The measurement of urinary ifosfamide, isophosphoramide mustard, dechloroethyl ifosfamide and carboxyifosfamide using high-performance thin-layer chromatography with photographic densitometry (TLC-PD) is described. This technique was also used to demonstrate the large inter-individual variation in the ifosfamide metabolic profile of patients receiving the drug as single-agent therapy for non-small-cell lung cancer. In addition, oral administration was shown to result in higher levels of these metabolites in the urine. Fractionation of the ifosfamide dose over several days resulted in increasing levels of metabolites in the urine, consistent with auto-induction of ifosfamide metabolism.

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IntroductionEpileptic and psychogenic non-epileptic seizures (PNES) are common diagnostic problems encountered in hospital practice. This study explores the use of unsupervised machine learning in discovering themes in medical records of patients presenting with PNES. We hypothesised that themes generated by machine learning are comparable with the classification by human experts.MethodsThis is a retrospective analysis of the medical records in the emergency department of patients (age >18 years) with PNES who underwent inpatient video-electroencephalography monitoring from May 2009 to June 2014 and received a final diagnosis of PNES. Prior to machine learning of written text, we applied a standardised approach in natural language processing to create a document-term matrix (removal of numbers, stop-words and punctuations, transforming fonts to lower case). The words were separated into tokens and treated as if existing within a bag-of-words. A probability of each word existing within a topic (theme) was modelled on multivariate Dirichlet distribution (R Foundation, V.3.5.0). Next, we asked four experts to independently provide a clinical interpretation of the generated topics. When the majority of (≥3) experts agreed, it was regarded as highly congruent. Interactive data are available on the web at (https://gntem2.github.io/PNES/%23topic=1&lambda=0.6&term=).ResultsThere were 39 patients (74.4% women, median age 35 years with range 20–82). A total of 121 documents were converted to text files for text mining. There were 15 generated topics with 12/15 topics rated as highly congruent. The main themes were about descriptors of seizures and medication use.ConclusionsThe findings from machine learning on PNES-related documentation provides evidence for the feasibility of applying machine-learning methodology to analyse large volumes of medical records. The topics generated by machine learning were congruent with interpretations by clinicians indicating this method can be used for screening of medical conditions among large volumes of medical records.

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