Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.

Multichannel mixture models for time-series analysis and classification of engagement with multiple health services: An application to psychology and physiotherapy utilization patterns after traffic accidents.

BACKGROUND: Motor vehicle accidents (MVA) represent a significant burden on health systems globally. Tens of thousands of people are injured in Australia every year and may experience significant disability. Associated economic costs are substantial. There is little literature on the health service utilization patterns of MVA patients. To fill this gap, this study has been designed to investigate temporal patterns of psychology and physiotherapy service utilization following transport-related injuries. METHOD: De-identified compensation data was provided by the Australian Transport Accident Commission. Utilization of physiotherapy and psychology services was analysed. The datasets contained 788 psychology and 3115 physiotherapy claimants and 22,522 and 118,453 episodes of service utilization, respectively. 582 claimants used both services, and their data were preprocessed to generate multidimensional time series. Time series clustering was applied using a mixture of hidden Markov models to identify the main distinct patterns of service utilization. Combinations of hidden states and clusters were evaluated and optimized using the Bayesian information criterion and interpretability. Cluster membership was further investigated using static covariates and multinomial logistic regression, and classified using high-performing classifiers (extreme gradient boosting machine, random forest and support vector machine) with 5-fold cross-validation. RESULTS: Four clusters of claimants were obtained from the clustering of the time series of service utilization. Service volumes and costs increased progressively from clusters 1 to 4. Membership of cluster 1 was positively associated with nerve damage and negatively associated with severe ABI and spinal injuries. Cluster 3 was positively associated with severe ABI, brain/head injury and psychiatric injury. Cluster 4 was positively associated with internal injuries. The classifiers were capable of classifying cluster membership with moderate to strong performance (AUC: 0.62-0.96). CONCLUSION: The available time series of post-accident psychology and physiotherapy service utilization were coalesced into four clusters that were clearly distinct in terms of patterns of utilization. In addition, pre-treatment covariates allowed prediction of a claimant's post-accident service utilization with reasonable accuracy. Such results can be useful for a range of decision-making processes, including the design of interventions aimed at improving claimant care and recovery.

Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.

Machine learning (ML) involves algorithms learning patterns in large, complex datasets to predict and classify. Algorithms include neural networks (NN), logistic regression (LR), and support vector machines (SVM). ML may generate substantial improvements in neurosurgery. This systematic review assessed the current state of neurosurgical ML applications and the performance of algorithms applied. Our systematic search strategy yielded 6866 results, 70 of which met inclusion criteria. Performance statistics analyzed included area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Natural language processing (NLP) was used to model topics across the corpus and to identify keywords within surgical subspecialties. ML applications were heterogeneous. The densest cluster of studies focused on preoperative evaluation, planning, and outcome prediction in spine surgery. The main algorithms applied were NN, LR, and SVM. Input and output features varied widely and were listed to facilitate future research. The accuracy (F(2,19) = 6.56, p < 0.01) and specificity (F(2,16) = 5.57, p < 0.01) of NN, LR, and SVM differed significantly. NN algorithms demonstrated significantly higher accuracy than LR. SVM demonstrated significantly higher specificity than LR. We found no significant difference between NN, LR, and SVM AUC and sensitivity. NLP topic modeling reached maximum coherence at seven topics, which were defined by modeling approach, surgery type, and pathology themes. Keywords captured research foci within surgical domains. ML technology accurately predicts outcomes and facilitates clinical decision-making in neurosurgery. NNs frequently outperformed other algorithms on supervised learning tasks. This study identified gaps in the literature and opportunities for future neurosurgical ML research.

Correction: Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models.

[This corrects the article DOI: 10.1371/journal.pone.0206274.].

The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene.

OBJECTIVES: (i) To demonstrate the feasibility of automated, direct observation and collection of hand hygiene data, (ii) to develop computer visual methods capable of reporting compliance with moment 1 (the performance of hand hygiene before touching a patient) and (iii) to report the diagnostic accuracy of automated, direct observation of moment 1. DESIGN: Observation of simulated hand hygiene encounters between a healthcare worker and a patient. SETTING: Computer laboratory in a university. PARTICIPANTS: Healthy volunteers. MAIN OUTCOME MEASURES: Sensitivity and specificity of automatic detection of the first moment of hand hygiene. METHODS: We captured video and depth images using a Kinect camera and developed computer visual methods to automatically detect the use of alcohol-based hand rub (ABHR), rubbing together of hands and subsequent contact of the patient by the healthcare worker using depth imagery. RESULTS: We acquired images from 18 different simulated hand hygiene encounters where the healthcare worker complied with the first moment of hand hygiene, and 8 encounters where they did not. The diagnostic accuracy of determining that ABHR was dispensed and that the patient was touched was excellent (sensitivity 100%, specificity 100%). The diagnostic accuracy of determining that the hands were rubbed together after dispensing ABHR was good (sensitivity 83%, specificity 88%). CONCLUSIONS: We have demonstrated that it is possible to automate the direct observation of hand hygiene performance in a simulated clinical setting. We used cheap, widely available consumer technology and depth imagery which potentially increases clinical application and decreases privacy concerns.

Analysis of healthcare service utilization after transport-related injuries by a mixture of hidden Markov models.

BACKGROUND: Transport injuries commonly result in significant disease burden, leading to physical disability, mental health deterioration and reduced quality of life. Analyzing the patterns of healthcare service utilization after transport injuries can provide an insight into the health of the affected parties, allow improved health system resource planning, and provide a baseline against which any future system-level interventions can be evaluated. Therefore, this research aims to use time series of service utilization provided by a compensation agency to identify groups of claimants with similar utilization patterns, describe such patterns, and characterize the groups in terms of demographic, accident type and injury type. METHODS: To achieve this aim, we have proposed an analytical framework that utilizes latent variables to describe the utilization patterns over time and group the claimants into clusters based on their service utilization time series. To perform the clustering without dismissing the temporal dimension of the time series, we have used a well-established statistical approach known as the mixture of hidden Markov models (MHMM). Ensuing the clustering, we have applied multinomial logistic regression to provide a description of the clusters against demographic, injury and accident covariates. RESULTS: We have tested our model with data on psychology service utilization from one of the main compensation agencies for transport accidents in Australia, and found that three clear clusters of service utilization can be evinced from the data. These three clusters correspond to claimants who have tended to use the services 1) only briefly after the accident; 2) for an intermediate period of time and in moderate amounts; and 3) for a sustained period of time, and intensely. The size of these clusters is approximately 67%, 27% and 6% of the number of claimants, respectively. The multinomial logistic regression analysis has showed that claimants who were 30 to 60-year-old at the time of accident, were witnesses, and who suffered a soft tissue injury were more likely to be part of the intermediate cluster than the majority cluster. Conversely, claimants who suffered more severe injuries such as a brain head injury or anon-limb fracture injury and who started their service utilization later were more likely to be part of the sustained cluster. CONCLUSION: This research has showed that clustering of service utilization time series is an effective approach for identifying the main user groups and utilization patterns of a healthcare service. In addition, using logistic regression to describe the clusters in terms of demographic, injury and accident covariates has helped identify the salient attributes of the claimants in each cluster. This finding is very important for the compensation agency and potentially other authorities as it provides a baseline to improve need understanding, resource planning and service provision.

Sequential Labeling With Structural SVM Under Nondecomposable Losses.

Sequential labeling addresses the classification of sequential data, which are widespread in fields as diverse as computer vision, finance, and genomics. The model traditionally used for sequential labeling is the hidden Markov model (HMM), where the sequence of class labels to be predicted is encoded as a Markov chain. In recent years, HMMs have benefited from minimum-loss training approaches, such as the structural support vector machine (SSVM), which, in many cases, has reported higher classification accuracy. However, the loss functions available for training are restricted to decomposable cases, such as the 0-1 loss and the Hamming loss. In many practical cases, other loss functions, such as those based on the $F_{1}$ measure, the precision/recall break-even point, and the average precision (AP), can describe desirable performance more effectively. For this reason, in this paper, we propose a training algorithm for SSVM that can minimize any loss based on the classification contingency table, and we present a training algorithm that minimizes an AP loss. Experimental results over a set of diverse and challenging data sets (TUM Kitchen, CMU Multimodal Activity, and Ozone Level Detection) show that the proposed training algorithms achieve significant improvements of the $F_{1}$ measure and AP compared with the conventional SSVM, and their performance is in line with or above that of other state-of-the-art sequential labeling approaches.

AdOn HDP-HMM: An Adaptive Online Model for Segmentation and Classification of Sequential Data.

Recent years have witnessed an increasing need for the automated classification of sequential data, such as activities of daily living, social media interactions, financial series, and others. With the continuous flow of new data, it is critical to classify the observations on-the-fly and without being limited by a predetermined number of classes. In addition, a model should be able to update its parameters in response to a possible evolution in the distributions of the classes. This compelling problem, however, does not seem to have been adequately addressed in the literature, since most studies focus on offline classification over predefined class sets. In this paper, we present a principled solution for this problem based on an adaptive online system leveraging Markov switching models and hierarchical Dirichlet process priors. This adaptive online approach is capable of classifying the sequential data over an unlimited number of classes while meeting the memory and delay constraints typical of streaming contexts. In this paper, we introduce an adaptive "learning rate" that is responsible for balancing the extent to which the model retains its previous parameters or adapts to new observations. Experimental results on stationary and evolving synthetic data and two video data sets, TUM Assistive Kitchen and collated Weizmann, show a remarkable performance in terms of segmentation and classification, particularly for sequences from evolutionary distributions and/or those containing previously unseen classes.

Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition.

BACKGROUND: Previous state-of-the-art systems on Drug Name Recognition (DNR) and Clinical Concept Extraction (CCE) have focused on a combination of text "feature engineering" and conventional machine learning algorithms such as conditional random fields and support vector machines. However, developing good features is inherently heavily time-consuming. Conversely, more modern machine learning approaches such as recurrent neural networks (RNNs) have proved capable of automatically learning effective features from either random assignments or automated word "embeddings". OBJECTIVES: (i) To create a highly accurate DNR and CCE system that avoids conventional, time-consuming feature engineering. (ii) To create richer, more specialized word embeddings by using health domain datasets such as MIMIC-III. (iii) To evaluate our systems over three contemporary datasets. METHODS: Two deep learning methods, namely the Bidirectional LSTM and the Bidirectional LSTM-CRF, are evaluated. A CRF model is set as the baseline to compare the deep learning systems to a traditional machine learning approach. The same features are used for all the models. RESULTS: We have obtained the best results with the Bidirectional LSTM-CRF model, which has outperformed all previously proposed systems. The specialized embeddings have helped to cover unusual words in DrugBank and MedLine, but not in the i2b2/VA dataset. CONCLUSIONS: We present a state-of-the-art system for DNR and CCE. Automated word embeddings has allowed us to avoid costly feature engineering and achieve higher accuracy. Nevertheless, the embeddings need to be retrained over datasets that are adequate for the domain, in order to adequately cover the domain-specific vocabulary.

Automatic temporal segment detection and affect recognition from face and body display.

Psychologists have long explored mechanisms with which humans recognize other humans' affective states from modalities, such as voice and face display. This exploration has led to the identification of the main mechanisms, including the important role played in the recognition process by the modalities' dynamics. Constrained by the human physiology, the temporal evolution of a modality appears to be well approximated by a sequence of temporal segments called onset, apex, and offset. Stemming from these findings, computer scientists, over the past 15 years, have proposed various methodologies to automate the recognition process. We note, however, two main limitations to date. The first is that much of the past research has focused on affect recognition from single modalities. The second is that even the few multimodal systems have not paid sufficient attention to the modalities' dynamics: The automatic determination of their temporal segments, their synchronization to the purpose of modality fusion, and their role in affect recognition are yet to be adequately explored. To address this issue, this paper focuses on affective face and body display, proposes a method to automatically detect their temporal segments or phases, explores whether the detection of the temporal phases can effectively support recognition of affective states, and recognizes affective states based on phase synchronization/alignment. The experimental results obtained show the following: 1) affective face and body displays are simultaneous but not strictly synchronous; 2) explicit detection of the temporal phases can improve the accuracy of affect recognition; 3) recognition from fused face and body modalities performs better than that from the face or the body modality alone; and 4) synchronized feature-level fusion achieves better performance than decision-level fusion.