Time series extrinsic regression: Predicting numeric values from time series data.

This paper studies time series extrinsic regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label. This task generalizes time series forecasting, relaxing the requirement that the value predicted be a future value of the input series or primarily depend on more recent values. In this paper, we motivate and study this task, and benchmark existing solutions and adaptations of TSC algorithms on a novel archive of 19 TSER datasets which we have assembled.

Novel Methods of Incorporating Time in Longitudinal Multivariate Analysis Reveals Hidden Associations With Disease Activity in Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. SLE is characterized by high inter-patient variability, including fluctuations over time, a factor which most biomarker studies omit from consideration. We investigated relationships between disease activity and biomarker expression in SLE, using novel methods to control for time-dependent variability, in a proof-of-concept study to evaluate whether doing so revealed additional information.

Surgical motion analysis using discriminative interpretable patterns.

Objective: The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care.

Surgical skills: Can learning curves be computed from recordings of surgical activities?

Purpose: Surgery is one of the riskiest and most important medical acts that are performed today. The need to improve patient outcomes and surgeon training, and to reduce the costs of surgery, has motivated the equipment of operating rooms with sensors that record surgical interventions. The richness and complexity of the data that are collected call for new methods to support computer-assisted surgery.

Finding discriminative and interpretable patterns in sequences of surgical activities.

Objective: Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest to understand and analyze surgical behaviors. This article addresses the issue of identifying discriminative patterns of surgical practice from recordings of surgeries.

Automatic matching of surgeries to predict surgeons' next actions.

Objective: More than half a million surgeries are performed every day worldwide, which makes surgery one of the most important component of global health care. In this context, the objective of this paper is to introduce a new method for the prediction of the possible next task that a surgeon is going to perform during surgery.

Material And Method: We formulate the problem as finding the optimal registration of a partial sequence to a complete reference sequence of surgical activities.

Non-linear temporal scaling of surgical processes.

Objective: Surgery is one of the riskiest and most important medical acts that is performed today. Understanding the ways in which surgeries are similar or different from each other is of major interest. Desires to improve patient outcomes and surgeon training, and to reduce the costs of surgery, all motivate a better understanding of surgical practices.

Improved protocols for vibrational spectroscopic analysis of body fluids.

The applications of vibrational spectroscopy to the examination of human blood serum are explored. Although FTIR spectra can be recorded in aqueous solutions at (gelatin) concentrations as low as 100 mg/L, the high-wavenumber region remains obscured by water absorption. Using Raman spectroscopy, high quality spectra of gelatine solutions as low as 10 mg/L can be achieved, also covering the high-wavenumber regions.

Law & psychiatry: the evolution of laws regulating psychiatric commitment in France.

This column reviews the evolution of French laws governing psychiatric commitment, culminating in the July 2011 Act, which was opposed by most professional organizations. The 2011 Act has maintained the two traditional French approaches to involuntary treatment: at the request of a third person and upon a decision by a prefect representing the government. However, the 2011 Act introduced major innovations into French practices: systematic review by a judge, a 72-hour observation period, and the possibility of compulsory community treatment.

Specific vs general cognitive remediation for executive functioning in schizophrenia: a multicenter randomized trial.

Background: This study assesses the benefits of an individualized therapy (RECOS program) compared with the more general cognitive remediation therapy (CRT).

Methods: 138 participants took part with 65 randomized to CRT and 73 to RECOS. In the RECOS group, participants were directed towards one of five training modules (verbal memory, visuo-spatial memory and attention, working memory, selective attention or reasoning) corresponding to their key cognitive concern whereas the CRT group received a standard program.

[The specificities of the assessment of quality of life in psychiatry].

Psychiatry, like other medical disciplines, has integrated the assessment of quality of life into the various parameters necessary for the monitoring of a patient's healthcare project. However, some specificities exist, inherent to the repercussions of the pathology on the subjective experience and its expression as well as to the patient's mode of treatment.

Discovering significant evolution patterns from satellite image time series.

Satellite Image Time Series (SITS) provide us with precious information on land cover evolution. By studying these series of images we can both understand the changes of specific areas and discover global phenomena that spread over larger areas. Changes that can occur throughout the sensing time can spread over very long periods and may have different start time and end time depending on the location, which complicates the mining and the analysis of series of images.