Toward Suicidal Ideation Detection with Lexical Network Features and Machine Learning

In this study, we introduce a new network feature for detecting suicidal ideation from clinical texts and conduct various additional experiments to enrich the state of knowledge. We evaluate statistical features with and without stopwords, use lexical networks for feature extraction and classification, and compare the results with standard machine learning methods using a logistic classifier, a neural network, and a deep learning method. We utilize three text collections. The first two contain transcriptions of interviews conducted by experts with suicidal (n=161 patients that experienced severe ideation) and control subjects (n=153). The third collection consists of interviews conducted by experts with epilepsy patients, with a few of them admitting to experiencing suicidal ideation in the past (32 suicidal and 77 control). The selected methods detect suicidal ideation with an average area under the curve (AUC) score of 95% on the merged collection with high suicidal ideation, and the trained models generalize over the third collection with an average AUC score of 69%. Results reveal that lexical networks are promising for classification and feature extraction as successful as the deep learning model. We also observe that a logistic classifier’s performance was comparable with the deep learning method while promising explainability

A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support

Abstract Background Probabilistic assessments of clinical care are essential for quality care. Yet, machine learning, which supports this care process has been limited to categorical results. To maximize its usefulness, it is important to find novel approaches that calibrate the ML output with a likelihood scale. Current state-of-the-art calibration methods are generally accurate and applicable to many ML models, but improved granularity and accuracy of such methods would increase the information available for clinical decision making. This novel non-parametric Bayesian approach is demonstrated on a variety of data sets, including simulated classifier outputs, biomedical data sets from the University of California, Irvine (UCI) Machine Learning Repository, and a clinical data set built to determine suicide risk from the language of emergency department patients. Results The method is first demonstrated on support-vector machine (SVM) models, which generally produce well-behaved, well understood scores. The method produces calibrations that are comparable to the state-of-the-art Bayesian Binning in Quantiles (BBQ) method when the SVM models are able to effectively separate cases and controls. However, as the SVM models’ ability to discriminate classes decreases, our approach yields more granular and dynamic calibrated probabilities comparing to the BBQ method. Improvements in granularity and range are even more dramatic when the discrimination between the classes is artificially degraded by replacing the SVM model with an ad hoc k-means classifier. Conclusions The method allows both clinicians and patients to have a more nuanced view of the output of an ML model, allowing better decision making. The method is demonstrated on simulated data, various biomedical data sets and a clinical data set, to which diverse ML methods are applied. Trivially extending the method to (non-ML) clinical scores is also discussed

Listeria monocytogenes transiently alters mitochondrial dynamics during infection

Mitochondria are essential and highly dynamic organelles, constantly undergoing fusion and fission. We analyzed mitochondrial dynamics during infection with the human bacterial pathogen Listeria monocytogenes and show that this infection profoundly alters mitochondrial dynamics by causing transient mitochondrial network fragmentation. Mitochondrial fragmentation is specific to pathogenic Listeria monocytogenes, and it is not observed with the nonpathogenic Listeria innocua species or several other intracellular pathogens. Strikingly, the efficiency of Listeria infection is affected in cells where either mitochondrial fusion or fission has been altered by siRNA treatment, highlighting the relevance of mitochondrial dynamics for Listeria infection. We identified the secreted pore-forming toxin listeriolysin O as the bacterial factor mainly responsible for mitochondrial network disruption and mitochondrial function modulation. Together, our results suggest that the transient shutdown of mitochondrial function and dynamics represents a strategy used by Listeria at the onset of infection to interfere with cellular physiology