Automatic extraction of relations between gene mutations and cancer entities occurring in the cancer literature using text mining can rapidly provide vital information to support precision cancer medicine. However, mutation-cancer relation extraction is more challenging than general relation extraction from free text, since it is often not possible without cancer-specific background knowledge and thus the model replies on a deeper understanding of complex surrounding tokens. We propose a deep learning model that jointly extracts mutations and their associated cancers. Background knowledge comes from two different knowledge bases which store different types of information about mutations. Given the different ways in which knowledge is stored in these two resources, we propose two separate methods for embedding knowledge, namely sentence-based knowledge integration and attribute-aware knowledge integration. The evaluation demonstrated that our model outperforms a number of baseline models and gains 96.00%, 92.57% and 94.57% F1 scores on three public datasets, EMU BCa, EMU PCa, and BRONCO, thus illustrating the effectiveness of our knowledge integration approach. The auxiliary experiments show that our models can utilize more informative text from the KBs and link the mutations to their corresponding cancer disease although the input text provides insufficient context.

OBJECTIVES: (1) Systematically review the literature on computerized audit and feedback (e-A&F) systems in healthcare. (2) Compare features of current systems against e-A&F best practices. (3) Generate hypotheses on how e-A&F systems may impact patient care and outcomes. METHODS: We searched MEDLINE (Ovid), EMBASE (Ovid), and CINAHL (Ebsco) databases to December 31, 2020. Two reviewers independently performed selection, extraction, and quality appraisal (Mixed Methods Appraisal Tool). System features were compared with 18 best practices derived from Clinical Performance Feedback Intervention Theory. We then used realist concepts to generate hypotheses on mechanisms of e-A&F impact. Results are reported in accordance with the PRISMA statement. RESULTS: Our search yielded 4301 unique articles. We included 88 studies evaluating 65 e-A&F systems, spanning a diverse range of clinical areas, including medical, surgical, general practice, etc. Systems adopted a median of 8 best practices (interquartile range 6-10), with 32 systems providing near real-time feedback data and 20 systems incorporating action planning. High-confidence hypotheses suggested that favorable e-A&F systems prompted specific actions, particularly enabled by timely and role-specific feedback (including patient lists and individual performance data) and embedded action plans, in order to improve system usage, care quality, and patient outcomes. CONCLUSIONS: e-A&F systems continue to be developed for many clinical applications. Yet, several systems still lack basic features recommended by best practice, such as timely feedback and action planning. Systems should focus on actionability, by providing real-time data for feedback that is specific to user roles, with embedded action plans. PROTOCOL REGISTRATION: PROSPERO CRD42016048695.

To interpret the genetic profile present in a patient sample, it is necessary to know which mutations have important roles in the development of the corresponding cancer type. Named entity recognition (NER) is a core step in the text mining pipeline which facilitates mining valuable cancer information from the scientific literature. However, due to the scarcity of related datasets, previous NER attempts in this domain either suffer from low performance when deep learning based models are deployed, or they apply feature-based machine learning models or rule-based models to tackle this problem, which requires intensive efforts from domain experts, and limit the model generalization capability. In this paper, we propose EPICURE, an ensemble pre-trained model equipped with a conditional random field pattern (CRF) layer and a span prediction pattern (Span) layer to extract cancer mutations from text. We also adopt a data augmentation strategy to expand our training set from multiple datasets. Experimental results on three benchmark datasets show competitive results compared to the baseline models, validating our model's effectiveness and advances in generalization capability.

OBJECTIVE: Informative presence (IP) is the phenomenon whereby the presence or absence of patient data is potentially informative with respect to their health condition, with informative observation (IO) being the longitudinal equivalent. These phenomena predominantly exist within routinely collected healthcare data, in which data collection is driven by the clinical requirements of patients and clinicians. The extent to which IP and IO are considered when using such data to develop clinical prediction models (CPMs) is unknown, as is the existing methodology aiming at handling these issues. This review aims to synthesize such existing methodology, thereby helping identify an agenda for future methodological work. MATERIALS AND METHODS: A systematic literature search was conducted by 2 independent reviewers using prespecified keywords. RESULTS: Thirty-six articles were included. We categorized the methods presented within as derived predictors (including some representation of the measurement process as a predictor in the model), modeling under IP, and latent structures. Including missing indicators or summary measures as predictors is the most commonly presented approach amongst the included studies (24 of 36 articles). DISCUSSION: This is the first review to collate the literature in this area under a prediction framework. A considerable body relevant of literature exists, and we present ways in which the described methods could be developed further. Guidance is required for specifying the conditions under which each method should be used to enable applied prediction modelers to use these methods. CONCLUSIONS: A growing recognition of IP and IO exists within the literature, and methodology is increasingly becoming available to leverage these phenomena for prediction purposes. IP and IO should be approached differently in a prediction context than when the primary goal is explanation. The work included in this review has demonstrated theoretical and empirical benefits of incorporating IP and IO, and therefore we recommend that applied health researchers consider incorporating these methods in their work.

OBJECTIVE: To investigate how the general public trades off explainability versus accuracy of artificial intelligence (AI) systems and whether this differs between healthcare and non-healthcare scenarios. MATERIALS AND METHODS: Citizens' juries are a form of deliberative democracy eliciting informed judgment from a representative sample of the general public around policy questions. We organized two 5-day citizens' juries in the UK with 18 jurors each. Jurors considered 3 AI systems with different levels of accuracy and explainability in 2 healthcare and 2 non-healthcare scenarios. Per scenario, jurors voted for their preferred system; votes were analyzed descriptively. Qualitative data on considerations behind their preferences included transcribed audio-recordings of plenary sessions, observational field notes, outputs from small group work and free-text comments accompanying jurors' votes; qualitative data were analyzed thematically by scenario, per and across AI systems. RESULTS: In healthcare scenarios, jurors favored accuracy over explainability, whereas in non-healthcare contexts they either valued explainability equally to, or more than, accuracy. Jurors' considerations in favor of accuracy regarded the impact of decisions on individuals and society, and the potential to increase efficiency of services. Reasons for emphasizing explainability included increased opportunities for individuals and society to learn and improve future prospects and enhanced ability for humans to identify and resolve system biases. CONCLUSION: Citizens may value explainability of AI systems in healthcare less than in non-healthcare domains and less than often assumed by professionals, especially when weighed against system accuracy. The public should therefore be actively consulted when developing policy on AI explainability.

Multi-morbidity, the health state of having two or more concurrent chronic conditions, is becoming more common as populations age, but is poorly understood. Identifying and understanding commonly occurring sets of diseases is important to inform clinical decisions to improve patient services and outcomes. Network analysis has been previously used to investigate multi-morbidity, but a classic application only allows for information on binary sets of diseases to contribute to the graph. We propose the use of hypergraphs, which allows for the incorporation of data on people with any number of conditions, and also allows us to obtain a quantitative understanding of the centrality, a measure of how well connected items in the network are to each other, of both single diseases and sets of conditions. Using this framework we illustrate its application with the set of conditions described in the Charlson morbidity index using data extracted from routinely collected population-scale, patient level electronic health records (EHR) for a cohort of adults in Wales, UK. Stroke and diabetes were found to be the most central single conditions. Sets of diseases featuring diabetes; diabetes with Chronic Pulmonary Disease, Renal Disease, Congestive Heart Failure and Cancer were the most central pairs of diseases. We investigated the differences between results obtained from the hypergraph and a classic binary graph and found that the centrality of diseases such as paraplegia, which are connected strongly to a single other disease is exaggerated in binary graphs compared to hypergraphs. The measure of centrality is derived from the weighting metrics calculated for disease sets and further investigation is needed to better understand the effect of the metric used in identifying the clinical significance and ranked centrality of grouped diseases. These initial results indicate that hypergraphs can be used as a valuable tool for analysing previously poorly understood relationships and information available in EHR data.

Wearable and mobile technology provides new opportunities to manage health conditions remotely and unobtrusively. For example, healthcare providers can repeatedly sample a person's condition to monitor progression of symptoms and intervene if necessary. There is usually a utility-tolerability trade-off between collecting information at sufficient frequencies and quantities to be useful, and over-burdening the user or the underlying technology, particularly when active input is required from the user. Selecting the next sampling time adaptively using previous responses, so that people are only sampled at high frequency when necessary, can help to manage this trade-off. We present a novel approach to adaptive sampling using clustered continuous-time hidden Markov models. The model predicts, at any given sampling time, the probability of moving to an 'alert' state, and the next sample time is scheduled when this probability has exceeded a given threshold. The clusters, each representing a distinct sub-model, allow heterogeneity in states and state transitions. The work is illustrated using longitudinal mental-health symptom data in 49 people collected using ClinTouch, a mobile app designed to monitor people with a diagnosis of schizophrenia. Using these data, we show how the adaptive sampling scheme behaves under different model parameters and risk thresholds, and how the average sampling can be substantially reduced whilst maintaining a high sampling frequency during high-risk periods.

Temporal phenotyping enables clinicians to better understand observable characteristics of a disease as it progresses. Modelling disease progression that captures interactions between phenotypes is inherently challenging. Temporal models that capture change in disease over time can identify the key features that characterize disease subtypes that underpin these trajectories. These models will enable clinicians to identify early warning signs of progression in specific sub-types and therefore to make informed decisions tailored to individual patients. In this paper, we explore two approaches to building temporal phenotypes based on the topology of data: topological data analysis and pseudo time-series. Using type 2 diabetes data, we show that the topological data analysis approach is able to identify disease trajectories and that pseudo time-series can infer a state space model characterized by transitions between hidden states that represent distinct temporal phenotypes. Both approaches highlight lipid profiles as key factors in distinguishing the phenotypes.

Ecological Momentary Assessment (EMA) tools are used to monitor the thoughts and feelings of people in their everyday lives over time. In this paper we examine the feasibility of multi-item, multi-subject Hidden Markov Models (HMMs) to identify response clusters in people with schizophrenia. Data comprise 49 participants from two randomised clinical trials using the mobile app ClinTouch, an EMA tool for daily monitoring of schizophrenia symptoms. The app was used for up to 12 weeks (median follow-up 83 days, 78% response rate). We find that a 3-cluster model with 3 states per cluster performs best amongst the configurations tested, and the feasibility of HMMs as applied to multi-item EMA data is demonstrated. However, there is substantial heterogeneity between participants within each hidden state for which sampling error due to short observation periods is a likely contributor. More data are needed to validate and refine the modelling approach taken here.

OBJECTIVE: The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention. MATERIALS AND METHODS: We searched MEDLINE, PsycINFO, and Scopus by combining terms related to geolocation and smartphones with SMI concepts. Study selection and data extraction were done in duplicate. RESULTS: Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. CONCLUSIONS: The growing body of evidence for the association between SMI concepts and geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.

Scanpath analysis is a controversial and important topic in eye tracking research. Previous work has shown the value of scanpath analysis in perceptual tasks; little research has examined its utility for understanding human reasoning in complex tasks. Here, we analyze n-grams, which are continuous ordered subsequences of participants' scanpaths. In particular we studied the length of n-grams that are most appropriate for this form of analysis. We reuse datasets from previous studies of human cognition, medical diagnosis and art, systematically analyzing the frequency of n-grams of increasing length, and compare this approach with a string alignment-based method. The results show that subsequences of four or more areas of interest may not be of value for finding patterns that distinguish between two groups. The study is the first to systematically define the parameters of the length of n-gram suitable for analysis, using an approach that holds across diverse domains.

INTRODUCTION: The construction of reliable, reusable clinical code sets is essential when re-using Electronic Health Record (EHR) data for research. Yet code set definitions are rarely transparent and their sharing is almost non-existent. There is a lack of methodological standards for the management (construction, sharing, revision and reuse) of clinical code sets which needs to be addressed to ensure the reliability and credibility of studies which use code sets. OBJECTIVE: To review methodological literature on the management of sets of clinical codes used in research on clinical databases and to provide a list of best practice recommendations for future studies and software tools. METHODS: We performed an exhaustive search for methodological papers about clinical code set engineering for re-using EHR data in research. This was supplemented with papers identified by snowball sampling. In addition, a list of e-phenotyping systems was constructed by merging references from several systematic reviews on this topic, and the processes adopted by those systems for code set management was reviewed. RESULTS: Thirty methodological papers were reviewed. Common approaches included: creating an initial list of synonyms for the condition of interest (n=20); making use of the hierarchical nature of coding terminologies during searching (n=23); reviewing sets with clinician input (n=20); and reusing and updating an existing code set (n=20). Several open source software tools (n=3) were discovered. DISCUSSION: There is a need for software tools that enable users to easily and quickly create, revise, extend, review and share code sets and we provide a list of recommendations for their design and implementation. CONCLUSION: Research re-using EHR data could be improved through the further development, more widespread use and routine reporting of the methods by which clinical codes were selected.

Risk of psychotic relapse in schizophrenic patients is commonly measured by social functioning (SF), which focuses on patients' daily activities. Monitoring of SF usually relies on infrequent clinic visits, limiting the capacity to detect sudden changes. GPS data that is passively collected with smartphones introduce new opportunities to monitor SF. We conducted a five-day pilot study with five schizophrenic patients to assess the feasibility of this approach. Participants used a smartphone to continuously record their GPS location, and completed a paper-based SF diary to register out-of-home activities. We implemented a time-based method and a density-based method to identify the geolocations visited and then we clustered geolocations visited in places visited. Finally, we used semantic enrichment to classify places types and associated activities. We evaluated the performance of the two approaches by comparing the activities detected from the GPS data with those recorded in the SF diary. Recall was better for the density-based method, ranging from 0.686 (Standard Deviation [SD] 0.168) to 0.771 (SD 0.264) while precision was better for the time-based method (0.722 (SD 0.197) to 0.954 (SD 0.093)). To conclude, using routinely collected GPS data and relatively simple analytical methods we detected patients' out-of-home activities with moderate recall, more sophisticated analytical methods may obtain better performance.

OBJECTIVE: To investigate the agreement among clinical experts in their judgments of monitoring data with respect to artifacts, and to examine the effect of reference standards that consist of individual and joint expert judgments on the performance of artifact filters. DESIGN: Individual judgments of four physicians, a majority vote judgment, and a consensus judgment were obtained for 30 time series of three monitoring variables: mean arterial blood pressure (ABPm), central venous pressure (CVP), and heart rate (HR). The individual and joint judgments were used to tune three existing automated filtering methods and to evaluate the performance of the resulting filters. MEASUREMENTS: The interrater agreement was calculated in terms of positive specific agreement (PSA). The performance of the artifact filters was quantified in terms of sensitivity and positive predictive value (PPV). RESULTS: PSA values between 0.33 and 0.85 were observed among clinical experts in their selection of artifacts, with relatively high values for CVP data. Artifact filters developed using judgments of individual experts were found to moderately generalize to new time series and other experts; sensitivity values ranged from 0.40 to 0.60 for ABPm and HR filters (PPV: 0.57-0.84), and from 0.63 to 0.80 for CVP filters (PPV: 0.71-0.86). A higher performance value for the filters was found for the three variable types when joint judgments were used for tuning the filtering methods. CONCLUSION: Given the disagreement among experts in their individual judgment of monitoring data with respect to artifacts, the use of joint reference standards obtained from multiple experts is recommended for development of automatic artifact filters.