Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

"Even if the test result is negative, they should be able to tell us what is wrong with us": a qualitative study of patient expectations of rapid diagnostic tests for malaria.

BACKGROUND: The debate on rapid diagnostic tests (RDTs) for malaria has begun to shift from whether RDTs should be used, to how and under what circumstances their use can be optimized. This has increased the need for a better understanding of the complexities surrounding the role of RDTs in appropriate treatment of fever. Studies have focused on clinician practices, but few have sought to understand patient perspectives, beyond notions of acceptability. METHODS: This qualitative study aimed to explore patient and caregiver perceptions and experiences of RDTs following a trial to assess the introduction of the tests into routine clinical care at four health facilities in one district in Ghana. Six focus group discussions and one in-depth interview were carried out with those who had received an RDT with a negative test result. RESULTS: Patients had high expectations of RDTs. They welcomed the tests as aiding clinical diagnoses and as tools that could communicate their problem better than they could, verbally. However, respondents also believed the tests could identify any cause of illness, beyond malaria. Experiences of patients suggested that RDTs were adopted into an existing system where patients are both physically and intellectually removed from diagnostic processes and where clinicians retain authority that supersedes tests and their results. In this situation, patients did not feel able to articulate a demand for test-driven diagnosis. CONCLUSIONS: Improvements in communication between the health worker and patient, particularly to explain the capabilities of the test and management of RDT negative cases, may both manage patient expectations and promote patient demand for test-driven diagnoses

Expectancies, working alliance, and outcome in transdiagnostic and single diagnosis treatment for anxiety disorders: an investigation of mediation

Patients’ outcome expectancies and the working alliance are two psychotherapy process variables that researchers have found to be associated with treatment outcome, irrespective of treatment approach and problem area. Despite this, little is known about the mechanisms accounting for this association, and whether contextual factors (e.g., psychotherapy type) impact the strength of these relationships. The primary aim of this study was to examine whether patient-rated working alliance quality mediates the relationship between outcome expectancies and pre- to post-treatment change in anxiety symptoms using data from a recent randomized clinical trial comparing a transdiagnostic treatment (the Unified Protocol [UP]; Barlow et al., Unified protocol for transdiagnostic treatment of emotional disorders: Client workbook, Oxford University Press, New York, 2011a; Barlow et al., Unified protocol for transdiagnostic treatment of emotional disorders: Patient workbook. New York: Oxford University Press, 2017b) to single diagnosis protocols (SDPs) for patients with a principal heterogeneous anxiety disorder (n = 179). The second aim was to explore whether cognitive-behavioral treatment condition (UP vs. SDP) moderated this indirect relationship. Results from mediation and moderated mediation models indicated that, when collapsing across the two treatment conditions, the relationship between expectancies and outcome was partially mediated by the working alliance [B = 0.037, SE = 0.05, 95% CI (.005, 0.096)]. Interestingly, within-condition analyses showed that this conditional indirect effect was only present for SDP patients, whereas in the UP condition, working alliance did not account for the association between expectancies and outcome. These findings suggest that outcome expectancies and working alliance quality may interact to influence treatment outcomes, and that the nature and strength of the relationships among these constructs may differ as a function of the specific cognitive-behavioral treatment approach utilized.This study was funded by grant R01 MH090053 from the National Institutes of Health. (R01 MH090053 - National Institutes of Health)First author draf

Beyond Covariation: Cues to Causal Structure

Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning

Crohn's disease activity index and Vienna classification - Is it worthwhile to calculate before surgery?

Background: Crohn's disease (CD) patients with increased disease activity may reveal an increased risk for perioperative complications. The `Crohn's disease activity index' (CDAI) and the `Vienna classification' (VC) were developed for standardized disease activity estimations. The significance of these scores to predict extent, type and early outcome of surgery in CD patients was analyzed. Methods: In 179 surgically treated CD patients, the CDAI and VC were assessed from a prospective database. Relations of the scores with CD risk factors, type, number, location and complications of surgery were analyzed. Results: VC behavior and location subtypes were associated with distinct types of surgery (i.e. `strictureplasty' in `stricturing disease', `colon surgery' in `colon involvement'), but not with surgery type and extent or outcome. Surgery extent (i.e. with 5 vs. 3 `surgical sites' 425 +/- 25 vs. 223.3 +/- 25) and complications (357.1 +/- 36.9 (with) vs. 244.4 +/- 13 (without)) were associated with elevated CDAI levels; however, nicotine abuse remained the only significant risk factor for perioperative complications after multiple logistic regression. Conclusion: The significance of VC or CDAI for predicting the extent of surgery or complications is limited. None of the tested variables except preoperative nicotine abuse influenced the likelihood for perioperative complications. Copyright (c) 2006 S. Karger AG, Base