Machine learning algorithms to predict breast cancer recurrence using structured and unstructured sources from electronic health records

Recurrence is a critical aspect of breast cancer (BC) that is inexorably tied to mortality. Reuse of healthcare data through Machine Learning (ML) algorithms offers great opportunities to improve the stratification of patients at risk of cancer recurrence. We hypothesized that combining features from structured and unstructured sources would provide better prediction results for 5-year cancer recurrence than either source alone. We collected and preprocessed clinical data from a cohort of BC patients, resulting in 823 valid subjects for analysis. We derived three sets of features: structured information, features from free text, and a combination of both. We evaluated the performance of five ML algorithms to predict 5-year cancer recurrence and selected the best-performing to test our hypothesis. The XGB (eXtreme Gradient Boosting) model yielded the best performance among the five evaluated algorithms, with precision = 0.900, recall = 0.907, F1-score = 0.897, and area under the receiver operating characteristic AUROC = 0.807. The best prediction results were achieved with the structured dataset, followed by the unstructured dataset, while the combined dataset achieved the poorest performance. ML algorithms for BC recurrence prediction are valuable tools to improve patient risk stratification, help with post-cancer monitoring, and plan more effective follow-up. Structured data provides the best results when fed to ML algorithms. However, an approach based on natural language processing offers comparable results while potentially requiring less mapping effort.European Union | Ref. 875406Fondo Europeo de Desarrollo Regional (FEDER)Xunta de Galici

Using XML and XSLT for flexible elicitation of mental-health risk knowledge

Current tools for assessing risks associated with mental-health problems require assessors to make high-level judgements based on clinical experience. This paper describes how new technologies can enhance qualitative research methods to identify lower-level cues underlying these judgements, which can be collected by people without a specialist mental-health background. Methods and evolving results: Content analysis of interviews with 46 multidisciplinary mental-health experts exposed the cues and their interrelationships, which were represented by a mind map using software that stores maps as XML. All 46 mind maps were integrated into a single XML knowledge structure and analysed by a Lisp program to generate quantitative information about the numbers of experts associated with each part of it. The knowledge was refined by the experts, using software developed in Flash to record their collective views within the XML itself. These views specified how the XML should be transformed by XSLT, a technology for rendering XML, which resulted in a validated hierarchical knowledge structure associating patient cues with risks. Conclusions: Changing knowledge elicitation requirements were accommodated by flexible transformations of XML data using XSLT, which also facilitated generation of multiple data-gathering tools suiting different assessment circumstances and levels of mental-health knowledge

Discerning the Ambiguous Role of Missense TTN Variants in Inherited Arrhythmogenic Syndromes

The titin gene (TTN) is associated with several diseases, including inherited arrhythmias. Most of these diagnoses are attributed to rare TTN variants encoding truncated forms, but missense variants represent a diagnostic challenge for clinical genetics. The proper interpretation of genetic data is critical for translation into the clinical setting. Notably, many TTN variants were classified before 2015, when the American College of Medical Genetics and Genomics (ACMG) published recommendations to accurately classify genetic variants. Our aim was to perform an exhaustive reanalysis of rare missense TTN variants that were classified before 2015, and that have ambiguous roles in inherited arrhythmogenic syndromes. Rare missense TTN variants classified before 2015 were updated following the ACMG recommendations and according to all the currently available data. Our cohort included 193 individuals definitively diagnosed with an inherited arrhythmogenic syndrome before 2015. Our analysis resulted in the reclassification of 36.8% of the missense variants from unknown to benign/likely benign. Of all the remaining variants, currently classified as of unknown significance, 38.3% showed a potential, but not confirmed, deleterious role. Most of these rare missense TTN variants with a suspected deleterious role were identified in patients diagnosed with hypertrophic cardiomyopathy. More than 35% of the rare missense TTN variants previously classified as ambiguous were reclassified as not deleterious, mainly because of improved population frequencies. Despite being inconclusive, almost 40% of the variants showed a potentially deleterious role in inherited arrhythmogenic syndromes. Our results highlight the importance of the periodical reclassification of rare missense TTN variants to improve genetic diagnoses and help increase the accuracy of personalized medicine

Feature engineering and a proposed decision-support system for systematic reviewers of medical evidence

Objectives: Evidence-based medicine depends on the timely synthesis of research findings. An important source of synthesized evidence resides in systematic reviews. However, a bottleneck in review production involves dual screening of citations with titles and abstracts to find eligible studies. For this research, we tested the effect of various kinds of textual information (features) on performance of a machine learning classifier. Based on our findings, we propose an automated system to reduce screeing burden, as well as offer quality assurance. Methods: We built a database of citations from 5 systematic reviews that varied with respect to domain, topic, and sponsor. Consensus judgments regarding eligibility were inferred from published reports. We extracted 5 feature sets from citations: alphabetic, alphanumeric +, indexing, features mapped to concepts in systematic reviews, and topic models. To simulate a two-person team, we divided the data into random halves. We optimized the parameters of a Bayesian classifier, then trained and tested models on alternate data halves. Overall, we conducted 50 independent tests. Results: All tests of summary performance (mean F3) surpassed the corresponding baseline, P<0.0001. The ranks for mean F3, precision, and classification error were statistically different across feature sets averaged over reviews; P-values for Friedman's test were .045, .002, and .002, respectively. Differences in ranks for mean recall were not statistically significant. Alphanumeric+ features were associated with best performance; mean reduction in screening burden for this feature type ranged from 88% to 98% for the second pass through citations and from 38% to 48% overall. Conclusions: A computer-assisted, decision support system based on our methods could substantially reduce the burden of screening citations for systematic review teams and solo reviewers. Additionally, such a system could deliver quality assurance both by confirming concordant decisions and by naming studies associated with discordant decisions for further consideration. © 2014 Bekhuis et al

Comparative efficacy of two primary care interventions to assist withdrawal from long term benzodiazepine use: A protocol for a clustered, randomized clinical trial

<p>Abstract</p> <p>Background</p> <p>Although benzodiazepines are effective, long-term use is not recommended because of potential adverse effects; the risks of tolerance and dependence; and an increased risk of hip fractures, motor vehicle accidents, and memory impairment. The estimated prevalence of long-term benzodiazepine use in the general population is about 2,2 to 2,6%, is higher in women and increases steadily with age. Interventions performed by General Practitioners may help patients to discontinue long-term benzodiazepine use. We have designed a trial to evaluate the effectiveness and safety of two brief general practitioner-provided interventions, based on gradual dose reduction, and will compare the effectiveness of these interventions with that of routine clinical practice.</p> <p>Methods/Design</p> <p>In a three-arm cluster randomized controlled trial, general practitioners will be randomly allocated to: a) a group in which the first patient visit will feature a structured interview, followed by visits every 2-3 weeks to the end of dose reduction; b) a group in which the first patient visit will feature a structured interview plus delivery of written instructions to self-reduce benzodiazepine dose, or c) routine care. Using a computerized pharmaceutical prescription database, 495 patients, aged 18-80 years, taking benzodiazepine for at least 6 months, will be recruited in primary care health districts of three regions of Spain (the Balearic Islands, Catalonia, and Valencia). The primary outcome will be benzodiazepine use at 12 months. The secondary outcomes will include measurements of anxiety and depression symptoms, benzodiazepine dependence, quality of sleep, and alcohol consumption.</p> <p>Discussion</p> <p>Although some interventions have been shown to be effective in reducing benzodiazepine consumption by long-term users, the clinical relevance of such interventions is limited by their complexity. This randomized trial will compare the effectiveness and safety of two complex stepped care interventions with that of routine care in a study with sufficient statistical power to detect clinically relevant differences.</p> <p>Trial Registration</p> <p>Current Controlled Trials: <a href="http://www.controlled-trials.com/ISRCTN13024375">ISRCTN13024375</a></p

Standards for Scalable Clinical Decision Support: Need, Current and Emerging Standards, Gaps, and Proposal for Progress

Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS