SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment

Objective: In an ageing world population more citizens are at risk of cognitive impairment, with negative consequences on their ability of independent living, quality of life and sustainability of healthcare systems. Cognitive neuroscience researchers have identified behavioral anomalies that are significant indicators of cognitive decline. A general goal is the design of innovative methods and tools for continuously monitoring the functional abilities of the seniors at risk and reporting the behavioral anomalies to the clinicians. SmartFABER is a pervasive system targeting this objective. Methods: A non-intrusive sensor network continuously acquires data about the interaction of the senior with the home environment during daily activities. A novel hybrid statistical and knowledge-based technique is used to analyses this data and detect the behavioral anomalies, whose history is presented through a dashboard to the clinicians. Differently from related works, SmartFABER can detect abnormal behaviors at a fine-grained level. Results: We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates

Ensembling classical machine learning and deep learning approaches for morbidity identification from clinical notes

The past decade has seen an explosion of the amount of digital information generated within the healthcare domain. Digital data exist in the form of images, video, speech, transcripts, electronic health records, clinical records, and free-text. Analysis and interpretation of healthcare data is a daunting task, and it demands a great deal of time, resources, and human effort. In this paper, we focus on the problem of co-morbidity recognition from patient’s clinical records. To this aim, we employ both classical machine learning and deep learning approaches.We use word embeddings and bag-of-words representations, coupled with feature selection techniques. The goal of our work is to develop a classification system to identify whether a certain health condition occurs for a patient by studying his/her past clinical records. In more detail, we have used pre-trained word2vec, domain-trained, GloVe, fastText, and universal sentence encoder embeddings to tackle the classification of sixteen morbidity conditions within clinical records. We have compared the outcomes of classical machine learning and deep learning approaches with the employed feature representation methods and feature selection methods. We present a comprehensive discussion of the performances and behaviour of the employed classical machine learning and deep learning approaches. Finally, we have also used ensemble learning techniques over a large number of combinations of classifiers to improve the single model performance. For our experiments, we used the n2c2 natural language processing research dataset, released by Harvard Medical School. The dataset is in the form of clinical notes that contain patient discharge summaries. Given the unbalancedness of the data and their small size, the experimental results indicate the advantage of the ensemble learning technique with respect to single classifier models. In particular, the ensemble learning technique has slightly improved the performances of single classification models but has greatly reduced the variance of predictions stabilizing the accuracies (i.e., the lower standard deviation in comparison with single classifiers). In real-life scenarios, our work can be employed to identify with high accuracy morbidity conditions of patients by feeding our tool with their current clinical notes. Moreover, other domains where classification is a common problem might benefit from our approach as well

TF-IDF vs word embeddings for morbidity identification in clinical notes: An initial study

Today, we are seeing an ever-increasing number of clinical notes that contain clinical results, images, and textual descriptions of patient's health state. All these data can be analyzed and employed to cater novel services that can help people and domain experts with their common healthcare tasks. However, many technologies such as Deep Learning and tools like Word Embeddings have started to be investigated only recently, and many challenges remain open when it comes to healthcare domain applications. To address these challenges, we propose the use of Deep Learning and Word Embeddings for identifying sixteen morbidity types within textual descriptions of clinical records. For this purpose, we have used a Deep Learning model based on Bidirectional Long-Short Term Memory (LSTM) layers which can exploit state-of-the-art vector representations of data such as Word Embeddings. We have employed pre-trained Word Embeddings namely GloVe and Word2Vec, and our own Word Embeddings trained on the target domain. Furthermore, we have compared the performances of the deep learning approaches against the traditional tf-idf using Support Vector Machine and Multilayer perceptron (our baselines). From the obtained results it seems that the latter outperform the combination of Deep Learning approaches using any word embeddings. Our preliminary results indicate that there are specific features that make the dataset biased in favour of traditional machine learning approaches

K-LM: Knowledge Augmenting in Language Models Within the Scholarly Domain

The use of superior algorithms and complex architectures in language models have successfully imparted human-like abilities to machines for specific tasks. But two significant constraints, the available training data size and the understanding of domain-specific context, hamper the pre-trained language models from optimal and reliable performance. A potential solution to tackle these limitations is to equip the language models with domain knowledge. While the commonly adopted techniques use Knowledge Graphs Embeddings (KGEs) to inject domain knowledge, we provide a Knowledge Language Model (K-LM) to use the Resource Description Framework (RDF) triples directly, extracted from world knowledge bases. The proposed model works in conjunction with Generative Pretrained Transformer (GPT-2) and Bidirectional Encoder Representations from Transformers (BERT) and uses a well-defined pipeline to select, categorize, and filter the RDF triples. In addition, we introduce heuristic methods to inject domain-specific knowledge in K-LM, leveraging knowledge graphs (KGs). We tested our approaches on the classification task within the scholarly domain using two KGs, and our results show that our proposed language model has significantly outperformed the baselines and BERT for each KG. Our experimental findings also help us conclude the importance of relevance of KG used over the quantity of injected RDF triples. Also, each of our proposed methods for injecting the RDF triples has increased the overall model's accuracy, demonstrating that K-LM is a potential choice for domain adaptation to solve knowledge-driven problems

Towards Low-Resource Real-Time Assessment of Empathy in Counselling

Gauging therapist empathy in counselling is an important component of understanding counselling quality. While session-level empathy assessment based on machine learning has been investigated extensively, it relies on relatively large amounts of well-annotated dialogue data, and real-time evaluation has been overlooked in the past. In this paper, we focus on the task of low-resource utterance-level binary empathy assessment. We train deep learning models on heuristically constructed empathy vs. non-empathy contrast in general conversations, and apply the models directly to therapeutic dialogues, assuming correlation between empathy manifested in those two domains. We show that such training yields poor performance in general, probe its causes, and examine the actual effect of learning from empathy contrast in general conversation

Anno-MI: A Dataset of Expert-Annotated Counselling Dialogues

Research on natural language processing for counselling dialogue analysis has seen substantial development in recent years, but access to this area remains extremely limited due to the lack of publicly available expert-annotated therapy conversations. In this work, we introduce AnnoMI, the first publicly and freely accessible dataset of professionally transcribed and expert-annotated therapy dialogues. It consists of 133 conversations that demonstrate high- and low-quality motivational interviewing (MI), an effective counselling technique, and the annotations by domain experts cover key MI attributes. We detail the data collection process including dialogue selection, transcription and annotation. We also present analyses of AnnoMI and discuss its potential applications

Integrative effect of activated biochar to reduce water stress impact and enhance antioxidant capacity in crops

Biochar is a soil amendment that can change soil's physical and hydraulic properties. However, biochar application is far from being a ‘one size fits-all’ approach. The impact of the management practices is dependent on biochar type (feedstock and production conditions), application depth and method, climate and site characteristics. Hence, this study aims to enrich the available inconclusive information on how biochar could affect clay loamy soil and to assess the potential impact of the induced change on water stress mitigation of rain-fed durum wheat under the specific condition of the semi-arid environment of North West of Tunisia. A field experiment was investigated in which three biochar rates 0 (B0), 10 (equivalent to 0.5% of weight) (B1) and 20 t/ha (equivalent to 1% of weight), (B2), were tested. Other laboratory analysis allowed the evaluation of soil water retention curve (SWRC), saturated hydraulic conductivity (Ks), dry density (ρb) and biostress biomarkers such as glutathione-S-transferase (GST), catalase activities (CAT) and malondialdehyde content (MDA) as well as yield attributes. Results showed that treatment B2 significantly decreased ρb and Ks with relative change values of about −3.1% and −19%. Consequently, SWRC showed a better water retention capacity, mostly from saturation to matric potential value (h) of 33 kPa. Total (TAWC), plant (PAWC) and readily (RAWC) available water contents, significantly increased under B2 with relative changes of +6%, +44% and +44% respectively. Moreover, GST and CAT were also boosted under B2. Consequently, biological and grain yields as well as grain water use efficiency (GWUE) significantly increased. GWUE increased from 0.81 ± 0.04 in B0 to 1.09 ± 0.01 kg/m3 in B2. The correlation analysis showed a significant and positive correlation, between GWUE and soil water parameters (θs, θfc and θmre) suggesting the indirect effect of biochar on water-use efficiency for grain yield of wheat. Therefore, among the tested rates 20 t/ha could be suggested to improve plant soil water availability and reduce the harmful impact of drought stress on rain-fed durum wheat