Towards Machine Consciousness: Grounding Abstract Models as pi-Processes

We present a two-level model of concurrent communicating systems (CCS) to serve as a basis formachine consciousness. A language implementing threads within logic programming is ¯rstintroduced. This high-level framework allows for the de¯nition of abstract processes that can beexecuted on a virtual machine. We then look for a possible grounding of these processes into thebrain. Towards this end, we map abstract de¯nitions (including logical expressions representingcompiled knowledge) into a variant of the pi-calculus. We illustrate this approach through aseries of examples extending from a purely reactive behavior to patterns of consciousness.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

The importance of sonographer experience and machine quality with regards to the role of musculoskeletal ultrasound in routine care of rheumatoid arthritis patients.

OBJECTIVES: Regarding recent progress, musculoskeletal ultrasound (US) will probably soon be integrated in standard care of patient with rheumatoid arthritis (RA). However, in daily care, quality of US machines and level of experience of sonographers are varied. We conducted a study to assess reproducibility and feasibility of an US scoring for RA, including US devices of different quality and rheumatologist with various levels of expertise in US as it would be in daily care. METHODS: The Swiss Sonography in Arthritis and Rheumatism (SONAR) group has developed a semi-quantitative score using OMERACT criteria for synovitis and erosion in RA. The score was taught to 108 rheumatologists trained in US. One year after the last workshop, 19 rheumatologists participated in the study. Scans were performed on 6 US machines ranging from low to high quality, each with a different patient. Weighted kappa was calculated for each pair of readers. RESULTS: Overall, the agreement was fair to moderate. Quality of device, experience of the sonographers and practice of the score before the study improved substantially the agreement. Agreement assessed on higher quality machine, among sonographers with good experience in US increased to substantial (median kappa for B-mode and Doppler: 0.64 and 0.41 for erosion). CONCLUSIONS: This study demonstrated feasibility and reproducibility of the Swiss US SONAR score for RA. Our results confirmed importance of the quality of US machine and the training of sonographers for the implementation of US scoring in the routine daily care of RA.

Length of the Mitral Isthmus But Not Anatomical Location of Ablation Line Predicts Bidirectional Mitral Isthmus Block in Patients Undergoing Catheter Ablation of Persistent Atrial Fibrillation: A Randomized Controlled Trial.

INTRODUCTION: Mitral isthmus (MI) ablation is an effective option in patients undergoing ablation for persistent atrial fibrillation (AF). Achieving bidirectional conduction block across the MI is challenging, and predictors of MI ablation success remain incompletely understood. We sought to determine the impact of anatomical location of the ablation line on the efficacy of MI ablation. METHODS AND RESULTS: A total of 40 consecutive patients (87% male; 54 ± 10 years) undergoing stepwise AF ablation were included. MI ablation was performed in sinus rhythm. MI ablation was performed from the left inferior PV to either the posterior (group 1) or the anterolateral (group 2) mitral annulus depending on randomization. The length of the MI line (measured with the 3D mapping system) and the amplitude of the EGMs at 3 positions on the MI were measured in each patient. MI block was achieved in 14/19 (74%) patients in group 1 and 15/21 (71%) patients in group 2 (P = NS). Total MI radiofrequency time (18 ± 7 min vs. 17 ± 8 min; P = NS) was similar between groups. Patients with incomplete MI block had a longer MI length (34 ± 6 mm vs. 24 ± 5 mm; P < 0.001), a higher bipolar voltage along the MI (1.75 ± 0.74 mV vs. 1.05 ± 0.69 mV; P < 0.01), and a longer history of continuous AF (19 ± 17 months vs. 10 ± 10 months; P < 0.05). In multivariate analysis, decreased length of the MI was an independent predictor of successful MI block (OR 1.5; 95% CI 1.1-2.1; P < 0.05). CONCLUSIONS: Increased length but not anatomical location of the MI predicts failure to achieve bidirectional MI block during ablation of persistent AF.

Could machine learning improve the prediction of pelvic nodal status of prostate cancer patients? Preliminary results of a pilot study.

We tested and compared performances of Roach formula, Partin tables and of three Machine Learning (ML) based algorithms based on decision trees in identifying N+ prostate cancer (PC). 1,555 cN0 and 50 cN+ PC were analyzed. Results were also verified on an independent population of 204 operated cN0 patients, with a known pN status (187 pN0, 17 pN1 patients). ML performed better, also when tested on the surgical population, with accuracy, specificity, and sensitivity ranging between 48-86%, 35-91%, and 17-79%, respectively. ML potentially allows better prediction of the nodal status of PC, potentially allowing a better tailoring of pelvic irradiation.

Bidirectional relationship between the Body Mass Index and Substance Use in Young Men

BACKGROUND: Obesity and substance use are major concern in young people. This study explored the bidirectional longitudinal relationships between the body mass index (BMI) of young men and their use of: 1) four classes of non-medical prescription drugs; 2) alcohol; 3) tobacco; and 4) cannabis. METHODS: Baseline and follow-up data from the Cohort Study on Substance Use Risk Factors were used (n=5,007). A cross-lagged panel model, complemented by probit models as sensitivity analysis, was run to determine the bidirectional relationships between BMI and substance use. Alcohol was assessed using risky single-occasion drinking (RSOD); tobacco, using daily smoking; and cannabis, using hazardous cannabis use (defined as twice-weekly or more cannabis use). Non-medical prescription drugs use (NMPDU) included opioid analgesics, sedatives/sleeping pills, anxiolytics and stimulants. RESULTS: Different associations were found between BMI and substance use. Only RSOD (β= -.053, p=.005) and NMPDU of anxiolytics (β=.040, p=.020) at baseline significantly predicted BMI at follow-up. Baseline RSOD predicted a lower BMI at follow-up while baseline NMPDU of anxiolytics predicted higher BMI at follow-up. Furthermore, BMI at baseline significantly predicted daily smoking (β=.050, p=.007) and hazardous cannabis use (β=.058, p=.030). CONCLUSIONS: Our results suggest different associations between BMI and the use of various substances by young men. However, only RSOD and NMPDU of anxiolytics predicted BMI, whereas BMI predicted daily smoking and hazardous cannabis use.