient suffers from liver disease. The results indicate that the CART rate of accuracy is 92.94%. In the second phase, CBR is developed to diagnose the type of liver disease, and the new case triggers the CBR system to retrieve the most similar case from the case base in order to support the treatment of liver disease. The new case is supported by a similarity ratio, and the CBR diagnostic accuracy rate is 90.00%. Actual implementation shows that the intelligent diagnosis model is capable of integrating CART and CBR techniques to examine liver diseases with considerable accuracy. The model can be used as a supporting system in making decisions regarding liver disease diagnosis and treatment. The rules extracted from CART are helpful to physicians in diagnosing liver diseases. CBR can retrieve the most similar case from the case base in order to solve a new liver disease problem and can be of great assistance to physicians in identifying the type of liver disease, reducing diagnostic errors and improving the quality and effectiveness of medical treatment.     

Adaptive frequency decomposition of EEG with subsequent expert system analysis

We present a hybrid system for automatic analysis of clinical routine EEG, comprising a spectral analysis and an expert system. EEG raw data are transformed into the time–frequency domain by the so-called adaptive frequency decomposition. The resulting frequency components are converted into pseudo-linguistic facts via fuzzification. Finally, an expert system applies symbolic rules formulated by the neurologist to evaluate the extracted EEG features. The system detects artefacts, describes alpha rhythm by frequency, amplitude, and stability and after artefact rejection detects pathologic slow activity. All results are displayed as linguistic terms, numerical values and maps of temporal extent, giving an overview about the clinical routine EEG.     

中枢神经系统真菌感染诊断治疗进展

中枢神经系统真菌感染发生率近年有逐渐增高之趋势,颅内真菌感染可分为弥漫性感染和局灶性感染,临床表现多为脑膜炎、脑膜脑炎引起的发热和颅高压症状,以及由颅内占位性病变所致的局灶性神经缺损症状。中枢神经系统真菌感染的诊断需将病史、流行病学、基础疾病、临床表现、影像学表现和各项实验室检查结果等综合分析,脑组织或脑脊液标本中找到真菌是诊断的金标准。中枢神经系统真菌感染的治疗原则是有效控制致病危险因素,使用有效抗真菌药物,对真菌脓肿、肉芽肿等进行积极手术干预。同时应积极探索新的诊断、治疗方法,以期改善患者预后。     

A neuro-fuzzy inference system for modeling and prediction of heart rate variability in the neuro-intensive care unit

In the neurological intensive care unit (NICU), prediction of impending changes in patient condition would be highly beneficial. In this paper, we employ a neuro-fuzzy inference system (NFIS) for short-term prediction of heart rate variability in the NICU. An NFIS was selected because it allows for a “gray-box” approach through which a system identification procedure is used in conjunction with fuzzy modeling. The NFIS is described in detail and is compared to an auto-regressive moving average (ARMA) model for its ability to model both simulated and measured data from NICU patients. We found that the NFIS is capable of predicting changes in heart rate to a reasonable extent, and that the NFIS has both advantages and limitations over the ARMA model. The NFIS may therefore be a reasonable technique to consider for more extensive prediction purposes in ICU settings.     

Pre-operative ordering of minimally invasive surgical tools: a fuzzy inference system approach

OBJECTIVE: With a limited number of access ports, minimally invasive surgery (MIS) often requires the complete removal of one tool and reinsertion of another in order to provide surgeons with the full functionality necessary to complete a procedure. MATERIALS AND METHODS: Endoscope video from 14 MIS procedures performed at the University of Nebraska Medical Center was used to collect usage statistics for various surgical instruments. This usage data was normalized and input to a fuzzy inference system (FIS) with four membership functions (MFs) to provide a crisp rating value for each instrument. Input membership functions included: number of uses ("Use"), total time used ("Time"), number of changes ("Change") and time per use ("Ave-Time"). Tools were arranged in a simulated cartridge system based on a "Usefulness" output membership function in such a way as to allow easy selection of the next instrument necessary to complete the procedure. Performance was measured by comparing the amount of cartridge indexing needed to complete a procedure using the FIS-generated arrangement against a set of random tool arrangements. RESULTS: The 14 FIS-generated tool arrangements considered in this investigation performed better than 64.11% of randomly generated tool arrangements and as well or better than 80.48% of tool arrangements. Using the FIS in conjunction with a multifunction laparoscopic tool, it is projected that an average cycle savings of 17.75% and 17.39% can be achieved over the mean and median of the random tool arrangements, respectively. CONCLUSIONS: For a given set of tools, the FIS used in this investigation provides an efficient method of arranging tools for MIS that performs at least as well or better than simply placing the tool tips into the chambers in a random configuration. This leads to a decrease in operating room time and corresponding decreases in both patient trauma from insertion and removal of tools and monetary cost, which is directly related to the amount of time spent changing instruments.     

Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.     

A psychomotor diagnostic instrument for patients with post-traumatic stress disorder

Posttraumatic stress disorder (PTSD) is associated with an increased risk of physical disorders as a consequence of chronic stress reactions and adverse lifestyle behaviours. In addition, various other physical signs and symptoms may be present, as well as problems with emotional awareness, such as alexithymia, which may impede verbal information processing. Therefore, a psychomotor diagnostic instrument (PMDI) is developed, based on non-verbal information to contribute to a careful and reliable diagnostic procedure. The PDMI is designed to identify specific goals for body and movement oriented treatments of PTSD. It consists of a manual with an assessment procedure, guidelines for scoring items and for the calculation of cluster scores based on item scores. In this paper, the PMDI and its development are discussed, and illustrated by brief vignettes.     

Support system for decision making in the identification of risk for body dysmorphic disorder: A fuzzy model

PurposeTo develop a fuzzy linguistic model to quantify the level of distress of patients seeking cosmetic surgery. Body dysmorphic disorder (BDD) is a mental condition related to body image relatively common among cosmetic surgery patients; it is difficult to diagnose and is a significant cause of morbidity and mortality. Fuzzy cognitive maps are an efficient tool based on human knowledge and experience that can handle uncertainty in identifying or grading BDD symptoms and the degree of body image dissatisfaction. Individuals who seek cosmetic procedures suffer from some degree of dissatisfaction with appearance.MethodsA fuzzy model was developed to measure distress levels in cosmetic surgery patients based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), diagnostic criterion B for BDD. We studied 288 patients of both sexes seeking abdominoplasty, rhinoplasty, or rhytidoplasty in a university hospital.ResultsPatient distress ranged from “none” to “severe” (range = 7.5–31.6; cutoff point = 18; area under the ROC curve = 0.923). There was a significant agreement between the fuzzy model and DSM-IV criterion B (kappa = 0.805; p < 0.001).ConclusionThe fuzzy model measured distress levels with good accuracy, indicating that it can be used as a screening tool in cosmetic surgery and psychiatric practice.     

Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review

Stress is a major problem of our society, as it is the cause of many health problems and huge economic losses in companies. Continuous high mental workloads and non-stop technological development, which leads to constant change and need for adaptation, makes the problem increasingly serious for office workers. To prevent stress from becoming chronic and provoking irreversible damages, it is necessary to detect it in its early stages. Unfortunately, an automatic, continuous and unobtrusive early stress detection method does not exist yet. The multimodal nature of stress and the research conducted in this area suggest that the developed method will depend on several modalities. Thus, this work reviews and brings together the recent works carried out in the automatic stress detection looking over the measurements executed along the three main modalities, namely, psychological, physiological and behavioural modalities, along with contextual measurements, in order to give hints about the most appropriate techniques to be used and thereby, to facilitate the development of such a holistic system.     

严重烧伤后应激性溃疡的综合防治

目的探讨严重烧伤后应激性溃疡的致病原因及防治方法。方法对1985年4月至2006年4月间收治的严重烧伤患者中并发应激性溃疡的38例患者临床资料进行回顾性分析。结果442例严重烧伤患者中，并发应激性溃疡者38例，发病率为8．6％；并发应激性溃疡者38例中死亡13例，病死率为34．2％。结论严重烧伤后应激性溃疡的发生与休克、感染等应激因素有关，因此积极消除诱发因素、加强制酸、早期进食、保护胃黏膜、应用生长抑素、必要时手术治疗，同时加强创面处理和抗感染治疗是防治烧伤后应激性溃疡的有效方法。     

Development of a FPGA based fuzzy neural network system for early diagnosis of critical health condition of a patient

The paper describes the design and training of a fuzzy neural network used for early diagnosis of a patient through an FPGA based implementation of a smart instrument. The system employs a fuzzy interface cascaded with a feed-forward neural network. In order to obtain an optimum decision regarding the future pathophysiological state of a patient, the optimal weights of the synapses between the neurons have been determined by using inverse delayed function model of neurons. The neurons that are considered in the proposed network are devoid of self connections instead of commonly used self connected neurons. The current work also find out the optimal number of neurons in the hidden layer for accurate diagnosis as against the available number of CLB in the FPGA. The system has been trained and tested with renal data of patients taken at 10 days interval of time. Applying the methodology, the chance of attainment of critical renal condition of a patient has been predicted with an accuracy of 95.2%, 30 days ahead of actually attaining the critical condition. The system has also been tested for pathophysiological state prediction of patients at multiple time steps ahead and the prediction at the next instant of time stands out to be the most accurate.