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

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

SOMATOSENSORY EVOKED POTENTIALS OF INFERIOR ALVEOLAR NERVE.

Purpose: The use of inferior alveolar nerve somatosensory evoked potentials may represent an objective means of evaluating sensory nerve function in the maxillofacial region. The aim of this work was to confirm the existence of a standard sequence of prominent events in the trigeminal somatosensory evoked potentials (TSEPs) of inferior alveolar nerve (IAN) waveform, examine those components and their normal variability by statistical analysis, and discuss TSEPs' nervous origin and some patterns of TSEPs' abnormalities due to dysfunctional nerves. Materials and Methods: TSEPs were obtained following electrical stimulation (square wave pulses 0.2 millisecond [ms] in duration, 4 to 6.5 mA, 0.7/second repetition rate, 200 averages) of the gum at the mental foramen level via intraoral surface electrodes and recorded from the contralateral central scalp sites. Results: We successfully recognized steady waveforms of sufficient quality and consistently recorded a "W"-shaped response: latency onset and peak of the initial deflection of positive polarity were approximately 12 ms and 20 ms, respectively. Negative and positive deflections followed with respective peak latencies at around 26 ms and 36 ms. One side of the lower lip can be compared with the contralateral side and patients may serve as their own control in cases of unilateral nerve injury. The anaesthetic block showed the total abolition of responses. Reproducible TSEP waveform was only obtained during nerve stimulation and not during masseter muscle stimulation. Conclusions: TSEPs, obtained with the present technique, may represent an objective, low-invasive, and reliable way of testing sensory nerve function in the maxillofacial region. © 2006 American Association of Oral and Maxillofacial Surgeons

Generating valid grammar-based test inputs by means of genetic programming and annotated grammars

Automated generation of system level tests for grammar based systems requires the generation of complex and highly structured inputs, which must typically satisfy some formal grammar. In our previous work, we showed that genetic programming combined with probabilities learned from corpora gives significantly better results over the baseline (random) strategy. In this work, we extend our previous work by introducing grammar annotations as an alternative to learned probabilities, to be used when finding and preparing the corpus required for learning is not affordable. Experimental results carried out on six grammar based systems of varying levels of complexity show that grammar annotations produce a higher number of valid sentences and achieve similar levels of coverage and fault detection as learned probabilities