Medical diagnosis by fuzzy standard additive model with wavelets

This paper proposes a combination of fuzzy standard additive model (SAM) with wavelet features for medical diagnosis. Wavelet transformation is used to reduce the dimension of high-dimensional datasets. This helps to improve the convergence speed of supervised learning process of the fuzzy SAM, which has a heavy computational burden in high-dimensional data. Fuzzy SAM becomes highly capable when deployed with wavelet features. This combination remarkably reduces its computational training burden. The performance of the proposed methodology is examined for two frequently used medical datasets: the lump breast cancer and heart disease. Experiments are deployed with a five-fold cross validation. Results demonstrate the superiority of the proposed method compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. Faster convergence but higher accuracy shows a win-win solution of the proposed approach.

Medication communication through documentation in medical wards: knowledge and power relations

Health professionals communicate with each other about medication information using different forms of documentation. This article explores knowledge and power relations surrounding medication information exchanged through documentation among nurses, doctors and pharmacists. Ethnographic fieldwork was conducted in 2010 in two medical wards of a metropolitan hospital in Australia. Data collection methods included participant observations, field interviews, video-recordings, document retrieval and video reflexive focus groups. A critical discourse analytic framework was used to guide data analysis. The written medication chart was the main means of communicating medication decisions from doctors to nurses as compared to verbal communication. Nurses positioned themselves as auditors of the medication chart and scrutinised medical prescribing to maintain the discourse of patient safety. Pharmacists utilised the discourse of scientific judgement to guide their decision-making on the necessity of verbal communication with nurses and doctors. Targeted interdisciplinary meetings involving nurses, doctors and pharmacists should be organised in ward settings to discuss the importance of having documented medication information conveyed verbally across different disciplines. Health professionals should be encouraged to proactively seek out each other to relay changes in medication regimens and treatment goals.

Leadership and teamwork in medical emergencies: performance of nursing students and registered nurses in simulated patient scenarios

Aims and objectives: To examine nursing students' and registered nurses' teamwork skills whilst managing simulated deteriorating patients. Background: Studies continue to show the lack of timely recognition of patient deterioration. Management of deteriorating patients can be influenced by education and experience. Design: Mixed methods study conducted in two universities and a rural hospital in Victoria, and one university in Queensland, Australia. Methods: Three simulation scenarios (chest pain, hypovolaemic shock and respiratory distress) were completed in teams of three by 97 nursing students and 44 registered nurses, equating to a total of 32 student and 15 registered nurse teams. Data were obtained from (1) Objective Structured Clinical Examination rating to assess performance; (2) Team Emergency Assessment Measure scores to assess teamwork; (3) simulation video footage; (4) reflective interview during participants' review of video footage. Qualitative thematic analysis of video and interview data was undertaken. Results: Objective structured clinical examination performance was similar across registered nurses and students (mean 54% and 49%); however, Team Emergency Assessment Measure scores differed significantly between the two groups (57% vs 38%, t = 6·841, p < 0·01). In both groups, there was a correlation between technical (Objective Structured Clinical Examination) and nontechnical (Team Emergency Assessment Measure) scores for the respiratory distress scenario (student teams: r = 0·530, p = 0·004, registered nurse teams r = 0·903, p < 0·01) and hypovolaemia scenario (student teams: r = 0·534, p = 0·02, registered nurse teams: r = 0·535, p = 0·049). Themes generated from the analysis of the combined quantitative and qualitative data were as follows: (1) leadership and followership behaviours; (2) help-seeking behaviours; (3) reliance on previous experience; (4) fixation on a single detail; and (5) team support. Conclusions: There is scope to improve leadership, team work and task management skills for registered nurses and nursing students. Simulation appears to be beneficial in enabling less experienced staff to assess their teamwork skills. Relevance to clinical practice: There is a need to encourage less experienced staff to become leaders and for all staff to develop improved teamwork skills for medical emergencies. © 2014 John Wiley & Sons Ltd.

A hybrid model for named entity recognition using unstructured medical text

Named entity recognition (NER) is an essential step in the process of information extraction within text mining. This paper proposes a technique to extract drug named entities from unstructured and informal medical text using a hybrid model of lexicon-based and rule-based techniques. In the proposed model, a lexicon is first used as the initial step to detect drug named entities. Inference rules are then deployed to further extract undetected drug names. The designed rules employ part of speech tags and morphological features for drug name detection. The proposed hybrid model is evaluated using a benchmark data set from the i2b2 2009 medication challenge, and is able to achieve an f-score of 66.97%.

Classification ensemble to improve medical named entity recognition

An accurate Named Entity Recognition (NER) is important for knowledge discovery in text mining. This paper proposes an ensemble machine learning approach to recognise Named Entities (NEs) from unstructured and informal medical text. Specifically, Conditional Random Field (CRF) and Maximum Entropy (ME) classifiers are applied individually to the test data set from the i2b2 2010 medication challenge. Each classifier is trained using a different set of features. The first set focuses on the contextual features of the data, while the second concentrates on the linguistic features of each word. The results of the two classifiers are then combined. The proposed approach achieves an f-score of 81.8%, showing a considerable improvement over the results from CRF and ME classifiers individually which achieve f-scores of 76% and 66.3% for the same data set, respectively.

Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.

Leadership and teamwork in medical emergencies: Performance of nursing students and registered nurses in simulated patient scenarios

Aims and objectives: To examine nursing students' and registered nurses' teamwork skills whilst managing simulated deteriorating patients. Background: Studies continue to show the lack of timely recognition of patient deterioration. Management of deteriorating patients can be influenced by education and experience. Design: Mixed methods study conducted in two universities and a rural hospital in Victoria, and one university in Queensland, Australia. Methods: Three simulation scenarios (chest pain, hypovolaemic shock and respiratory distress) were completed in teams of three by 97 nursing students and 44 registered nurses, equating to a total of 32 student and 15 registered nurse teams. Data were obtained from (1) Objective Structured Clinical Examination rating to assess performance; (2) Team Emergency Assessment Measure scores to assess teamwork; (3) simulation video footage; (4) reflective interview during participants' review of video footage. Qualitative thematic analysis of video and interview data was undertaken. Results: Objective structured clinical examination performance was similar across registered nurses and students (mean 54% and 49%); however, Team Emergency Assessment Measure scores differed significantly between the two groups (57% vs 38%, t = 6·841, p < 0·01). In both groups, there was a correlation between technical (Objective Structured Clinical Examination) and nontechnical (Team Emergency Assessment Measure) scores for the respiratory distress scenario (student teams: r = 0·530, p = 0·004, registered nurse teams r = 0·903, p < 0·01) and hypovolaemia scenario (student teams: r = 0·534, p = 0·02, registered nurse teams: r = 0·535, p = 0·049). Themes generated from the analysis of the combined quantitative and qualitative data were as follows: (1) leadership and followership behaviours; (2) help-seeking behaviours; (3) reliance on previous experience; (4) fixation on a single detail; and (5) team support. Conclusions: There is scope to improve leadership, team work and task management skills for registered nurses and nursing students. Simulation appears to be beneficial in enabling less experienced staff to assess their teamwork skills. Relevance to clinical practice: There is a need to encourage less experienced staff to become leaders and for all staff to develop improved teamwork skills for medical emergencies.

Patient participation in nursing care on medical wards: An integrative review

BACKGROUND: Patient participation is a way for patients to engage in their nursing care. In view of the possible link between patient participation and safety, there is a need for an updated review to assess patient participation in nursing care. OBJECTIVES: To investigate patients' and nurses' perceptions of and behaviours towards patient participation in nursing care in the context of hospital medical wards. DESIGN: Integrative review. DATA SOURCES: Three search strategies were employed in August 2013; a computerised database search of Cumulative Index of Nursing and Allied Health Literature, Cochrane Library, Medline and PsychINFO; reference lists were hand-searched; and forward citation searching was executed. REVIEW METHODS: After reviewing the studies, extracting study data and completing summary tables the methodological quality was assessed using the Mixed-Methods Assessment Tool by two reviewers. Reviewers met then to discuss discrepancies as well as the overall strengths and limitations of the studies. Discrepancies were overcome through consensus or a third reviewer adjudicated the issue. Within and across study analysis and synthesis of the findings sections was undertaken using thematic synthesis. RESULTS: Eight studies met inclusion criteria. Four themes were identified - enacting participation, challenges to participation, promoting participation and types of participation. Most studies included were conducted in Europe. The majority of studies used qualitative methodologies, with all studies sampling patients; nurses were included in three studies. Data were largely collected using self-reported perceptions; two studies included observational data. Methodological issues included a lack of reflexivity, un-validated data collection tools, sampling issues and low response rates. CONCLUSIONS: On medical wards, patients and nurses desire, perceive or enact patient participation passively. Challenging factors for patient participation include patients' willingness, nurses' approach and confusion around expectations and roles. Information-sharing was identified as an activity that promotes patient participation, suggesting nurses encourage active communication with patients in practice. Involving patients in assessment and care planning may also enhance patient participation. For education, enhancing nurses' understanding of the attributes of patient participation, as well as patient-centred care approaches may be beneficial for medical ward nurses. From here, researchers need to examine ways to overcome the barriers to patient participation; further nurse participants and observational data is required on medical wards.

Patients' perceptions of participation in nursing care on medical wards

BACKGROUND: Patient participation benefits the patient and is a core concept of patient-centred care. Patients believe in their ability to prevent errors; thus, they may play a vital role in combating adverse event rates in hospitals. AIMS AND OBJECTIVES: To explore hospitalised medical patients' perceptions of participating in nursing care, including the barriers and facilitators for this activity. RESEARCH METHODS: This interpretive study was conducted on four medical wards, in two hospitals. Purposeful maximum variation sampling was operationalised to recruit patients that differed in areas such as age, gender and mobility status. In-depth semi-structured audiotaped interviews were undertaken and analysed using inductive content analysis. RESULTS: Twenty patients participated in the study. Four categories were uncovered in the data. First, valuing participation showed patients' willingness to participate, viewing it as a worthwhile task. Second, exchanging intelligence was a way of participating where patients' knowledge was built and shared with health professionals. Third, on the lookout was a type of participation where patients monitored their care, showing an attentive approach towards their own safety. Fourth, power imbalance was characterised by patients feeling their opportunities for participation were restricted. CONCLUSIONS: Patients were motivated to participate and valued participation. Cultivating this motivation may be crucial to patient empowerment and practices of safety monitoring, a fundamental strategy to addressing patient safety issues in hospitals. Engaging nurse-patient relationships, inclusive of knowledge sharing, is required in practice to empower patients to participate. Educating patients on the consequences of non-participation may motivate them, while nurses may benefit from training on patient-centred approaches. Future research should address ways to increase patient motivation and opportunities to participate.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Fuzzy system with tabu search learning for classification of motor imagery data

This paper introduces an approach to classify EEG signals using wavelet transform and a fuzzy standard additive model (FSAM) with tabu search learning mechanism. Wavelet coefficients are ranked based on statistics of the Wilcoxon test. The most informative coefficients are assembled to form a feature set that serves as inputs to the tabu-FSAM. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II are employed for the experiments. Classification performance is evaluated using accuracy, mutual information, Gini coefficient and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, ensemble learning Adaboost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The proposed tabu-FSAM method considerably dominates the competitive classifiers, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II.

Using clinical trial data and linked administrative health data to reduce the risk of adverse events associated with the uptake of newly released drugs by older Australians: a model process

BACKGROUND: The study was undertaken to evaluate the contribution of a process which uses clinical trial data plus linked de-identified administrative health data to forecast potential risk of adverse events associated with the use of newly released drugs by older Australian patients. METHODS: The study uses publicly available data from the clinical trials of a newly released drug to ascertain which patient age groups, gender, comorbidities and co-medications were excluded in the trials. It then uses linked de-identified hospital morbidity and medications dispensing data to investigate the comorbidities and co-medications of patients who suffer from the target morbidity of the new drug and who are the likely target population for the drug. The clinical trial information and the linked morbidity and medication data are compared to assess which patient groups could potentially be at risk of an adverse event associated with use of the new drug. RESULTS: Applying the model in a retrospective real-world scenario identified that the majority of the sample group of Australian patients aged 65 years and over with the target morbidity of the newly released COX-2-selective NSAID rofecoxib also suffered from a major morbidity excluded in the trials of that drug, indicating a substantial potential risk of adverse events amongst those patients. This risk was borne out in post-release morbidity and mortality associated with use of that drug. CONCLUSIONS: Clinical trial data and linked administrative health data can together support a prospective assessment of patient groups who could be at risk of an adverse event if they are prescribed a newly released drug in the context of their age, gender, comorbidities and/or co-medications. Communication of this independent risk information to prescribers has the potential to reduce adverse events in the period after the release of the new drug, which is when the risk is greatest.Note: The terms 'adverse drug reaction' and 'adverse drug event' have come to be used interchangeably in the current literature. For consistency, the authors have chosen to use the wider term 'adverse drug event' (ADE).

Localization and degmentation of 3D intervertebral discs in MR Images by data driven estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.