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. Our results show that the state-of-the-art TSC algorithm Rocket, when adapted for regression, achieves the highest overall accuracy compared to adaptations of other TSC algorithms and state-of-the-art machine learning (ML) algorithms such as XGBoost, Random Forest and Support Vector Regression. More importantly, we show that much research is needed in this field to improve the accuracy of ML models. We also find evidence that further research has excellent prospects of improving upon these straightforward baselines.

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

Objective: 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. Methods: We measured 4 serum biomarkers (MIF, CCL2, CCL19, and CXCL10) and 13 routine clinical laboratory parameters, alongside disease activity measured by the SLE disease activity index-2k (SLEDAI-2k), collected longitudinally. We analyzed these data with unsupervised learning methods via ensemble clustering, incorporating temporal relationships using dynamic time warping for distance metric calculation. Results: Data from 843 visits in 110 patients (median age 47, 83% female) demonstrated highly heterogeneous time-dependent relationships between disease activity and biomarkers. Unbiased magnitude-based hierarchical clustering of biomarker expression levels isolated a patient subset (n = 9) with distinctively heterogeneous expression of the 17 biological parameters, and who had MIF, CCL2, CCL19, and CXCL10 levels that were higher and more strongly associated with disease activity, based on leave-one-out cross-validated regression analysis. In the remaining subgroup, a time-dependent regression model revealed significantly stronger predictive power of biomarkers for disease activity, compared to a time-agnostic regression model. Despite no significant difference in simple magnitude, using dynamic time warping analysis to align longitudinal profiles revealed a large subset (n = 69) with significantly stronger associations between biological parameters and disease activity. This subgroup had significantly lower flare rates, disease activity and damage scores, suggesting this clustering is clinically meaningful. Conclusions: These results suggest associations between biological parameters and disease activity in SLE exist in a multi-dimensional time-dependent pattern, with implications for the analysis of biomarkers in SLE often used to identify therapeutic targets. Novel methods to analyse high-dimensional data and control for time-dependent variability may have broad utility in the study complex relationships between clinical and biological parameters.

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. MATERIAL AND METHOD: In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency. RESULTS: We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify individual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment. CONCLUSIONS: The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect.

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. The aim of this paper is to support the monitoring of junior surgeons learning their surgical skill sets. METHODS: Our method is fully automatic and takes as input a series of surgical interventions each represented by a low-level recording of all activities performed by the surgeon during the intervention (e.g., cut the skin with a scalpel). Our method produces a curve describing the process of standardization of the behavior of junior surgeons. Given the fact that junior surgeons receive constant feedback from senior surgeons during surgery, these curves can be directly interpreted as learning curves. RESULTS: Our method is assessed using the behavior of a junior surgeon in anterior cervical discectomy and fusion surgery over his first three years after residency. They revealed the ability of the method to accurately represent the surgical skill evolution. We also showed that the learning curves can be computed by phases allowing a finer evaluation of the skill progression. CONCLUSION: Preliminary results suggest that our approach constitutes a useful addition to surgical training monitoring.

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. These recordings are sequences of low-level surgical activities representing the actions performed by surgeons during surgeries. MATERIALS AND METHOD: To discover patterns that are specific to a group of surgeries, we use the vector space model (VSM) which is originally an algebraic model for representing text documents. We split long sequences of surgical activities into subsequences of consecutive activities. We then compute the relative frequencies of these subsequences using the tf*idf framework and we use the Cosine similarity to classify the sequences. This process makes it possible to discover which patterns discriminate one set of surgeries recordings from another set. RESULTS: Experiments were performed on 40 neurosurgeries of anterior cervical discectomy (ACD). The results demonstrate that our method accurately identifies patterns that can discriminate between (1) locations where the surgery took place, (2) levels of expertise of surgeons (i.e., expert vs. intermediate) and even (3) individual surgeons who performed the intervention. We also show how the tf*idf weight vector can be used to both visualize the most interesting patterns and to highlight the parts of a given surgery that are the most interesting. CONCLUSIONS: Identifying patterns that discriminate groups of surgeon is a very important step in improving the understanding of surgical processes. The proposed method finds discriminative and interpretable patterns in sequences of surgical activities. Our approach provides intuitive results, as it identifies automatically the set of patterns explaining the differences between the groups.

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. We propose an efficient algorithm to find the optimal partial alignment and a prediction system using maximum a posteriori probability estimation and filtering. We also introduce a weighting scheme allowing to improve the predictions by taking into account the relative similarity between the current surgery and a set of pre-recorded surgeries. RESULTS: Our method is evaluated on two types of neurosurgical procedures: lumbar disc herniation removal and anterior cervical discectomy. Results show that our method outperformed the state of the art by predicting the next task that the surgeon will perform with 95% accuracy. CONCLUSIONS: This work shows that, even from the low-level description of surgeries and without other sources of information, it is often possible to predict the next surgical task when the conditions are consistent with the previously recorded surgeries. We also showed that our method is able to assess when there is actually a large divergence between the predictions and decide that it is not reasonable to make a prediction.

Toward Electronic Surveillance of Invasive Mold Diseases in Hematology-Oncology Patients: An Expert System Combining Natural Language Processing of Chest Computed Tomography Reports, Microbiology, and Antifungal Drug Data.

PURPOSE: Prospective epidemiologic surveillance of invasive mold disease (IMD) in hematology patients is hampered by the absence of a reliable laboratory prompt. This study develops an expert system for electronic surveillance of IMD that combines probabilities using natural language processing (NLP) of computed tomography (CT) reports with microbiology and antifungal drug data to improve prediction of IMD. METHODS: Microbiology indicators and antifungal drug-dispensing data were extracted from hospital information systems at three tertiary hospitals for 123 hematology-oncology patients. Of this group, 64 case patients had 26 probable/proven IMD according to international definitions, and 59 patients were uninfected controls. Derived probabilities from NLP combined with medical expertise identified patients at high likelihood of IMD, with remaining patients processed by a machine-learning classifier trained on all available features. RESULTS: Compared with the baseline text classifier, the expert system that incorporated the best performing algorithm (naïve Bayes) improved specificity from 50.8% (95% CI, 37.5% to 64.1%) to 74.6% (95% CI, 61.6% to 85.0%), reducing false positives by 48% from 29 to 15; improved sensitivity slightly from 96.9% (95% CI, 89.2% to 99.6%) to 98.4% (95% CI, 91.6% to 100%); and improved receiver operating characteristic area from 73.9% (95% CI, 67.1% to 80.6%) to 92.8% (95% CI, 88% to 97.5%). CONCLUSION: An expert system that uses multiple sources of data (CT reports, microbiology, antifungal drug dispensing) is a promising approach to continuous prospective surveillance of IMD in the hospital, and demonstrates reduced false notifications (positives) compared with NLP of CT reports alone. Our expert system could provide decision support for IMD surveillance, which is critical to antifungal stewardship and improving supportive care in cancer.

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. To facilitate this, surgeons have started recording the activities that are performed during surgery. New methods have to be developed to be able to make the most of this extremely rich and complex data. The objective of this work is to enable the simultaneous comparison of a set of surgeries, in order to be able to extract high-level information about surgical practices. MATERIALS AND METHOD: We introduce non-linear temporal scaling (NLTS): a method that finds a multiple alignment of a set of surgeries. Experiments are carried out on a set of lumbar disc neurosurgeries. We assess our method both on a highly standardised phase of the surgery (closure) and on the whole surgery. RESULTS: Experiments show that NLTS makes it possible to consistently derive standards of surgical practice and to understand differences between groups of surgeries. We take the training of surgeons as the common theme for the evaluation of the results and highlight, for example, the main differences between the practices of junior and senior surgeons in the removal of a lumbar disc herniation. CONCLUSIONS: NLTS is an effective and efficient method to find a multiple alignment of a set of surgeries. NLTS realigns a set of sequences along their intrinsic timeline, which makes it possible to extract standards 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. In human serum, spectral profiles are weak and partially obscured by water features. Dried deposits are shown to be physically and chemically inhomogeneous resulting in reduced measurement reproducibility. Concentration of the serum using commercially available centrifugal filter devices results in an improvement in the spectral intensity and quality. Additionally, in Raman spectroscopy, reduced background and significantly enhanced signal collection is achievable by measurement in an inverted geometry. The improved protocols for spectroscopic measurement of human serum are applicable to a range of bodily fluids and should accelerate potential clinical applications.

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 main outcome was the total score on BADS (Behavioural Assessment of Dysexecutive Syndrome) and the secondary outcomes were: cognition (executive functions; selective attention; visuospatial memory and attention; verbal memory; working memory) and clinical measures (symptoms; insight; neurocognitive complaints; self-esteem). All outcomes were assessed at baseline (T1), week 12 (posttherapy, T2), and follow-up (week 36, i.e., 6months posttherapy, T3). RESULTS: No difference was shown for the main outcome. A significant improvement was found for BADS' profile score for RECOS at T2 and T3, and for CRT at T3. Change in BADS in the RECOS and CRT arms were not significantly different between T1 and T2 (+0.86, p=0.108), or between T1 and T3 (+0.36, p=0.540). Significant improvements were found in several secondary outcomes including cognition (executive functions, selective attention, verbal memory, and visuospatial abilities) and clinician measures (symptoms and awareness to be hampered by cognitive deficits in everyday) in both treatment arms following treatment. Self-esteem improved only in RECOS arm at T3, and working memory improved only in CRT arm at T2 and T3, but there were no differences in changes between arms. CONCLUSIONS: RECOS (specific remediation) and CRT (general remediation) globally showed similar efficacy in the present trial.

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. This work focuses on frequent sequential pattern mining (FSPM) methods, since this family of methods fits the above-mentioned issues. This family of methods consists of finding the most frequent evolution behaviors, and is actually able to extract long-term changes as well as short term ones, whenever the change may start and end. However, applying FSPM methods to SITS implies confronting two main challenges, related to the characteristics of SITS and the domain's constraints. First, satellite images associate multiple measures with a single pixel (the radiometric levels of different wavelengths corresponding to infra-red, red, etc.), which makes the search space multi-dimensional and thus requires specific mining algorithms. Furthermore, the non evolving regions, which are the vast majority and overwhelm the evolving ones, challenge the discovery of these patterns. We propose a SITS mining framework that enables discovery of these patterns despite these constraints and characteristics. Our proposal is inspired from FSPM and provides a relevant visualization principle. Experiments carried out on 35 images sensed over 20 years show the proposed approach makes it possible to extract relevant evolution behaviors.

[The specificities of the assessment of quality of life in psychiatry]. / Les spécificités de l'evaluation de la qualité de vie en psychiatrie.

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.