A flexible framework for visualizing and exploring patient misdiagnosis over time.

Diagnosis is a complex and ambiguous process and yet, it is the critical hinge point for all subsequent clinical reasoning and decision-making. Tracking the quality of the patient diagnostic process has the potential to provide valuable insights in improving the diagnostic accuracy and to reduce downstream errors but needs to be informative, timely, and efficient at scale. However, due to the rate at which healthcare data are captured on a daily basis, manually reviewing the diagnostic history of each patient would be a severely taxing process without efficient data reduction and representation. Application of data visualization and visual analytics to healthcare data is one promising approach for addressing these challenges. This paper presents a novel flexible visualization and analysis framework for exploring the patient diagnostic process over time (i.e., patient diagnosis paths). Our framework allows users to select a specific set of patients, events and/or conditions, filter data based on different attributes, and view further details on the selected patient cohort while providing an interactive view of the resulting patient diagnosis paths. A practical demonstration of our system is presented with a case study exploring infection-based patient diagnosis paths.

Cuproptosis-related lncRNAs are correlated with tumour metabolism and immune microenvironment and predict prognosis in pancreatic cancer patients.

Cuproptosis is a novel cell death pathway, and the regulatory mechanism in pancreatic cancer (PC) is unclear. The authors aimed to figure out whether cuproptosis-related lncRNAs (CRLs) could predict prognosis in PC and the underlying mechanism. First, the prognostic model based on seven CRLs screened by the least absolute shrinkage and selection operator Cox analysis was constructed. Following this, the risk score was calculated for pancreatic cancer patients and divided patients into high and low-risk groups. In our prognostic model, PC patients with higher risk scores had poorer outcomes. Based on several prognostic features, a predictive nomogram was established. Furthermore, the functional enrichment analysis of differentially expressed genes between risk groups was performed, indicating that endocrine and metabolic pathways were potential regulatory pathways between risk groups. TP53, KRAS, CDKN2A, and SMAD4 were dominant mutated genes in the high-risk group and tumour mutational burden was positively correlated with the risk score. Finally, the tumour immune landscape indicated patients in the high-risk group were more immunosuppressive than that in the low-risk group, with lower infiltration of CD8+ T cells and higher M2 macrophages. Above all, CRLs can be applied to predict PC prognosis, which is closely correlated with the tumour metabolism and immune microenvironment.

Design and application of a novel telemedicine system jointly driven by multinetwork integration and remote control: Practical experience from PLAGH, China.

In China, several problems were common in the telemedicine systems, such as the poor network stability and difficult interconnection. A new telemedicine system jointly driven by multinetwork integration and remote control has been designed to address these problems. A multilink aggregation algorithm and an overlay network for telemedicine system (ONTMS) were developed to improve network stability, and a non-intervention remote control method was designed for Internet of Things (IoT) devices/systems. The authors monitored the network parameters, and distributed the questionnaire to participants, for evaluating the telemedicine system and services. Under a detection bandwidth of 8 Mbps, the aggregation parameters of Unicom 4G, Telecom 4G, and China Mobile 4G were optimal, with an uplink bandwidth, delay, and packet loss ratio (PLR) of 7.93 Mbps, 58.80 ms, and 0.06%, respectively. These parameters were significantly superior to those of China Mobile 4G, the best single network (p < 0.001). Through the ONTMS, the mean round-trip transporting delay from Beijing to Sanya was 76 ms, and the PLR was 0 at vast majority of time. A total of 1988 participants, including 1920 patients and 68 doctors, completed the questionnaires. More than 97% of participants felt that the audio and video transmission and remote control were fluent and convenient. 96% of patients rated the telemedicine services with scores of 4 or 5. This system has shown robust network property and excellent interaction ability, and satisfied the needs of patients and doctors.

Coping Strategies Used by Patients After Diagnosis Disclosure in the Transition to Palliative Care: A Cross-Sectional Study.

Objectives: The objectives of this study were to identify the coping strategies used by cancer and non-cancer patients with palliative needs, and to verify if there were differences in the coping strategies adopted between sociodemographic groups. Methods: This is a cross-sectional study carried out from September to November 2019, at Maputo Central Hospital, in the units of Medicine, Surgery, Orthopedics, Gynecology and Obstetrics. Eligible patients (n = 94) were included in the study and answered a self-completion scale adapted from the Coping Strategies Inventory by Folkman and Lazarus together with a sociodemographic questionnaire. Results: Our study demonstrates that the most used coping strategies were Social Support, followed by Planful Problem Solving, Escape-Avoidance, and Positive Reappraisal strategies. In addition, significant differences were observed between religious beliefs, with Christians resorting more to coping strategies related to Social Support, Accepting Responsibility and Escape-Avoidance than Evangelicals, and between different levels of education, with greater resort to Social Support, Accepting Responsibility, Planful Problem Solving, and Positive Reappraisal in patients with high education. Conclusions: The results indicate that most of the respondents in this study used more adaptive coping strategies, such as Social Support and Positive Reappraisal, and less avoidant strategies, such as Distancing and Confrontation. There is a need to reinforce positive strategies from health professionals to increase satisfaction, autonomy, and promote patient's quality of life.

Perspectives before incremental trans-disciplinary cross-validation of clinical self-evaluation tools and functional MRI in psychiatry: 10 years later.

Translational validity (or trans-disciplinary validity) is defined as one possible approach to achieving incremental validity by combining simultaneous clinical state-dependent measures and functional MRI data acquisition. It is designed under the assumption that the simultaneous administration of the two methods may produce a dataset with enhanced synchronization and concordance. Translational validation aims at "bridging" the explanatory gap by implementing validated psychometric tools clinically in the experimental settings of fMRI and then translating them back to clinical utility. Our studies may have identified common diagnostic task-specific denominators in terms of activations and network modulation. However, those common denominators need further investigation to determine whether they signify disease or syndrome-specific features (signatures), which, at the end of the day, raises one more question about the poverty of current conventional psychiatric classification criteria. We propose herewith a novel algorithm for translational validation based on our explorative findings. The algorithm itself includes pre-selection of a test based on its psychometric characteristics, adaptation to the functional MRI paradigm, exploration of the underpinning whole brain neural correlates in healthy controls as compared to a patient population with certain diagnoses, and finally, investigation of the differences between two or more diagnostic classes.

Moving toward AI-assisted decision-making: Observation on clinicians' management of multimedia patient information in synchronous and asynchronous telehealth contexts.

BACKGROUND: Artificial intelligence (AI) intends to support clinicians' patient diagnosis decisions by processing and identifying insights from multimedia patient information. OBJECTIVE: We explored clinicians' current decision-making patterns using multimedia patient information (MPI) provided by AI algorithms and identified areas where AI can support clinicians in diagnostic decision-making. DESIGN: We recruited 87 advanced practice nursing (APN) students who had experience making diagnostic decisions using AI algorithms under various care contexts, including telehealth and other healthcare modalities. The participants described their diagnostic decision-making experiences using videos, images, and audio-based MPI. RESULTS: Clinicians processed multimedia patient information differentially such that their focus, selection, and utilization of MPI influence diagnosis and satisfaction levels. CONCLUSIONS AND IMPLICATIONS: To streamline collaboration between AI and clinicians across healthcare contexts, AI should understand clinicians' patterns of MPI processing under various care environments and provide them with interpretable analytic results for them. Furthermore, clinicians must be trained with the interface and contents of AI technology and analytic assistance.

Effects of the Positive Threshold and Data Analysis on Human MOG Antibody Detection by Live Flow Cytometry.

Human autoantibodies targeting myelin oligodendrocyte glycoprotein (MOG Ab) have become a useful clinical biomarker for the diagnosis of a spectrum of inflammatory demyelinating disorders. Live cell-based assays that detect MOG Ab against conformational MOG are currently the gold standard. Flow cytometry, in which serum binding to MOG-expressing cells and control cells are quantitively evaluated, is a widely used observer-independent, precise, and reliable detection method. However, there is currently no consensus on data analysis; for example, seropositive thresholds have been reported using varying standard deviations above a control cohort. Herein, we used a large cohort of 482 sera including samples from patients with monophasic or relapsing demyelination phenotypes consistent with MOG antibody-associated demyelination and other neurological diseases, as well as healthy controls, and applied a series of published analyses involving a background subtraction (delta) or a division (ratio). Loss of seropositivity and reduced detection sensitivity were observed when MOG ratio analyses or when 10 standard deviation (SD) or an arbitrary number was used to establish the threshold. Background binding and MOG ratio value were negatively correlated, in which patients seronegative by MOG ratio had high non-specific binding, a characteristic of serum that must be acknowledged. Most MOG Ab serostatuses were similar across analyses when optimal thresholds obtained by ROC analyses were used, demonstrating the robust nature and high discriminatory power of flow cytometry cell-based assays. With increased demand to identify MOG Ab-positive patients, a consensus on analysis is vital to improve patient diagnosis and for cross-study comparisons to ultimately define MOG Ab-associated disorders.

Severity detection tool for patients with infectious disease.

Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.

Gene modification strategies using AO-mediated exon skipping and CRISPR/Cas9 as potential therapies for Duchenne muscular dystrophy patients.

Duchenne muscular dystrophy (DMD) is an X-linked genetic disease affecting 1 in 5000 young males worldwide annually. Patients experience muscle weakness and loss of ambulation at an early age, with ∼75% reduced life expectancy. Recently developed genetic editing strategies aim to convert severe DMD phenotypes to a milder disease course. Among these, the antisense oligonucleotide (AO)-mediated exon skipping and the adeno-associated viral-delivered clustered regularly interspaced short palindromic repeat (CRISPR) associated protein 9 (adeno-associated viral (AAV)-delivered CRISPR/Cas9) gene editing have shown promising results in restoring dystrophin protein expression and functionality in skeletal and heart muscle in both animals and human cells in vivo and in vitro. However, therapeutic benefits currently remain unclear. The aim of this review is to compare the potential therapeutic benefits, efficacy, safety, and clinical progress of AO-mediated exon skipping and CRISPR/Cas9 gene-editing strategies. Both techniques have demonstrated therapeutic benefit and long-term efficacy in clinical trials. AAV-delivery of CRISPR/Cas9 may potentially correct disease-causing mutations following a single treatment compared to the required continuous AO/PMO-delivery of exon skipping drugs. The latter has the potential to increase the dystrophin expression in skeletal/heart muscle with sustained effects. However, therapeutic challenges including the need for optimised delivery must be overcome in to advance current clinical data.

Machine learning approach of automatic identification and counting of blood cells.

A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a machine learning approach for automatic identification and counting of three types of blood cells using 'you only look once' (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. They also tested the trained model on smear images from a different dataset and found that the learned models are generalised. Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.

MicroRNAs and their target mRNAs as potential biomarkers among smokers and non-smokers with lung adenocarcinoma.

Lung adenocarcinoma is one of the major causes of mortality. Current methods of diagnosis can be improved through identification of disease specific biomarkers. MicroRNAs are small non-coding regulators of gene expression, which can be potential biomarkers in various diseases. Thus, the main objective of this study was to gain mechanistic insights into genetic abnormalities occurring in lung adenocarcinoma by implementing an integrative analysis of miRNAs and mRNAs expression profiles in the case of both smokers and non-smokers. Differential expression was analysed by comparing publicly available lung adenocarcinoma samples with controls. Furthermore, weighted gene co-expression network analysis is performed which revealed mRNAs and miRNAs significantly correlated with lung adenocarcinoma. Moreover, an integrative analysis resulted in identification of several miRNA-mRNA pairs which were significantly dysregulated in non-smokers with lung adenocarcinoma. Also two pairs (miR-133b/Protein Kinase C Zeta (PRKCZ) and miR-557/STEAP3) were found specifically dysregulated in smokers. Pathway analysis further revealed their role in important signalling pathways including cell cycle. This analysis has not only increased the authors' understanding about lung adenocarcinoma but also proposed potential biomarkers. However, further wet laboratory studies are required for the validation of these potential biomarkers which can be used to diagnose lung adenocarcinoma.

Non-invasive platform to estimate fasting blood glucose levels from salivary electrochemical parameters.

Diabetes is a metabolic disorder that affects more than 400 million people worldwide. Most existing approaches for measuring fasting blood glucose levels (FBGLs) are invasive. This work presents a proof-of-concept study in which saliva is used as a proxy biofluid to estimate FBGL. Saliva collected from 175 volunteers was analysed using portable, handheld sensors to measure its electrochemical properties such as conductivity, redox potential, pH and K+, Na+ and Ca2+ ionic concentrations. These data, along with the person's gender and age, were trained and tested after casewise annotation with their true FBGL values using a set of mathematical algorithms. An accuracy of 87.4 ± 1.7% and a mean relative deviation of 14.1% (R 2 = 0.76) was achieved using a mathematical algorithm. All parameters except the gender were found to play a key role in the FBGL determination process. Finally, the individual electrochemical sensors were integrated into a single platform and interfaced with the authors' algorithm through a simple graphical user interface. The system was revalidated on 60 new saliva samples and gave an accuracy of 81.67 ± 2.53% (R 2 = 0.71). This study paves the way for rapid, efficient and painless FBGL estimation from saliva.

Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria.

To predict diabetes mellitus model data mining (DM) based approaches on the dataset collected from the seven northwestern states of Nigeria. Data were collected from both primary and secondary sources through questionnaires and verbal interviews from patients with diabetic mellitus and other chronic diseases. Some hospital data were also used from the records of patients involved in this work. The dataset comprises 281 instances with 8 attributes. R programming software (version 5.3.1) was used in the experiments. The DM techniques used in this research were binomial logistic regression, classification, confusion matrix and correlation coefficient. The data were partitioned into training and testing sets. Training data were used in building the model while testing data were used to validate the model. The algorithm for the best-fitted model converges with null deviance: 281.951, residual deviance: 16.476 and AIC: 30.476. The significance variables are AGE, GLU, DBP and KDYP with 0.025, 0.01, 0.05 and 0.025 P values, respectively. The predicted model accounted for the accuracy of ∼97.1%. The correlation analysis results revealed that diabetic patients are more likely to be hypertensive than patients with other chronic diseases considered in the research.

Upper gastrointestinal anatomy detection with multi-task convolutional neural networks.

Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi-task anatomy detection convolutional neural network (MT-AD-CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors' model is able to eliminate non-informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub-branch is added to the detection network to classify NBI images, informative and non-informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false-positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.

Implementation of the pulse rhythmic rate for the efficient diagnosing of the heartbeat.

The mortality rate has risen due to the increase in number of cardiac patients in recent times due to the lack of unawareness of the symptoms. This work mainly aims to detect the anomalies of the rhythmic conditions of the pulse derived from the electrocardiogram (ECG) pattern based on correlation and the method of mapping. As this device is a programmable one and a real-time application wearable system on the wrist which is physically connected to the veins, it continuously monitors the photoplethysmography (PPG) pattern based on certain parameters and rhythmic conditions, it ensures whether the patient is under the safe condition or not. The salient features of PPG waveform are extracted with respect to various abnormal categories of ECG beats subdivided into various time durations of one, two and three. The PPG pattern using various feature extraction and the correlation transforms with the signal processing application. The extracted features help to find the skipped beat with irregularities of the rhythm will activate the emergency condition protocol in the device. The location of the patient with a critical condition is sent to the nearest health centre. This innovation is a portable one and a user-friendly application which can save many lives in the society.

Eye-tracking-aided digital system for strabismus diagnosis.

Strabismus is one of the most common vision disorders in preschool children. It can cause amblyopia and even permanent vision loss. In addition to a vision problem, strabismus brings to both children and adults serious negative impacts in their daily life, education, employment etc. Timely diagnosis of strabismus is thus crucial. However, traditional diagnosis methods conducted by ophthalmologists rely significantly on their experiences, making the diagnosis results subjective. It is also inconvenient for those methods being used for strabismus examination in large communities such as schools. In light of that, in this Letter, the authors develop an objective, digital and automatic system based on eye-tracking technique for diagnosing strabismus. The system exploits eye-tracking technique to acquire a person's eye gaze data while he or she is looking at some targets. A group of features are proposed to characterise the gaze data. The person's strabismus condition can be diagnosed according to the features. A strabismus gaze dataset is built using the system. Experimental results on the dataset demonstrate the effectiveness of the proposed system for strabismus diagnosis.

Scope of physiological and behavioural pain assessment techniques in children - a review.

Pain is an unpleasant subjective experience. At present, clinicians are using self-report or pain scales to recognise and monitor pain in children. However, these techniques are not efficient to observe the pain in children having cognitive disorder and also require highly skilled observers to measure pain. Using these techniques it is also difficult to choose the analgesic drug dosages to the patients after surgery. Thus, this conceptual work explains the demand for automatic coding techniques to evaluate pain and also it documents some evidence of techniques that act as an alternative approach for objectively determining pain in children. In this review, some good indicators of pain in children are explained in detail; they are facial expressions from an RGB image, thermal image and also feature from well proven physiological signals such as electrocardiogram, skin conductance, body temperature, surgical pleth index, pupillary reflex dilation, analgesia nociception index, photoplethysmography, perfusion index etc.

Developed wearable miniature sensor to diagnose initial perturbations of cardiorespiratory system.

The progress of microelectromechanical systems tends to fabricate miniature motion sensors that can be used for various purposes of biomedical systems, particularly on-body applications. A miniature wireless sensor is developed that not only monitors heartbeat and respiration rate based on chest movements but also identifies initial problems in the cardiorespiratory system, presenting a healthy measure defined based on height and length of the normal distribution of respiration rate and heartbeat. The obtained results of various tests are compared with two commercial sensors consisting of electrocardiogram sensor as well as belt sensor of respiration rate as a reference (gold standard), showing that the root-mean-square errors obtain <2.27 beats/min for a heartbeat and 0.93 breaths/min for respiration rate. In addition, the standard deviation of the errors reaches <1.26 and 0.63 for heartbeat and respiration rates, separately. According to the outcome results, the sensor can be considered an appropriate candidate for in-home health monitoring, particularly early detection of cardiovascular system problems.

Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm.

Osteoporosis is a life threatening disease which commonly affects women mostly after their menopause. It primarily causes mild bone fractures, which on advanced stage leads to the death of an individual. The diagnosis of osteoporosis is done based on bone mineral density (BMD) values obtained through various clinical methods experimented from various skeletal regions. The main objective of the authors' work is to develop a hybrid classifier model that discriminates the osteoporotic patient from healthy person, based on BMD values. In this Letter, the authors propose the monarch butterfly optimisation-based artificial neural network classifier which helps in earlier diagnosis and prevention of osteoporosis. The experiments were conducted using 10-fold cross-validation method for two datasets lumbar spine and femoral neck. The results were compared with other similar hybrid approaches. The proposed method resulted with the accuracy, specificity and sensitivity of 97.9% ± 0.14, 98.33% ± 0.03 and 95.24% ± 0.08, respectively, for lumbar spine dataset and 99.3% ± 0.16%, 99.2% ± 0.13 and 100, respectively, for femoral neck dataset. Further, its performance is compared using receiver operating characteristics analysis and Wilcoxon signed-rank test. The results proved that the proposed classifier is efficient and it outperformed the other approaches in all the cases.