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Background and Objective: Code assignment is of paramount importance in many levels in modern hospitals, from ensuring accurate billing process to creating a valid record of patient care history. However, the coding process is tedious and subjective, and it requires medical coders with extensive training. This study aims to evaluate the performance of deep-learning-based systems to automatically map clinical notes to ICD-9 medical codes. Methods: The evaluations of this research are focused on end-to-end learning methods without manually defined rules. Traditional machine learning algorithms, as well as state-of-the-art deep learning methods such as Recurrent Neural Networks and Convolution Neural Networks, were applied to the Medical Information Mart for Intensive Care (MIMIC-III) dataset. An extensive number of experiments was applied to different settings of the tested algorithm. Results: Findings showed that the deep learning-based methods outperformed other conventional machine learning methods. From our assessment, the best models could predict the top 10 ICD-9 codes with 0.6957 F 1 and 0.8967 accuracy and could estimate the top 10 ICD-9 categories with 0.7233 F 1 and 0.8588 accuracy. Our implementation also outperformed existing work under certain evaluation metrics. Conclusion: A set of standard metrics was utilized in assessing the performance of ICD-9 code assignment on MIMIC-III dataset. All the developed evaluation tools and resources are available online, which can be used as a baseline for further research.

Background: Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living.Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications. Methods:The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors.Results: A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls.Discussion: This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected.

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The early postoperative period is a crucial stage in a patient's recovery as they are susceptible to a range of complications, with detection and management the key to avoiding long term consequences.Wearable devices are an innovative way of monitoring patient's post-intervention and may translate into improved patient outcomes, and reduced strain on healthcare resources, as they may facilitate safer and earlier discharge from the hospital setting. Several recent studies have investigated the use of wearable devices in postoperative monitoring. This review outlines the current literature including the range of wearable devices used for postoperative monitoring, the variety of surgeries investigated, and the outcomes assessed.A search of five electronic databases was performed. Data on the range of wearable devices, outcomes and surgeries investigated were extracted and synoptically analysed. Twenty-four articles were retrieved. Data on several different types of surgery were available and discussed. Most studies used wrist-mounted wearable devices and accelerometers or pedometers to assess physical activity metrics, including step counts and physical activity intensity (PAI), as markers of recovery. Wearable devices can provide objective data capture in the early postoperative phase to remotely monitor patients using various metrics including temperature, cardiac monitoring and physical activity. The majority of current research is focussed on wrist-mounted accelerometers and pedometers used to assess physical activity as a marker of postoperative function. Further research is required to demonstrate improved safety and cost-effectiveness of this technology.