Explaining Explainable Artificial Intelligence: An integrative model of objective and subjective influences on XAI

Explainable artificial intelligence (XAI) is a new field within artificial intelligence (AI) and machine learning (ML). XAI offers a transparency of AI and ML that can bridge the gap in information that has been absent from “black-box” ML models. Given its nascency, there are several taxonomies of XAI in the literature. The current paper incorporates the taxonomies in the literature into one unifying framework, which defines the types of explanations, types of transparency, and model methods that together inform the user’s processes towards developing trust in AI and ML systems

Clinical text data in machine learning: Systematic review

Background: Clinical narratives represent the main form of communication within healthcare providing a personalized account of patient history and assessments, offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study is to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigate the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 110 relevant studies and extracted information about the text data used to support machine learning, the NLP tasks supported and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation and any relevant statistics. Results: The vast majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable due to sensitive nature of data considered. Beside the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The vast majority of studies focused on the task of text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which does not require data annotation

Improving Providers’ Survival Estimates and Selection of Prognosis- and Guidelines-Appropriate Radiotherapy Regimens for Patients with Symptomatic Bone Metastases: Development and Evaluation of the BMETS Model and Decision Support Platform

In the management of symptomatic bone metastases, selection of appropriate palliative radiotherapy (RT) regimens should be based on patient-specific characteristics including estimated survival time. Yet, provider predictions of patient survival are notoriously inaccurate. Moreover, available evidence- and consensus-based guidelines do not provide clear criteria for selecting between the range of palliative RT regimens available. In an effort to improve selection of prognosis- and guidelines-appropriate palliative bone treatments, we developed the Bone Metastases Ensemble Trees for Survival (BMETS) model. Built using an institutional database of 397 patients seen in consultation for symptomatic bone metastases, this machine-learning model estimates survival time following RT consultation using 27 prognostic covariates. Cross validations procedures revealed excellent discrimination for survival, and the BMETS outperformed validated, simpler statistical models, justifying its use in this population. To better characterize a component of decisional uncertainty faced by providers, we next sought to identify the prevalence of “complicated” symptomatic bone metastases across a breadth of possible operational definitions. Our efforts identified up to 96 possible definitions of “complicated” bone metastases, present in up to 67.1% of patients in our database. Given that such “complicated” lesions may have been excluded from clinical trials in this setting, these data highlight the difficulty faced by providers when attempting to select appropriate RT regimens using inadequately defined selection criteria. Informed by these insights, we developed the BMETS Decision Support Platform (BMETS-DSP). This provider-facing, web-based tool was created to (1) collect relevant patient-specific data, (2) display an individualized predicted survival curve as per the BMETS model, and (3) provide case-specific, evidence-based recommendations for treatment of symptomatic bone metastases. We then conducted a pilot assessment of the clinical utility of the BMETS-DSP. In this preliminary assessment, the BMETS-DSP significantly improved physician accuracy in estimating survival and increased prognostic confidence, likelihood of sharing prognosis, and use of prognosis-appropriate RT regimens in the care of case patients. Collectively, this research provides early justification for the use of a machine-learning survival model and resultant decisions support platform to guide individualized selection of palliative RT regimens for symptomatic bone metastases. These data support a multi-institutional, randomized trial of the BMETS-DSP