Summary An emphasis on overly broad notions of generalisability as it pertains to applications of machine learning in health care can overlook situations in which machine learning might provide clinical utility. We believe that this narrow focus on generalisability should be replaced with wider considerations for the ultimate goal of building machine learning systems that are useful at the bedside.

Objective Determine if deep learning detects sepsis earlier and more accurately than other models. To evaluate model performance using implementation-oriented metrics that simulate clinical practice. Materials and Methods We trained internally and temporally validated a deep learning model (multi-output Gaussian process and recurrent neural network [MGP–RNN]) to detect sepsis using encounters from adult hospitalized patients at a large tertiary academic center. Sepsis was defined as the presence of 2 or more systemic inflammatory response syndrome (SIRS) criteria, a blood culture order, and at least one element of end-organ failure. The training dataset included demographics, comorbidities, vital signs, medication administrations, and labs from October 1, 2014 to December 1, 2015, while the temporal validation dataset was from March 1, 2018 to August 31, 2018. Comparisons were made to 3 machine learning methods, random forest (RF), Cox regression (CR), and penalized logistic regression (PLR), and 3 clinical scores used to detect sepsis, SIRS, quick Sequential Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS). Traditional discrimination statistics such as the C-statistic as well as metrics aligned with operational implementation were assessed. Results The training set and internal validation included 42 979 encounters, while the temporal validation set included 39 786 encounters. The C-statistic for predicting sepsis within 4 h of onset was 0.88 for the MGP–RNN compared to 0.836 for RF, 0.849 for CR, 0.822 for PLR, 0.756 for SIRS, 0.619 for NEWS, and 0.481 for qSOFA. MGP–RNN detected sepsis a median of 5 h in advance. Temporal validation assessment continued to show the MGP–RNN outperform all 7 clinical risk score and machine learning comparisons. Conclusions We developed and validated a novel deep learning model to detect sepsis. Using our data elements and feature set, our modeling approach outperformed other machine learning methods and clinical scores.

Confocal Raman spectroscopy was implemented in a new label-free technique to quantify molecular diffusion coefficients within gels. A leading anti-HIV drug, tenofovir, was analyzed in a clinical microbicide gel. The gel was tested undiluted, and in 10 – 50%w/w dilutions with vaginal fluid simulant to capture the range of conditions likely occurring in vivo. The concentration distributions of tenofovir in gel over time and space were measured and input to a mathematical diffusion model to deduce diffusion coefficients. These were 3.16 ± 0.11 × 10-6 cm2/s in undiluted gel, and increased by 11 – 46% depending upon the extent of dilution. Results were interpreted with respect to traditional release rate measurements in devices such as Franz cells. This comparison highlighted an advantage of our assay in that it characterizes the diffusive barrier within the gel material itself; in contrast, release rate in the traditional assay is affected by external conditions, such as drug partitioning at the gel/liquid sink interface. This new assay is relevant to diffusion in polymeric hydrogels over pharmacologically relevant length scales, e.g. those characteristic of topical drug delivery. Resulting transport parameters are salient measures of drug delivery potential, and serve as inputs to computational models of drug delivery performance.

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