On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Interferon-gamma release assays for the diagnosis of tuberculosis and tuberculosis infection in HIV-infected adults: a systematic review and meta-analysis.

Background: Despite the widespread use of interferon-gamma release assays (IGRAs), their role in diagnosing tuberculosis and targeting preventive therapy in HIV-infected patients remains unclear. We conducted a comprehensive systematic review to contribute to the evidence-based practice in HIV-infected people. Methodology/Principal Findings: We searched MEDLINE, Cochrane, and Biomedicine databases to identify articles published between January 2005 and July 2011 that assessed QuantiFERON H -TB Gold In-Tube (QFT-GIT) and T-SPOT H .TB (T-SPOT.TB) in HIV-infected adults. We assessed their accuracy for the diagnosis of tuberculosis and incident active tuberculosis, and the proportion of indeterminate results. The search identified 38 evaluable studies covering a total of 6514 HIV-infected participants. The pooled sensitivity and specificity for tuberculosis were 61% and 72% for QFT-GIT, and 65% and 70% for T-SPOT.TB. The cumulative incidence of subsequent active tuberculosis was 8.3% for QFT-GIT and 10% for T-SPOT.TB in patients tested positive (one study each), and 0% for QFT-GIT (two studies) and T-SPOT.TB (one study) respectively in those tested negative. Pooled indeterminate rates were 8.2% for QFT-GIT and 5.9% for T-SPOT.TB. Rates were higher in high burden settings (12.0% for QFT-GIT and 7.7% for T-SPOT.TB) than in low-intermediate burden settings (3.9% for QFT-GIT and 4.3% for T-SPOT.TB). They were also higher in patients with CD4 + T-cell count, 200 (11.6% for QFT-GIT and 11.4% for T-SPOT.TB) than in those with CD4 + T-cell count $ 200 (3.1% for QFT-GIT and 7.9% for T-SPOT.TB). Conclusions/Significance: IGRAs have suboptimal accuracy for confirming or ruling out active tuberculosis disease in HIV-infected adults. While their predictive value for incident active tuberculosis is modest, a negative QFT-GIT implies a very low short- to medium-term risk. Identifying the factors associated with indeterminate results will help to optimize the use of IGRAs in clinical practice, particularly in resource-limited countries with a high prevalence of HIV-coinfection.

Diagnosis of tuberculosis infection using interferon-γ-based assays

Interferon-γ-based assays, collectively known as IFN-γ release assays (IGRAs), have emerged as a reliable alternative to the old tuberculin skin test (TST) for the immunodiagnosis of tuberculosis (TB) infection. The 2 commercially available tests, the enzyme-linked immunosorbent assay (ELISA), QuantiFERON-TB Gold Intube (QFT-IT), and the enzyme-linked immunospot assay (ELISPOT), T-SPOT.TB, are more accurate than TST for the diagnosis of TB, since they are highly specific and correlate better with the existence of risk factors for the infection. According to the available data, T-SPOT.TB obtains a higher number of positive results than QFT-IT, while its specificity seems to be lower. Although the sensitivity of the IFN-γ -based assays may be impaired to some extent by cellular immunosuppression and extreme ages of life, they perform better than TST in these situations. Data from longitudinal studies suggest that IFN-γ-based tests are better predictors of subsequent development of active TB than TST; however this prognostic value has not been consistently demonstrated. This review focuses on the clinical use of the IFN-γ -based tests in different risk TB groups, and notes the main limitations and areas for future development.

How do we make the (right) diagnosis of acute appendicitis?

Objectives: To evaluate the correct diagnosis in unselected patients presenting withsuspected acute appendicitis in the Emergency Department in Hospital Trueta. To evaluate the different scenarios to achieve the correct diagnosis in patients with suspected acute appendicitis estimated by clinical evaluation without imaging, US only, CT only or US in all patients followed by CT after a non-diagnostic US.Design: Cross-sectional study conducted between April 2014 and March 2015.Settings: Medium-sized teaching hospital in Girona.Participants: Consecutive adult patients, 14 years old or older, with clinically suspectedacute appendicitis evaluated at the emergency department.Main outcome: Correct diagnosis of acute appendicitis

Diagnosis, prevention and treatment of hepatorenal syndrome in cirrhosis

Hepatorenal syndrome (HRS) is a serious complication of end-stage liver disease, occurring mainly in patients with advanced cirrhosis and ascites, who have marked circulatory dysfunction,1 as well as in patients with acute liver failure.2 In spite of its functional nature, HRS is associated with a poor prognosis,3 4 and the only effective treatment is liver transplantation. During the 56th Meeting of the American Association for the Study of Liver Diseases, the International Ascites Club held a Focused Study Group (FSG) on HRS for the purpose of reporting the results of an international workshop and to reach a consensus on a new definition, criteria for diagnosis and recommendations on HRS treatment. A similar workshop was held in Chicago in 1994 in which standardised nomenclature and diagnostic criteria for refractory ascites and HRS were established.5 The introduction of innovative treatments and improvements in our understanding of the pathogenesis of HRS during the previous decade led to an increasing need to undertake a new consensus meeting. This paper reports the scientific rationale behind the new definitions and recommendations. The international workshop included four issues debated by four panels of experts (see Acknowledgements). The issues were: (1) evidence-based HRS pathogenesis; (2) treatment of HRS using vasoconstrictors; (3) other HRS treatments using transjugular intrahepatic portosystemic stent-shunt (TIPS) and extracorporeal albumin dialysis (ECAD); and (4) new definitions and diagnostic criteria for HRS and recommendations for its treatment.

Early diagnosis of osteoporosis by means of orthopantomograms and oral x-rays: a systematic review

Osteoporosis is a systemic bone disease that is characterized by a generalized reduction of the bone mass. It is the main cause of fractures in elderly women. Bone densitometry is used in the lumbar spine and hip in order to detect osteoporosis in its early stages. Different studies have observed a correlation between the bone mineral density of the jaw (BMD) and that of the lumbar spine and/or hip. On the other hand, there are studies that evaluate the findings in the orthopantomograms and perapical X-rays, correlating them with the early diagnosis of osteoporosis and highlighting the role of the dentist in the early diagnosis of this disease. Materials and methods: A search was carried out in the Medline-Pubmed database in order to identify those articles that deal with the association between the X-ray findings observed in the orthopantomograms and the diagnosis of the osteoporosis, as well as those that deal with the bone mineral density of the jaw. Results: There were 406 articles, and with the limits established, this number was reduced to 21. Almost all of the articles indicate that when examining oral X-rays, it is possible to detect signs indicative of osteoporosis. Discussion: The radiomorphometric indices use measurements in orthopantomograms and evaluate possible loss of bone mineral density. They can be analyzed alone or along with the visual indices. In the periapical X-rays, the photodensimetric analyses and the trabecular pattern appear to be the most useful. There are seven studies that analyze the densitometry of the jaw, but only three do so independently of the photodensitometric analysis. Conclusions: The combination of mandibular indices, along with surveys on the risk of fracture, can be useful as indicators of early diagnosis of osteoporosis. Visual and morphometric indices appear to be especially important in the orthopantomograms. Photodensitometry indices and the trabecular pattern are used in periapical X-rays. Studies on mandibular dual-energy X-ray absorptiometry are inconclusive

Laboratory diagnosis of brucellosis in a rural endemic area in northeastern Spain

Sera obtained from 62 patients from four mountain counties in Catalonia (Northeastern Spain), in whom brucellosis had been diagnosed on the basis of clinical evidence and/or personal history, were analyzed using the rose Bengal test, standard serum agglutination test (SAT), Coombs" test, ELISA, and complement fixation. The diagnosis was further confirmed through blood cultures. Clinical evidence, epidemiology, and the results from serologic tests were used to assign patients to one of two groups: group 1 (n = 38) patients had primary infections, whereas group 2 (n = 24) patients had been previously exposed to the microorganism, i.e. re-infection of group 2 individuals occurred after long periods of time during which no active infection by Brucella had been detected. Receivingoperating charts (ROC) were used to determine the diagnostic value of the different tests and to establish discriminant values. Blood culture was a valuable diagnostic tool in group 1 (0.92 sensitivity) but was inappropriate in group 2 (0.08). The combination of positive rose Bengal test and agglutination ≥1/160 was valid for diagnosis in group 1. In group 2, agglutination < 1/160 (including negative agglutination) did not rule out brucellosis. The combination of positive rose Bengal test and Coombs" test ≥1/320 was the best diagnostic criterion (0.8 specificity; 1 sensitivity). ELISA (for IgG, IgM, or both) did not improve diagnostic accuracy

Health-related quality of life in patients with dual diagnosis: clinical correlates

Background: Although the studies published so far have found an affectation in the Health Related Quality of Life (HRQOL) in both psychiatric and substance use dependence disorders, very few studies have applied HRQOL as an assessment measure in patients suffering both comorbid conditions, or Dual Diagnosis. The aim of the current study was to assess HRQOL in a group of patients with Dual Diagnosis compared to two other non-comorbid groups and to determine what clinical factors are related to HRQOL. Methods: Cross-sectional assessment of three experimental groups was made through the Short Form 36 Item Health Survey (SF-36). The sample consisted of a group with Dual Diagnosis (DD; N=35), one with Severe Mental Illness alone (SMI; N=35) and another one with Substance Use Dependence alone (SUD; N=35). The sample was composed only by males. To assess the clinical correlates of SF-36 HRQOL, lineal regression analyses were carried out. Results: The DD group showed lower scores in most of the subscales, and in the mental health domain. The group with SUD showed in general a better state in the HRQOL while the group with SMI held an intermediate position with respect to the other two groups. Daily medication, suicidal attempts and daily number of coffees were significantly associated to HRQOL, especially in the DD group. Conclusions: The DD group showed lower self-reported mental health quality of life. Assessment of HRQOL in dual patients allows to identify specific needs in this population, and may help to establish therapeutic goals to improve interventions.

Mapping the diagnosis axis of an interface terminology to the NANDA International Taxonomy

Background: Nursing terminologies are designed to support nursing practice but, as with any other clinical tool, they should be evaluated. Cross-mapping is a formal method for examining the validity of the existing controlled vocabularies. Objectives: The study aims to assess the inclusiveness and expressiveness of the nursing diagnosis axis of a newly implemented interface terminology by cross-mapping with the NANDA-I taxonomy. Design/Methods: The study applied a descriptive design, using a cross-sectional, bidirectional mapping strategy. The sample included 728 concepts from both vocabularies. Concept cross-mapping was carried out to identify one-to-one, negative, and hierarchical connections. The analysis was conducted using descriptive statistics. Results: Agreement of the raters" mapping achieved 97%. More than 60% of the nursing diagnosis concepts in the NANDA-I taxonomy were mapped to concepts in the diagnosis axis of the new interface terminology; 71.1% were reversely mapped. Conclusions: Main results for outcome measures suggest that the diagnosis axis of this interface terminology meets the validity criterion of cross-mapping when mapped from and to the NANDA-I taxonomy.

Diagnosis of Alzheimer’s Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands

Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann–Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

Alzheimer Disease Diagnosis based on Automatic Spontaneous Speech Analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to improvement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.