Classification of Failures in the Perception of Conversational Agents (CAs) and their Implications on Patient Safety

The use of Conversational agents (CAs) in healthcare is an emerging field. These CAs seem to be effective in accomplishing administrative tasks, e.g. providing locations of care facilities and scheduling appointments. Modern CAs use machine learning (ML) to recognize, understand and generate a response. Given the criticality of many healthcare settings, ML and other component errors may result in CA failures and may cause adverse effects on patients. Therefore, in-depth assurance is required before the deployment of ML in critical clinical applications, e.g. management of medication dose or medical diagnosis. CA safety issues could arise due to diverse causes, e.g. related to user interactions, environmental factors and ML errors. In this paper, we classify failures of perception (recognition and understanding) of CAs and their sources. We also present a case study of a CA used for calculating insulin dose for gestational diabetes mellitus (GDM) patients. We then correlate identified perception failures of CAs to potential scenarios that might compromise patient safety

Comparison of existing aneurysm models and their path forward

The two most important aneurysm types are cerebral aneurysms (CA) and abdominal aortic aneurysms (AAA), accounting together for over 80\% of all fatal aneurysm incidences. To minimise aneurysm related deaths, clinicians require various tools to accurately estimate its rupture risk. For both aneurysm types, the current state-of-the-art tools to evaluate rupture risk are identified and evaluated in terms of clinical applicability. We perform a comprehensive literature review, using the Web of Science database. Identified records (3127) are clustered by modelling approach and aneurysm location in a meta-analysis to quantify scientific relevance and to extract modelling patterns and further assessed according to PRISMA guidelines (179 full text screens). Beside general differences and similarities of CA and AAA, we identify and systematically evaluate four major modelling approaches on aneurysm rupture risk: finite element analysis and computational fluid dynamics as deterministic approaches and machine learning and assessment-tools and dimensionless parameters as stochastic approaches. The latter score highest in the evaluation for their potential as clinical applications for rupture prediction, due to readiness level and user friendliness. Deterministic approaches are less likely to be applied in a clinical environment because of their high model complexity. Because deterministic approaches consider underlying mechanism for aneurysm rupture, they have improved capability to account for unusual patient-specific characteristics, compared to stochastic approaches. We show that an increased interdisciplinary exchange between specialists can boost comprehension of this disease to design tools for a clinical environment. By combining deterministic and stochastic models, advantages of both approaches can improve accessibility for clinicians and prediction quality for rupture risk.Comment: 46 pages, 5 figure

Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market

In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic financial loss to the investors. This paper introduces the application of a recently introduced machine learning model - the Transformer model, to predict the future price of stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh. The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task at DSE. Recently the introduction of time2vec encoding to represent the time series features has made it possible to employ the transformer model for the stock price prediction. This paper concentrates on the application of transformer-based model to predict the price movement of eight specific stocks listed in DSE based on their historical daily and weekly data. Our experiments demonstrate promising results and acceptable root mean squared error on most of the stocks.Comment: 16 Pages, 14 Figures (including some containing subfigures

Robust Intent Classification using Bayesian LSTM for Clinical Conversational Agents (CAs)

Conversational Agents (CAs) are software programs that replicate hu-man conversations using machine learning (ML) and natural language processing (NLP). CAs are currently being utilised for diverse clinical applications such as symptom checking, health monitoring, medical triage and diagnosis. Intent clas-sification (IC) is an essential task of understanding user utterance in CAs which makes use of modern deep learning (DL) methods. Because of the inherent model uncertainty associated with those methods, accuracy alone cannot be relied upon in clinical applications where certain errors may compromise patient safety. In this work, we employ Bayesian Long Short-Term Memory Networks (LSTMs) to calculate model uncertainty for IC, with a specific emphasis on symptom checker CAs. This method provides a certainty measure with IC prediction that can be utilised in assuring safe response from CAs. We evaluated our method on in-distribution (ID) and out-of-distribution (OOD) data and found mean uncer-tainty to be much higher for OOD data. These findings suggest that our method is robust to OOD utterances and can detect non-understanding errors in CAs

Anaemia characteristic in end stage renal disease patients receiving haemodialysis at King Salman armed forced hospital in Tabuk, Saudi Arabia

BackgroundChronic kidney disease (CKD) is a disease associated with high rate of morbidity and mortality mainly due to cardiovascular disease. Anaemia is the most common haematological abnormality in end stage renal disease.AimsThe current Study aimed to determine the laboratory characteristic and management of anaemia among haemodialysis patients.Methods A cross sectional study conducted among 112 adult patients with the diagnosis of end stage renal disease (ESRD) on haemodialysis at King Salman Armed Forced Hospital in Tabuk, Saudi Arabia, data were collected by a pre-tested data collection sheet.Results There were 112 patients with a mean age of 43 years. The mean haemoglobin value was 10.5g/dL, which was lower than the target haemoglobin range recommended by Kidney Disease Outcomes Quality Initiative (KDOQI). Twenty- eight patients (25 per cent) had haemoglobin values between 11.0 and 12.0g/dL. Only seven patients (6.3 per cent) exceeded the recommended range (>12g/dL) and seventy- seven (68.7 per cent) had less than recommended range. The majority of patients had been receiving haemodialysis for two or more years. The most common primary cause of end stage renal failure was diabetic nephropathy. Hypertension was the most common co-morbidity, followed by diabetes, and ischemic heart disease.ConclusionPatients with end stage renal disease at a high risk for anaemia which should be investigated for correctable causes such as Iron-deficiency before initiating erythropoietin replacement therapy

Relationship between the use of drugs and changes in body weight among patients: A systematic review and meta-analysis

Purpose: To investigate the impact of drugs on the body weight of patients.Methods: All the randomized controlled trials that evaluated the impact of medications on the body weight of patients were searched in various databases. Studies quantifying the impact of drugs on body weight when compared to placebo or any other treatment were considered for this review. Moreover, the quantitative synthesis of evidence was also performed by generating the forest plot.Results: A total of 20 studies involving 18,547 participants were included in the current review. Weight gains ranging from 0.5 to 2.6 kg were associated with the use of pioglitazone, espindolol, brexpiprazole, glimepiride and ezogabine while weight loss ranging from 1.1 to 12 kg was linked with the use of betahistine, naltrexone, bupropion, liraglutide, phentermine, topiramate, orlistat, zonisamide, duloxetine, semaglutide, metformin and linagliptin. The quantitative synthesis suggested that drugs can significantly reduce body weight by -0.53 kg (CI 95 % -1.01, -0.04, p < 0.04) when compared to standard treatment.Conclusion: The findings of this review suggest substantial association of drugs and weight change during pharmacotherapy. Pioglitzone, brexpiprazole, espindolol, ezogabine and glimepiride cause weight gain while naltrexone, bupropion, betahistine, topiramate, phentermine, zonisamide, semaglutide, linagliptin, liraglutide, orlistat, duloxetine and metformin were associated with weight loss. Drug-induced changes in body weight might cause serious consequences and should be addressed before initiating treatment

Focally administered succinate improves cerebral metabolism in traumatic brain injury patients with mitochondrial dysfunction.

Following traumatic brain injury (TBI), raised cerebral lactate/pyruvate ratio (LPR) reflects impaired energy metabolism. Raised LPR correlates with poor outcome and mortality following TBI. We prospectively recruited patients with TBI requiring neurocritical care and multimodal monitoring, and utilised a tiered management protocol targeting LPR. We identified patients with persistent raised LPR despite adequate cerebral glucose and oxygen provision, which we clinically classified as cerebral 'mitochondrial dysfunction' (MD). In patients with TBI and MD, we administered disodium 2,3-13C2 succinate (12 mmol/L) by retrodialysis into the monitored region of the brain. We recovered 13C-labelled metabolites by microdialysis and utilised nuclear magnetic resonance spectroscopy (NMR) for identification and quantification.Of 33 patients with complete monitoring, 73% had MD at some point during monitoring. In 5 patients with multimodality-defined MD, succinate administration resulted in reduced LPR(-12%) and raised brain glucose(+17%). NMR of microdialysates demonstrated that the exogenous 13C-labelled succinate was metabolised intracellularly via the tricarboxylic acid cycle. By targeting LPR using a tiered clinical algorithm incorporating intracranial pressure, brain tissue oxygenation and microdialysis parameters, we identified MD in TBI patients requiring neurointensive care. In these, focal succinate administration improved energy metabolism, evidenced by reduction in LPR. Succinate merits further investigation for TBI therapy.The authors disclose receipt of the following financial support for the research, authorship, and/or publication of this article: Medical Research Council (Grant no.G1002277 ID98489) and National Institute for Health Research Biomedical Research Centre, Cambridge (Neuroscience Theme; Brain Injury and Repair Theme). Authors’ support: NMG–National Institute for Health Research; AA–Academy of Medical Sciences Newton Fellowship; MGS–National Institute for Health Research Biomedical Research Centre, Cambridge; IJ–Medical Research Council (Grant no.G1002277 ID 98489) and National Institute for Health Research Biomedical Research Centre, Cambridge; DKM–National Institute for Health Research Senior Investigator Awards; MJK–Cambridge Australia Oliphant Scholarship in partnership with the Cambridge Trust; PJH–National Institute for Health Research (Professorship, Biomedical Research Centre, Brain Injury MedTech Co-operative, Senior Investigator Award and the Royal College of Surgeons of England; KLHC–National Institute for Health Research Biomedical Research Centre, Cambridge (Neuroscience Theme; Brain Injury and Repair Theme); EPT–Swedish Brain Foundation (Hjärnfonden), Swedish Medical Society (SLS) and Swedish Society for Medical Research (SSMF); AH–Medical Research Council/Royal College of Surgeons of England Clinical Research Training Fellowship (Grant no.G0802251), the NIHR Biomedical Research Centre and the NIHR Brain Injury MedTech Co-operative

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Mortality and pulmonary complications in patients undergoing surgery with perioperative sars-cov-2 infection: An international cohort study

Background The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (740%) had emergency surgery and 280 (248%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (261%) patients. 30-day mortality was 238% (268 of 1128). Pulmonary complications occurred in 577 (512%) of 1128 patients; 30-day mortality in these patients was 380% (219 of 577), accounting for 817% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 175 [95% CI 128-240], p&lt;00001), age 70 years or older versus younger than 70 years (230 [165-322], p&lt;00001), American Society of Anesthesiologists grades 3-5 versus grades 1-2 (235 [157-353], p&lt;00001), malignant versus benign or obstetric diagnosis (155 [101-239], p=0046), emergency versus elective surgery (167 [106-263], p=0026), and major versus minor surgery (152 [101-231], p=0047). Interpretation Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research