Background: Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). Objectives: We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID. Sources: References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019. Content: We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n ¼ 24, 40%), ID consultation (n ¼ 15, 25%), medical or surgical wards (n ¼ 13, 20%), emergency department (n ¼ 4, 7%), primary care (n ¼ 3, 5%) and antimicrobial stewardship (n ¼ 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low-and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%). Implications: Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

We developed an integrated chip for real-time amplification and detection of nucleic acid using pH-sensing complementary metal-oxide semiconductor (CMOS) technology. Here we show an amplification-coupled detection method for directly measuring released hydrogen ions during nucleotide incorporation rather than relying on indirect measurements such as fluorescent dyes. This is a label-free, non-optical, real-time method for detecting and quantifying target sequences by monitoring pH signatures of native amplification chemistries. The chip has ion-sensitive field effect transistor (ISFET) sensors, temperature sensors, resistive heating, signal processing and control circuitry all integrated to create a full system-on-chip platform. We evaluated the platform using two amplification strategies: PCR and isothermal amplification. Using this platform, we genotyped and discriminated unique single-nucleotide polymorphism (SNP) variants of the cytochrome P450 family from crude human saliva. We anticipate this semiconductor technology will enable the creation of devices for cost-effective, portable and scalable real-time nucleic acid analysis.

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Greater consideration of the factors that drive non-expert decision making must be considered when designing CDSS interventions. Future work must aim to expand CDSS beyond simply selecting appropriate antimicrobials with clear and systematic reporting frameworks for CDSS interventions developed to address current gaps identified in the reporting of evidence.

Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with Type 1 diabetes mellitus (T1DM) such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this work, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (RMSE = 9.38±0.71 [mg/dL] over a 30-minute horizon, RMSE = 18.87±2.25 [mg/dL] over a 60minute horizon) and real patient cases (RMSE = 21.07±2.35 [mg/dL] for 30-minute, RMSE = 33.27±4.79% for 60-minute).In addition, the model provides competitive performance in providing effective prediction horizon (P H ef f ) with minimal time lag both in a simulated patient dataset (P H ef f = 29.0±0.7 for 30-min and P H ef f = 49.8±2.9 for 60-min) and in a real patient dataset (P H ef f = 19.3±3.1 for 30-min and P H ef f = 29.3±9.4 for 60-min). This approach is evaluated on a dataset of 10 simulated cases generated from the UVa/Padova simulator and a clinical dataset of 10 real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6ms on a phone compared to an execution time of 780ms on a laptop.