Software Foundation Research Articles

ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research.

ChatGPT, a conversational artificial intelligence developed by OpenAI, has rapidly become an invaluable tool for researchers. With the recent integration of Python code interpretation into the ChatGPT environment, there has been a significant increase in the potential utility of ChatGPT as a research tool, particularly in terms of data analysis applications. This study aimed to assess ChatGPT as a data analysis tool and provide researchers with a framework for applying ChatGPT to data management tasks, descriptive statistics, and inferential statistics. A subset of the National Inpatient Sample was extracted. Data analysis trials were divided into data processing, categorization, and tabulation, as well as descriptive and inferential statistics. For data processing, categorization, and tabulation assessments, ChatGPT was prompted to reclassify variables, subset variables, and present data, respectively. Descriptive statistics assessments included mean, SD, median, and IQR calculations. Inferential statistics assessments were conducted at varying levels of prompt specificity ("Basic," "Intermediate," and "Advanced"). Specific tests included chi-square, Pearson correlation, independent 2-sample t test, 1-way ANOVA, Fisher exact, Spearman correlation, Mann-Whitney U test, and Kruskal-Wallis H test. Outcomes from consecutive prompt-based trials were assessed against expected statistical values calculated in Python (Python Software Foundation), SAS (SAS Institute), and RStudio (Posit PBC). ChatGPT accurately performed data processing, categorization, and tabulation across all trials. For descriptive statistics, it provided accurate means, SDs, medians, and IQRs across all trials. Inferential statistics accuracy against expected statistical values varied with prompt specificity: 32.5% accuracy for "Basic" prompts, 81.3% for "Intermediate" prompts, and 92.5% for "Advanced" prompts. ChatGPT shows promise as a tool for exploratory data analysis, particularly for researchers with some statistical knowledge and limited programming expertise. However, its application requires careful prompt construction and human oversight to ensure accuracy. As a supplementary tool, ChatGPT can enhance data analysis efficiency and broaden research accessibility.

Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study.

Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma. This study aims to help clinicians implement timely and appropriate interventions to reduce the incidence of PPTIC and related complications, thereby lowering in-hospital mortality and disability rates for patients with trauma. We analyzed data from 13,235 patients with trauma from 4 medical centers, including medical histories, laboratory results, and hospitalization complications. We developed 10 ML models in Python (Python Software Foundation) to predict PPTIC based on preoperative indicators. Data from 10,023 Medical Information Mart for Intensive Care patients were divided into training (70%) and test (30%) sets, with 3212 patients from 3 other centers used for external validation. Model performance was assessed with 5-fold cross-validation, bootstrapping, Brier score, and Shapley additive explanation values. Univariate logistic regression identified PPTIC risk factors as (1) prolonged activated partial thromboplastin time, prothrombin time, and international normalized ratio; (2) decreased levels of hemoglobin, hematocrit, red blood cells, calcium, and sodium; (3) lower admission diastolic blood pressure; (4) elevated alanine aminotransferase and aspartate aminotransferase levels; (5) admission heart rate; and (6) emergency surgery and perioperative transfusion. Multivariate logistic regression revealed that patients with PPTIC faced significantly higher risks of sepsis (1.75-fold), heart failure (1.5-fold), delirium (3.08-fold), abnormal coagulation (3.57-fold), tracheostomy (2.76-fold), mortality (2.19-fold), and urinary tract infection (1.95-fold), along with longer hospital and intensive care unit stays. Random forest was the most effective ML model for predicting PPTIC, achieving an area under the receiver operating characteristic of 0.91, an area underthe precision-recallcurve of 0.89, accuracy of 0.84, sensitivity of 0.80, specificity of 0.88, precision of 0.88, F1-score of 0.84, and Brier score of 0.13 in external validation. Key PPTIC risk factors include (1) prolonged activated partial thromboplastin time, prothrombin time, and international normalized ratio; (2) low levels of hemoglobin, hematocrit, red blood cells, calcium, and sodium; (3) low diastolic blood pressure; (4) elevated alanine aminotransferase and aspartate aminotransferase levels; (5) admission heart rate; and (6) the need for emergency surgery and transfusion. PPTIC is associated with severe complications and extended hospital stays. Among the ML models, the random forest model was the most effective predictor. Chinese Clinical Trial Registry ChiCTR2300078097; https://www.chictr.org.cn/showproj.html?proj=211051.

Association of First-Trimester Low PAPP-A Levels (<0.2 MoM) with Maternal and Fetal Outcomes

Background and Aim: First-trimester screening is routinely performed globally to detect chromosomal abnormalities using non-invasive methods such as nuchal translucency (NT) measurements, pregnancy-associated plasma protein A (PAPP-A), and β-human chorionic gonadotropin (β-hCG). Recent studies have highlighted that low PAPP-A levels may be associated with adverse pregnancy outcomes, including pre-eclampsia, intrauterine growth restriction (IUGR), and pre-term delivery. Generally, low PAPP-A is defined as below 0.4 multiples of the median (MoM) or under the 5th percentile. However, extremely low PAPP-A, defined as levels below 0.2 MoM or under the 1st percentile, significantly increases the risk of adverse outcomes. This category has received less attention in research. Our study aims to investigate the correlation between PAPP-A levels below 0.2 MoM and adverse pregnancy outcomes. Materials and Methods: A retrospective cross-sectional study was conducted on 10,256 pregnant women who underwent first-trimester screening at Imam Khomeini Hospital in Ahvaz between January 2010 and April 2024. PAPP-A and β-hCG levels were measured, and factors such as maternal age, weight, parity, and abortion history were assessed. Data were obtained using the Fetal Medicine Foundation (FMF) software and medical records. Statistical analysis was performed using SPSS version 26. Results: Out of 10,256 pregnancies, 6,040 (6.4%) had PAPP-A levels below the 5th percentile, while 45 women had PAPP-A levels below 0.2 MoM. The average age of these women was 30.4 ± 4.812 years. Among those with PAPP-A < 0.2 MoM, 2.2% had trisomy 21, 6.7% had IUGR, 26.7% experienced gestational hypertension, and 15.6% developed pre-eclampsia. Trisomies 13 and 18 were not observed in the study population. Conclusions: Pregnant women with PAPP-A levels below 0.2 MoM showed increased incidences of trisomy 21, pre-eclampsia, gestational hypertension, and IUGR. Although low PAPP-A appears to contribute to both maternal and fetal complications, it cannot be used independently to predict adverse pregnancy outcomes. Further large-scale studies are required to better understand the implications of extremely low PAPP-A on pregnancy outcomes.

Evaluating Bard Gemini Pro and GPT-4 Vision Against Student Performance in Medical Visual Question Answering: Comparative Case Study.

The rapid development of large language models (LLMs) such as OpenAI's ChatGPT has significantly impacted medical research and education. These models have shown potential in fields ranging from radiological imaging interpretation to medical licensing examination assistance. Recently, LLMs have been enhanced with image recognition capabilities. This study aims to critically examine the effectiveness of these LLMs in medical diagnostics and training by assessing their accuracy and utility in answering image-based questions from medical licensing examinations. This study analyzed 1070 image-based multiple-choice questions from the AMBOSS learning platform, divided into 605 in English and 465 in German. Customized prompts in both languages directed the models to interpret medical images and provide the most likely diagnosis. Student performance data were obtained from AMBOSS, including metrics such as the "student passed mean" and "majority vote." Statistical analysis was conducted using Python (Python Software Foundation), with key libraries for data manipulation and visualization. GPT-4 1106 Vision Preview (OpenAI) outperformed Bard Gemini Pro (Google), correctly answering 56.9% (609/1070) of questions compared to Bard's 44.6% (477/1070), a statistically significant difference (χ2₁=32.1, P<.001). However, GPT-4 1106 left 16.1% (172/1070) of questions unanswered, significantly higher than Bard's 4.1% (44/1070; χ2₁=83.1, P<.001). When considering only answered questions, GPT-4 1106's accuracy increased to 67.8% (609/898), surpassing both Bard (477/1026, 46.5%; χ2₁=87.7, P<.001) and the student passed mean of 63% (674/1070, SE 1.48%; χ2₁=4.8, P=.03). Language-specific analysis revealed both models performed better in German than English, with GPT-4 1106 showing greater accuracy in German (282/465, 60.65% vs 327/605, 54.1%; χ2₁=4.4, P=.04) and Bard Gemini Pro exhibiting a similar trend (255/465, 54.8% vs 222/605, 36.7%; χ2₁=34.3, P<.001). The student majority vote achieved an overall accuracy of 94.5% (1011/1070), significantly outperforming both artificial intelligence models (GPT-4 1106: χ2₁=408.5, P<.001; Bard Gemini Pro: χ2₁=626.6, P<.001). Our study shows that GPT-4 1106 Vision Preview and Bard Gemini Pro have potential in medical visual question-answering tasks and to serve as a support for students. However, their performance varies depending on the language used, with a preference for German. They also have limitations in responding to non-English content. The accuracy rates, particularly when compared to student responses, highlight the potential of these models in medical education, yet the need for further optimization and understanding of their limitations in diverse linguistic contexts remains critical.

Advancing 3D dental scanning: The use of photogrammetry with light detection and ranging for edentulous arches

The advent of computer-aided design and computer-aided manufacturing (CAD-CAM) has necessitated the acquisition of digital scans. However, there are limitations and problems with acquiring accurate 3-dimensional (3D) casts from edentulous patients, especially in the presence of saliva. The purpose of this in vitro study was to develop a novel approach for obtaining 3D casts of edentulous arches by using 2-dimensional (2D) images as an alternative to traditional 3D scanners with and without light detection and ranging (LiDAR). This study comprised 6 groups, each consisting of 10 specimens. For the control group, 3D casts were generated by scanning edentulous mandibular molds using a dental laboratory scanner. Experimental groups included photogrammetry with and without LiDAR under various conditions (Groups PG360, PG120, LPG120, PG360S, LPG120S). For Groups PG120, LPG120, and LPG120S, a custom-made manikin was used. In all photogrammetry groups, images of each mold were captured with a mobile phone (iPhone 14 Pro Max). The casts from the experimental groups were superimposed onto those from the control group using the Blender Foundation software program (Version 3.6.1). The mean distances were calculated and statistically analyzed using 1-way ANOVA followed by the post hoc Tukey test (α=.05). The mean distances between the experimental groups and the control group varied significantly. The PG360 and PG120 groups showed a statistically significant difference from the control group (P<.001, 95% CI), with mean distances of 1.54 ±0.31 mm and 4.54 ±1.65 mm, respectively. The LPG120S group, which combined photogrammetry with LiDAR in the presence of artificial saliva, achieved a mean distance of 2.03 ±0.46mm, which was not significantly different from the control group (P=.501, 95% CI). The successful scanning of edentulous mandibular molds using a mobile phone was achieved through a combination of 2D images and LiDAR, covering a limited access angle of 120 degrees. Compared with other techniques, the method developed the most accurate 3D casts and was less susceptible to interference from saliva, a significant issue for intraoral scanners.

Revisit to Endolymphatic Duct Identification Using Middle Cranial Fossa Dural Plate: A Novel Technique.

Surgical treatments for Ménière's disease differ in efficacy. Endolymphatic duct blockage (EDB) is favored for its minimal risk and ability to preserve hearing. One of the main challenges in the technique is the difficulty in accurately identifying the endolymphatic duct (ED). The aim of the study is to see the feasibility of identifying the ED opening using a middle cranial fossa (MCF) dural plate as a reference point in a wet temporal bone and to measure the angle between the ED opening and the MCFdural plate which in the future can simplify the procedure ofED blockage. This prospective observational study, conducted from February to April 2024 at R L Jalappa Hospital, involved dissecting 20 wet temporal bones. A complete cortical mastoidectomy exposed the MCF dural plate, semicircular canals, sigmoid sinus, and sinodural angle. Using Donaldsonline ES, through blind controlled drilling the operculum of the vestibular aqueduct was identified. The angle between the ED opening and the MCF dural plate was measured using GNU Image Manipulation Program (GIMP) software (Free Software Foundation Ltd., Boston, MA, USA). A tangential line from the MCF dural plate to the ED opening was drawn using the same software and the line was named the "GauthamaPrasads" (GP) line. The study documented angles between the ED opening and the MCF dural plate, which ranged from 36.00° to 45.99° (mean angles from 20 temporal bones provided). The angle of 36.00° to 45.99° between the MCF dural plate and ED was consistently observed, thus proving that the MCF dural plate isa reliable anatomical landmark for surgeons for identification of ED. Accurate identification of the ED was feasible using this new anatomical landmark. These findings provide valuable anatomical insights that can aid in more efficient and accurate surgical interventions involving the ED.

Clinical and epidemiological characteristics of patients with pheochromocytoma and retroperitoneal paraganglioma

Introduction. Pheochromocytoma/paraganglioma (PPGL) is a rare hormonally active tumor of chromaffin tissue. Delayed diagnosis of this disease can lead to severe, even fatal, complications. Surgical treatment of PPGL without the necessary preoperative preparation can cause intraoperative uncontrolled hemodynamic impairment. In addition, since 2017, PPGL has been recognized as a malignant tumor due to the presence of metastatic potential. All these factors determine the need for early diagnosis of PPGL, which is based, first of all, on knowledge of the clinical picture by specialists in various fields.Aim. To check and confirm the epidemiological characteristics of PPGL as well as the most common clinical symptoms in the whole group of PPGL and in subgroups of clinical forms; to form a risk group for screening for PPGL.Materials and methods. There were enrolled 353 patients in the study. These patients were operated on at the St. Petersburg State University Hospital in the period from 2010 to 2022 inclusive. All the patients were verified PPGL diagnosis histologically. Clinical symptoms were assessed: a) retrospectively, by analyzing complaints and anamnesis from the initial examination in medical histories upon admission to the hospital for surgical treatment b) both in the entire group as a whole and in subgroups of clinical forms (paroxysmal, constant, mixed, asymptomatic). Statistical analysis was performed using the Python 3.11 programming language (Python Software Foundation, USA). descriptive statistics of quantitative characteristics are presented in the form of medians, first and third quartiles in me format [q1; q3], categorical features – in the form of absolute and relative frequencies in the format n (%). the frequency histogram was constructed using the Matplotlib 3.8.2 library.Results. PPGL is most common in women over 40 years of age. The most common symptoms of PPGL are the following: a feeling of rapid heartbeat (55.95%), headache (41.07%), arterial hypertension (AH) in the form of hypertensive crises (55.65%), constant increase in blood pressure (38.39%), episodes of moderate increases in blood pressure (24.11%), sweating (30.65%), general weakness and fatigue (26.19%), hot flashes or chills (24.11%). These symptoms are formed into the criteria for screening for PPGL. In the asymptomatic clinical form – lower back pain is more common (14%); with paroxysmal – panic attacks (10%); with mixed – thirst and dry mouth (10%), and with constant form – the actual constant nature of hypertension (100%).Conclusion. In a large sample of patients with a rare pathology, the most characteristic symptom complex was identified, characteristic of both PPGL and individual clinical forms. A risk group has been formed for screening for PPGL.

Evaluating Sustainable Digitalization: A Carbon-Aware Framework for Enhancing Eco-Friendly Business Process Reengineering

In an era where sustainability is paramount, understanding the environmental impact of digitalizing business processes is critical. Despite the growing emphasis on sustainable practices, there is a lack of comprehensive methodologies to evaluate how digitalization impacts environmental sustainability compared to traditional processes. This paper introduces a carbon-aware methodological framework specifically designed to assess the sustainability of business process reengineering through digitalization. The Digital Green framework quantitatively analyzes the environmental costs associated with digital transformation, ensuring that truly sustainable digitalization results in lower resource consumption relative to the complexity of the process being digitalized. To demonstrate its effectiveness, the framework was applied to a case study involving the reengineering of an administrative process at a small university in southern Italy. The case study highlighted the framework’s ability to quantify the environmental benefits or detriments of digital transformation, thus guiding organizations toward more sustainable digital practices. This research contributes to the field by offering a concrete tool for aligning digitalization efforts with ecological sustainability, and by paving the way for integration with initiatives such as the Green Software Foundation’s Software Carbon Intensity (SCI) specifications.

Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.

Small clinics are important in providing health care in local communities. Accurately predicting their closure would help manage health care resource allocation. There have been few studies on the prediction of clinic closure using machine learning techniques. This study aims to test the feasibility of predicting the closure of medical and dental clinics (MCs and DCs, respectively) and investigate important factors associated with their closure using machine running techniques. The units of analysis were MCs and DCs. This study used health insurance administrative data. The participants of this study ran and closed clinics between January 1, 2020, and December 31, 2021. Using all closed clinics, closed and run clinics were selected at a ratio of 1:2 based on the locality of study participants using the propensity matching score of logistic regression. This study used 23 and 19 variables to predict the closure of MCs and DCs, respectively. Key variables were extracted using permutation importance and the sequential feature selection technique. Finally, this study used 5 and 6 variables of MCs and DCs, respectively, for model learning. Furthermore, four machine learning techniques were used: (1) logistic regression, (2) support vector machine, (3) random forest (RF), and (4) Extreme Gradient Boost. This study evaluated the modeling accuracy using the area under curve (AUC) method and presented important factors critically affecting closures. This study used SAS (version 9.4; SAS Institute Inc) and Python (version 3.7.9; Python Software Foundation). The best-fit model for the closure of MCs with cross-validation was the support vector machine (AUC 0.762, 95% CI 0.746-0.777; P<.001) followed by RF (AUC 0.736, 95% CI 0.720-0.752; P<.001). The best-fit model for DCs was Extreme Gradient Boost (AUC 0.700, 95% CI 0.675-0.725; P<.001) followed by RF (AUC 0.687, 95% CI 0.661-0.712; P<.001). The most significant factor associated with the closure of MCs was years of operation, followed by population growth, population, and percentage of medical specialties. In contrast, the main factor affecting the closure of DCs was the number of patients, followed by annual variation in the number of patients, year of operation, and percentage of dental specialists. This study showed that machine running methods are useful tools for predicting the closure of small medical facilities with a moderate level of accuracy. Essential factors affecting medical facility closure also differed between MCs and DCs. Developing good models would prevent unnecessary medical facility closures at the national level.

The Target Detection of Wear Particles in Ferrographic Images Based on the Improved YOLOv8

An enhanced YOLOv8 algorithm is proposed in the following paper to address challenging issues encountered in ferrographic image target detection, such as the identification of complex-shaped wear particles, overlapping and intersecting wear particles, and small and edge-wear particles. This aim is achieved by integrating the main body network with the improved Deformable Convolutional Network v3 to enhance feature extraction capabilities. Additionally, the Dysample method is employed to optimize the upsampling technique in the neck network, resulting in a clearer fused feature image and improved precision for detecting small and edge-wear particles. In the head network, parameter sharing simplifies the detection head while enhancing convergence speed and precision through improvements made to the loss function. The experimental results of the present study demonstrate that compared to the original algorithm, this enhanced approach achieves an average precision improvement of 5.6% without compromising the detection speed (111.6FPS), therefore providing valuable support for online monitoring device software foundations.

Free open source communities sustainability: Does it make a difference in software quality?

ContextFree and Open Source Software (FOSS) communities’ ability to stay viable and productive over time is pivotal for society as they maintain the building blocks that digital infrastructure, products, and services depend on. Sustainability may, however, be characterized from multiple aspects, and less is known how these aspects interplay and impact community outputs, and software quality specifically.ObjectiveThis study, therefore, aims to empirically explore how the different aspects of FOSS sustainability impact software quality.Method16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects sourced from the Apache Software Foundation Incubator program. The impact of a decline in the sustainability metrics was analyzed against eight software quality metrics using Bayesian data analysis, which incorporates probability distributions to represent the regression coefficients and intercepts.ResultsFindings suggest that selected sustainability metrics do not significantly affect defect density or code coverage. However, a positive impact of community age was observed on specific code quality metrics, such as risk complexity, number of very large files, and code duplication percentage. Interestingly, findings show that even when communities are experiencing sustainability, certain code quality metrics are negatively impacted.ConclusionFindings imply that code quality practices are not consistently linked to sustainability, and defect management and prevention may be prioritized over the former. Results suggest that growth, resulting in a more complex and large codebase, combined with a probable lack of understanding of code quality standards, may explain the degradation in certain aspects of code quality.

NiADA (Non-invasive Anemia Detection App), a Smartphone-Based Application With Artificial Intelligence to Measure Blood Hemoglobin in Real-Time: A Clinical Validation.

Background Anemia is a severe public health problem in India affecting more than half of the population. To reduce its burden on the population, the Government of India under the Anemia Mukt Bharat program has adopted a monitoring strategy for the diagnosis and treatment of anemia. Point-of-care testing (POCT) devices play a pivotal role in testing hemoglobin at a community level where sophisticated laboratory instruments are not available. The majority of the currently available POCT devices are invasive in nature which have their own limitations. A non-invasive method of hemoglobin estimation will address many of the limitations of an invasive POCT instrument, which will further improve people's acceptability for hemoglobin testing. The Non-Invasive Anemia Detection App (NiADA) (Monere AI Private Limited, Kolkata, West Bengal, India), a non-invasive POCT application, uses artificial intelligence (AI) to predict the hemoglobin level from lower eyelid images. This real-time, point-of-care, low-cost solution uses a custom computer vision deep-learning algorithm to determine blood hemoglobin value. Method The study validates an AI-based smartphone application NiADAagainst laboratory hemoglobin estimation and a widely used point-of-care hemoglobin estimation instrument (HemoCue Hb 301; HemoCue AB, Angelholm, Skane County, Sweden). The study was conducted in a tertiary care hospital in Eastern India and recruited a total of 556 participants. These included 58 pediatric patients, 51 pregnant women, 214 adult females, and 224 adult males. Statistical analysis was performed using Python (Python Software Foundation, Wilmington, Delaware, United States). A p-value of < 0.05 was taken to be significant. Result The mean difference observed between NiADA and laboratory-estimated hemoglobin values came out to be -0.29 g/dL and -0.89 g/dL for adult females and males respectively, and 0.61 g/dL for pregnant women and -0.69g/dL for the pediatric population. The limits of agreement for NiADA were narrow at 2.77 to -2.18 g/dL for adult females, 3.76 to -1.96 g/dL for adult males, 1.89 to -3.29 g/dL for pregnant women, and 3.28 to -2.08 g/dL for the pediatric population. The sensitivity and specificity of the NiADA application against the laboratory estimation method were 75.8% and 53.8% for adult females, 70.0% and 48.3% for adult males, 23.8% and 90% for pregnant females, and 75% and 57% for the pediatric population. Conclusion As a non-invasive application, NiADA's performance is satisfactory and comparable with minimally invasive tools like HemoCue Hb 301 and otherPOCT devices.

Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: Tutorial.

Families of individuals with neurodevelopmental disabilities or differences (NDDs) often struggle to find reliable health information on the web. NDDs encompass various conditions affecting up to 14% of children in high-income countries, and most individuals present with complex phenotypes and related conditions. It is challenging for their families to develop literacy solely by searching information on the internet. While in-person coaching can enhance care, it is only available to a minority of those with NDDs. Chatbots, or computer programs that simulate conversation, have emerged in the commercial sector as useful tools for answering questions, but their use in health care remains limited. To address this challenge, the researchers developed a chatbot named CAMI (Coaching Assistant for Medical/Health Information) that can provide information about trusted resources covering core knowledge and services relevant to families of individuals with NDDs. The chatbot was developed, in collaboration with individuals with lived experience, to provide information about trusted resources covering core knowledge and services that may be of interest. The developers used the Django framework (Django Software Foundation) for the development and used a knowledge graph to depict the key entities in NDDs and their relationships to allow the chatbot to suggest web resources that may be related to the user queries. To identify NDD domain-specific entities from user input, a combination of standard sources (the Unified Medical Language System) and other entities were used which were identified by health professionals as well as collaborators. Although most entities were identified in the text, some were not captured in the system and therefore went undetected. Nonetheless, the chatbot was able to provide resources addressing most user queries related to NDDs. The researchers found that enriching the vocabulary with synonyms and lay language terms for specific subdomains enhanced entity detection. By using a data set of numerous individuals with NDDs, the researchers developed a knowledge graph that established meaningful connections between entities, allowing the chatbot to present related symptoms, diagnoses, and resources. To the researchers' knowledge, CAMI is the first chatbot to provide resources related to NDDs. Our work highlighted the importance of engaging end users to supplement standard generic ontologies to named entities for language recognition. It also demonstrates that complex medical and health-related information can be integrated using knowledge graphs and leveraging existing large datasets. This has multiple implications: generalizability to other health domains as well as reducing the need for experts and optimizing their input while keeping health care professionals in the loop. The researchers' work also shows how health and computer science domains need to collaborate to achieve the granularity needed to make chatbots truly useful and impactful.

Hybrid feature selection in a machine learning predictive model for perioperative myocardial injury in noncoronary cardiac surgery with cardiopulmonary bypass.

Perioperative myocardial injury (PMI) is associated with increased mobility and mortality after noncoronary cardiac surgery. However, limited studies have developed a predictive model for PMI. Therefore, we used hybrid feature selection (FS) methods to establish a predictive model for PMI in noncoronary cardiac surgery with cardiopulmonary bypass (CPB). This was a single-center retrospective study conducted at the Fuwai Hospital in China. Patients aged 18-70years who underwent elective noncoronary surgery with CPB at our institution from December 2018 to April 2021 were enrolled. The primary outcome was PMI, defined as the postoperative cardiac troponin I (cTnI) levels exceeding 220 times of upper reference limit (URL). Statistical analyses were conducted by Python (Python Software Foundation, version 3.9.7 and integrated development environment Jupyter Notebook 1.1.0) and SPSS software version 26.0 (IBM Corp., Armonk, New York, USA). A total of 1130 patients were eventually eligible for this study. The incidence of PMI was 20.3% (229/1130) in the overall patients, 20.6% (163/791) in the training dataset, and 19.5% (66/339) in the testing dataset. The logistic regression model performed the best AUC of 0.6893 (95 CI%: 0.6371-0.7382) by the traditional selection method, and the random forest model performed the best AUC of 0.6937 (95 CI%: 0.6416-0.7423) by the union of Wrapper and Embedded method, and the CatBoost model performed the best AUC of 0.6828 (95 CI%: 0.6304-0.7320) by the union of Embedded and forward logistic regression technique, and the Naïve Bayes model achieved the best AUC with 0.7254 (95 CI%: 0.6746-0.7723) by forwarding logistic regression method. Moreover, the decision tree, KNeighborsClassifier, and support vector machine models performed the worse AUC in all selection forms. Furthermore, the SHapley Additive exPlanations plot showed that prolonged CPB, aortic clamp time, and preoperative low platelets count were strongly related to the PMI risk. In total, four category feature selection methods were utilized, comprising five individual selection techniques and 15 combined methods. Notably, the combination of logistic regression and embedded methods demonstrated outstanding performance in predicting PMI risk. We also concluded that the machine learning model, including random forest, catboost, and Naive Bayes, were suitable candidates for establishing PMI predictive model. Nevertheless, additional investigation and validation are imperative for substantiating these finding.

An open-source research platform for mechanical ventilation based on Simulink® and STM32 nucleo

Abstract Gain in knowledge in experimental fields of research is mostly achieved using commercial hardware platforms. The linked costs however can limit a broad access by scientists and in the specific case of mechanical ventilation the pandemic demonstrated that further and collective research is essential and necessary. This contribution presents the modular design and implementation of a research platform which is based on STM32 Nucleo boards and Simulink®. The hardware required such as the pneumatic circuit are covered as well as the software architecture in which clinician related settings are processed for control references. The core functionality of mandatory volume controlled (VCV) and pressure controlled ventilation (PCV) while ensuring a positive end-expiratory pressure for the patient ( PEEP ) $(\text{PEEP})$ is evaluated and the foundational software models are published in a repository.

A Multidisciplinary Assessment of ChatGPT's Knowledge of Amyloidosis: Observational Study.

Amyloidosis, a rare multisystem condition, often requires complex, multidisciplinary care. Its low prevalence underscores the importance of efforts to ensure the availability of high-quality patient education materials for better outcomes. ChatGPT (OpenAI) is a large language model powered by artificial intelligence that offers a potential avenue for disseminating accurate, reliable, and accessible educational resources for both patients and providers. Its user-friendly interface, engaging conversational responses, and the capability for users to ask follow-up questions make it a promising future tool in delivering accurate and tailored information to patients. We performed a multidisciplinary assessment of the accuracy, reproducibility, and readability of ChatGPT in answering questions related to amyloidosis. In total, 98 amyloidosis questions related to cardiology, gastroenterology, and neurology were curated from medical societies, institutions, and amyloidosis Facebook support groups and inputted into ChatGPT-3.5 and ChatGPT-4. Cardiology- and gastroenterology-related responses were independently graded by a board-certified cardiologist and gastroenterologist, respectively, who specialize in amyloidosis. These 2 reviewers (RG and DCK) also graded general questions for which disagreements were resolved with discussion. Neurology-related responses were graded by a board-certified neurologist (AAH) who specializes in amyloidosis. Reviewers used the following grading scale: (1) comprehensive, (2) correct but inadequate, (3) some correct and some incorrect, and (4) completely incorrect. Questions were stratified by categories for further analysis. Reproducibility was assessed by inputting each question twice into each model. The readability of ChatGPT-4 responses was also evaluated using the Textstat library in Python (Python Software Foundation) and the Textstat readability package in R software (R Foundation for Statistical Computing). ChatGPT-4 (n=98) provided 93 (95%) responses with accurate information, and 82 (84%) were comprehensive. ChatGPT-3.5 (n=83) provided 74 (89%) responses with accurate information, and 66 (79%) were comprehensive. When examined by question category, ChatGTP-4 and ChatGPT-3.5 provided 53 (95%) and 48 (86%) comprehensive responses, respectively, to "general questions" (n=56). When examined by subject, ChatGPT-4 and ChatGPT-3.5 performed best in response to cardiology questions (n=12) with both models producing 10 (83%) comprehensive responses. For gastroenterology (n=15), ChatGPT-4 received comprehensive grades for 9 (60%) responses, and ChatGPT-3.5 provided 8 (53%) responses. Overall, 96 of 98 (98%) responses for ChatGPT-4 and 73 of 83 (88%) for ChatGPT-3.5 were reproducible. The readability of ChatGPT-4's responses ranged from 10th to beyond graduate US grade levels with an average of 15.5 (SD 1.9). Large language models are a promising tool for accurate and reliable health information for patients living with amyloidosis. However, ChatGPT's responses exceeded the American Medical Association's recommended fifth- to sixth-grade reading level. Future studies focusing on improving response accuracy and readability are warranted. Prior to widespread implementation, the technology's limitations and ethical implications must be further explored to ensure patient safety and equitable implementation.

Perceived attitudes toward LGBTQ + physicians among individuals with psychiatric illness in the religiously diverse Southern Region of Thailand: a multihospital-based survey

ObjectivesThis study purposed to analyze perceived attitudes toward LGBTQ + physicians and related factors among individuals with psychiatric illnesses in southern Thailand.Materials and methodsFrom May to July 2023, a cross-sectional study was conducted at four psychiatric outpatient clinics in Southern Thailand. The questionnaires utilized were: 1) a demographic information questionnaire, 2) a questionnaire regarding attitudes toward LGBTQ + physicians, 3) a questionnaire evaluating individuals’ attitudes toward LGBTQ + physicians while receiving medical attention, and 4) a patient-doctor relationship questionnaire. All data were analyzed using descriptive statistics, and the factors associated with perceived attitudes toward LGBTQ + physicians were analyzed using the Wilcoxon rank sum test, the Kruskal–Wallis test, and linear regressions. The analyses were conducted using the R Foundation for Statistical Computing software, version 4.3.1. Statistical significance was defined as a p-value of less than 0.05.ResultsAmong our 542 participants, the mean age was 36.3 ± 14.1 years. The majority were female (64.6%), Buddhist (62.4%), and diagnosed with depression (46.3%). Approximately three-quarters showed a good doctor-patient relationship (74.0%). The median (IQR) score of the perceived attitudes toward LGBTQ + physicians was 75 (66, 88). Predominantly, the LGBTQ + physicians were perceived as normal (76.3%) and being a viable part of society (88.7%). Moreover, our participants disagreed with the view that being an LGBTQ + physician was a sin (70.6%) or immoral (68.2%). They felt comfortable during history taking (79.0%), physical examination not involving private parts of the body (72.5%), and management for both medical (78.4%) and psychiatric conditions (81.4%) at the hands of LGBTQ + physicians. However, they reported feeling uncomfortable during history taking involving private matters (6.3%) and the physical examination of private parts (16.4%). Older age, absence of LGBTQ + close relatives/friends, and being a Muslim were associated with lower scores of perceived attitudes toward LGBTQ + physicians. Conversely, a higher level of education and a reported mismatch between the patient’s sex and gender were associated with higher scores.ConclusionsMost participants reported positive perceived attitudes toward LGBTQ + physicians. However, some age groups and adherents of Islam showed lower perceived attitude scores and reported feeling uncomfortable receiving medical treatment from LGBTQ + physicians. On the one hand, LGBTQ + physicians have cause to be concerned about this point; on the other hand, finding appropriate approaches to promote positive attitudes toward LGBTQ + physicians among these groups of people remains a necessity.

Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.

The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Development of Predictive Models Based on Biochemical Parameters to Triage COVID-19 Patients: A Study Conducted in a Tertiary Care Hospital.

Background The COVID-19 disease continues to cause severe mortality and morbidity. Biochemical parameters are being used to predict the severity of the infection. This study aims to predict disease severity and mortality to help reduce mortality through timely intervention in a cost-effective way. Methods A total of 324 COVID-19 casesadmitted at our hospital (All India Institute of Medical Sciences, Patna, BR, India) betweenJune 2020 to December 2020 (phase 1: 190 patients) and April 2021 to May 2021 (phase 2: 134 patients) were recruited for this study. Statistical analysis was done using SPSS Statistics version 23 (IBM Corp., Armonk, NY, USA) and model prediction using Python (The Python Software Foundation, Wilmington, DE, USA). Results There were significant differences in biochemical parameters at the time of admission among COVID-19 patients between phases 1 and 2, ICU and non-ICU admissions, and expired and discharged patients. Thereceiver operating characteristic (ROC)curves predicted mortality solely based on biochemical parameters. Using multiple logistic regression in Python, a total of four models (two each) were developed to predict ICU admission and mortality. A total of 92 out of 96 patients were placed into the correct management category by our model. This model would have allowed us to preserve 17 of the 21 patients we lost. Conclusions We developed predictive models for admission (ICU or non-ICU) and mortality based on biochemical parameters at the time of admission. A predictive model with a significant predictive capability for IL-6 and procalcitonin values using normal biochemical parameters was proposed. Both can be used as machine learning tools to prognosticate the severity of COVID-19 infections. This study is probably the first of its kind to propose triage for admission in the ICU or non-ICU at the medical emergency department during the first presentation for the necessary optimal treatment of COVID-19 based on a predictive model.

Niimath and fslmaths: replication as a method to enhance popular neuroimaging tools.

Neuroimaging involves the acquisition of extensive 3D images and 4D time series data to gain insights into brain structure and function. The analysis of such data necessitates both spatial and temporal processing. In this context, "fslmaths" has established itself as a foundational software tool within our field, facilitating domain-specific image processing. Here, we introduce "niimath," a clone of fslmaths. While the term "clone" often carries negative connotations, we illustrate the merits of replicating widely-used tools, touching on aspects of licensing, performance optimization, and portability. For instance, our work enables the popular functions of fslmaths to be disseminated in various forms, such as a high-performance compiled R package known as "imbibe", a Windows executable, and a WebAssembly plugin compatible with JavaScript. This versatility is demonstrated through our NiiVue live demo web page. This application allows 'edge computing' where image processing can be done with a zero-footprint tool that runs on any web device without requiring private data to be shared to the cloud. Furthermore, our efforts have contributed back to FSL, which has integrated the optimizations that we've developed. This synergy has enhanced the overall transparency, utility and efficiency of tools widely relied upon in the neuroimaging community.