Artificial intelligence exceeds humans in epidemiological job coding

BACKGROUND

Work circumstances can substantially negatively impact health. To explore this, large occupational cohorts of free-text job descriptions are manually coded and linked to exposure. Although several automatic coding tools have been developed, accurate exposure assessment is only feasible with human intervention.

METHODS

We developed OPERAS, a customizable decision support system for epidemiological job coding. Using 812,522 entries, we developed and tested classification models for the Professions et Catégories Socioprofessionnelles (PCS)2003, Nomenclature d'Activités Française (NAF)2008, International Standard Classifications of Occupation (ISCO)-88, and ISCO-68. Each code comes with an estimated correctness measure to identify instances potentially requiring expert review. Here, OPERAS' decision support enables an increase in efficiency and accuracy of the coding process through code suggestions. Using the Formaldehyde, Silica, ALOHA, and DOM job-exposure matrices, we assessed the classification models' exposure assessment accuracy.

RESULTS

We show that, using expert-coded job descriptions as gold standard, OPERAS realized a 0.66-0.84, 0.62-0.81, 0.60-0.79, and 0.57-0.78 inter-coder reliability (in Cohen's Kappa) on the first, second, third, and fourth coding levels, respectively. These exceed the respective inter-coder reliability of expert coders ranging 0.59-0.76, 0.56-0.71, 0.46-0.63, 0.40-0.56 on the same levels, enabling a 75.0-98.4% exposure assessment accuracy and an estimated 19.7-55.7% minimum workload reduction.

CONCLUSIONS

OPERAS secures a high degree of accuracy in occupational classification and exposure assessment of free-text job descriptions, substantially reducing workload. As such, OPERAS significantly outperforms both expert coders and other current coding tools. This enables large-scale, efficient, and effective exposure assessment securing healthy work conditions.

The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time-frequency analysis

The spectral analysis of signals is currently either dominated by the speed-accuracy trade-off or ignores a signal's often non-stationary character. Here we introduce an open-source algorithm to calculate the fast continuous wavelet transform (fCWT). The parallel environment of fCWT separates scale-independent and scale-dependent operations, while utilizing optimized fast Fourier transforms that exploit downsampled wavelets. fCWT is benchmarked for speed against eight competitive algorithms, tested on noise resistance and validated on synthetic electroencephalography and in vivo extracellular local field potential data. fCWT is shown to have the accuracy of CWT, to have 100 times higher spectral resolution than algorithms equal in speed, to be 122 times and 34 times faster than the reference and fastest state-of-the-art implementations and we demonstrate its real-time performance, as confirmed by the real-time analysis ratio. fCWT provides an improved balance between speed and accuracy, which enables real-time, wide-band, high-quality, time-frequency analysis of non-stationary noisy signals.

A dataset of continuous affect annotations and physiological signals for emotion analysis

From a computational viewpoint, emotions continue to be intriguingly hard to understand. In research, a direct and real-time inspection in realistic settings is not possible. Discrete, indirect, post-hoc recordings are therefore the norm. As a result, proper emotion assessment remains a problematic issue. The Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as it focusses on real-time continuous annotation of emotions, as experienced by the participants, while watching various videos. For this purpose, a novel, intuitive joystick-based annotation interface was developed, that allowed for simultaneous reporting of valence and arousal, that are instead often annotated independently. In parallel, eight high quality, synchronized physiological recordings (1000 Hz, 16-bit ADC) were obtained from ECG, BVP, EMG (3x), GSR (or EDA), respiration and skin temperature sensors. The dataset consists of the physiological and annotation data from 30 participants, 15 male and 15 female, who watched several validated video-stimuli. The validity of the emotion induction, as exemplified by the annotation and physiological data, is also presented.

Fast Exact Euclidean Distance (FEED): A New Class of Adaptable Distance Transforms

A new unique class of foldable distance transforms of digital images (DT) is introduced, baptized: Fast exact euclidean distance (FEED) transforms. FEED class algorithms calculate the DT starting-directly from the definition or rather its inverse. The principle of FEED class algorithms is introduced, followed by strategies for their efficient implementation. It is shown that FEED class algorithms unite properties of ordered propagation, raster scanning, and independent scanning DT. Moreover, FEED class algorithms shown to have a unique property: they can be tailored to the images under investigation. Benchmarks are conducted on both the Fabbri et al. data set and on a newly developed data set. Three baseline, three approximate, and three state-of-the-art DT algorithms were included, in addition to two implementations of FEED class algorithms. It illustrates that FEED class algorithms i) provide truly exact Euclidean DT; ii) do no suffer from disconnected Voronoi tiles, which is a unique feature for non-parallel but fast DT; iii) outperform any other approximate and exact Euclidean DT with its time complexity O(N), even after their optimization; and iv) are unequaled in that they can be adapted to the characteristics of the image class at hand.

Considerations for emotion-aware consumer products

Emotion-aware consumer products require reliable, short-term emotion assessment (i.e., unobtrusive, robust, and lacking calibration). To explore the feasibility of this, an experiment was conducted where the galvanic skin response (GSR) and three electromyography (EMG) signals (frontalis, corrugator supercilii, and zygomaticus major) were recorded on 24 participants who watched eight 2-min emotion inducing film fragments. The unfiltered psychophysiological signals were processed and six statistical parameters (i.e., mean, absolute deviation, standard deviation, variance, skewness, and kurtosis) were derived for each 10-s interval of the film fragment. For each physiological signal, skewness and kurtosis discriminated among affective states, accompanied by other parameters, depending on the signal. The skewness parameter also showed to indicate mixed emotions. Moreover, a mapping of events in the fragments on the signals showed the importance of short-term emotion assessment. Hence, this research identified generic features, denoted important considerations, and illustrated the feasibility of emotion-aware consumer products.

Emotional Prosody Measurement (EPM): a voice-based evaluation method for psychological therapy effectiveness

The voice embodies three sources of information: speech, the identity, and the emotional state of the speaker (i.e., emotional prosody). The latter feature is resembled by the variability of the F0 (also named fundamental frequency of pitch) (SD F0). To extract this feature, Emotional Prosody Measurement (EPM) was developed, which consists of 1) speech recording, 2) removal of speckle noise, 3) a Fourier Transform to extract the F0-signal, and 4) the determination of SD F0. After a pilot study in which six participants mimicked emotions by their voice, the core experiment was conducted to see whether EPM is successful. Twenty-five patients suffering from a panic disorder with agoraphobia participated. Two methods (story-telling and reliving) were used to trigger anxiety and were compared with comparable but more relaxed conditions. This resulted in a unique database of speech samples that was used to compare the EPM with the Subjective Unit of Distress to validate it as measure for anxiety/stress. The experimental manipulation of anxiety proved to be successful and EPM proved to be a successful evaluation method for psychological therapy effectiveness.