18 research outputs found
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
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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
Production of enantiomerically pure D-Phenylglycine using Pseudomonas aeruginosa 10145 as biocatalyst
Different bacterial strains were screened to detect nitrilase and/or nitrile hidratase/amidase activities towards benzonitrile, to be used as biocatalyst to produce enantiomerically pure non-proteinogenic amino acids using amino nitriles as starting material. The best biocatalyst found was Pseudomonas aeruginosa 10145, which showed high enzyme activities. Whole cells were used as catalyst for the transformation of 2-phenyl-2-amino-acetonitrile for the corresponding D-phenylglycine. The percentage conversion was followed by chiral HPLC. After 1 hour reaction 18% of 2-phenyl-2-amino-acetonitrile was converted into D-phenylglycine with an enantiomeric excess of over 95%. When an inducer was added to the media, an increase in nitrile hydrolyzing activities was detected, hence leading to total conversion of (R)-2-phenyl-2-amino-acetonitrile to the corresponding amino acid in 30 min reaction. The isolated yield of the target product was 50% and its characterization was performed by polarimetry, chiral HPLC, IR-FT spectroscopy and GC-MS