Deep learning for the prediction of clinical outcomes in internet-delivered CBT for depression and anxiety.

In treating depression and anxiety, just over half of all clients respond. Monitoring and obtaining early client feedback can allow for rapidly adapted treatment delivery and improve outcomes. This study seeks to develop a state-of-the-art deep-learning framework for predicting clinical outcomes in internet-delivered Cognitive Behavioural Therapy (iCBT) by leveraging large-scale, high-dimensional time-series data of client-reported mental health symptoms and platform interaction data. We use de-identified data from 45,876 clients on SilverCloud Health, a digital platform for the psychological treatment of depression and anxiety. We train deep recurrent neural network (RNN) models to predict whether a client will show reliable improvement by the end of treatment using clinical measures, interaction data with the iCBT program, or both. Outcomes are based on total improvement in symptoms of depression (Patient Health Questionnaire-9, PHQ-9) and anxiety (Generalized Anxiety Disorder-7, GAD-7), as reported within the iCBT program. Using internal and external datasets, we compare the proposed models against several benchmarks and rigorously evaluate them according to their predictive accuracy, sensitivity, specificity and AUROC over treatment. Our proposed RNN models consistently predict reliable improvement in PHQ-9 and GAD-7, using past clinical measures alone, with above 87% accuracy and 0.89 AUROC after three or more review periods, outperforming all benchmark models. Additional evaluations demonstrate the robustness of the achieved models across (i) different health services; (ii) geographic locations; (iii) iCBT programs, and (iv) client severity subgroups. Results demonstrate the robust performance of dynamic prediction models that can yield clinically helpful prognostic information ready for implementation within iCBT systems to support timely decision-making and treatment adjustments by iCBT clinical supporters towards improved client outcomes.

Assessment of National External Quality Assurance Programme of Pakistan as a tool for improving quality of lab results among participating laboratories.

OBJECTIVE: To assess the impact of the National External Quality Assessment Programme of Pakistan NEQAPP in improving the quality of laboratory results among the participating laboratories. METHODS: The cross-sectional observational study was conducted from July to December 2020 at the Department of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology, Rawalpindi, Pakistan, in association with the National Quality Assurance Programme of Pakistan. A survey questionnaire was developed and sent to the participating laboratories via email. Frequencies of their responses were calculated and data was analysed using SPSS 21. RESULTS: Of the 150 laboratories approached, 145(96.6%) responded. Among them, 140 (96.6%) laboratories were satisfied by the information provided on the programme's portal, 123(84.8%s) were pleased with the responsiveness of the programme manager, 140(96.6%) reported quality of services had improved after participation in the programme, 129(89%) indicated that the clinician's confidence had enhanced, and 122(84%) said the participation in the programme had improved the credibility of their respective of laboratories. CONCLUSIONS: The National External Quality Assessment Programme of Pakistan was found to have significantly contributed in improving the quality of laboratory results among the participating laboratories.

Artificial intelligence in healthcare: transforming the practice of medicine.

Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective, reliable and safe AI systems, and discuss the possible future direction of AI augmented healthcare systems.

Novel Technology to Capture Objective Data from Patients' Recovery from Laparoscopic Endometriosis Surgery.

STUDY OBJECTIVE: To assess the feasibility of a noncontact radio sensor as an objective measurement tool to study postoperative recovery from endometriosis surgery. DESIGN: Prospective cohort pilot study. SETTING: Center for minimally invasive gynecologic surgery at an academically affiliated community hospital in conjunction with in-home monitoring. PATIENTS: Patients aged above 18 years who sleep independently and were scheduled to have laparoscopy for the diagnosis and treatment of suspected endometriosis. INTERVENTIONS: A wireless, noncontact sensor, Emerald, was installed in the subjects' home and used to capture physiologic signals without body contact. The device captured objective data about the patients' movement and sleep in their home for 5 weeks before surgery and approximately 5 weeks postoperatively. The subjects were concurrently asked to complete a daily pain assessment using a numeric rating scale and a free text survey about their daily symptoms. MEASUREMENTS AND MAIN RESULTS: Three women aged 23 years to 39 years and with mild to moderate endometriosis participated in the study. Emerald-derived sleep and wake times were contextualized and corroborated by select participant comments from retrospective surveys. In addition, self-reported pain levels and 1 sleep variable, sleep onset to deep sleep time, showed a significant (p <.01), positive correlation with next-day-pain scores in all 3 subjects: r = 0.45, 0.50, and 0.55. In other words, the longer it took the subject to go from sleep onset to deep sleep, the higher their pain score the following day. CONCLUSION: A patient's experience with pain is challenging to meaningfully quantify. This study highlights Emerald's unique ability to capture objective data in both preoperative functioning and postoperative recovery in an endometriosis population. The utility of this uniquely objective data for the clinician-patient relationship is just beginning to be explored.

Radio Signal Sensing and Signal Processing to Monitor Behavioral Symptoms in Dementia: A Case Study.

OBJECTIVES: Alzheimer's Disease (AD)-related behavioral symptoms (i.e. agitation and/or pacing) develop in nearly 90% of AD patients. In this N = 1 study, we provide proof-of-concept of detecting changes in movement patterns that may reflect underlying behavioral symptoms using a highly novel radio sensor and identifying environmental triggers. METHODS: The Emerald device is a Wi-Fi-like box without on-body sensors, which emits and processes radio-waves to infer patient movement, spatial location and activity. It was installed for 70 days in the room of patient 'E', exhibiting agitated behaviors. RESULTS: Daily motion episode aggregation revealed motor activity fluctuation throughout the data collection period which was associated with potential socio-environmental triggers. We did not detect any adverse events attributable to the use of the device. CONCLUSION: This N-of-1 study suggests the Emerald device is feasible to use and can potentially yield actionable data regarding behavioral symptom management. No active or potential device risks were encountered.