Machine learning in transfusion medicine: A scoping review.

The update of the preventive activities for this year 2022 in the field of infectious diseases is of special relevance due to the importance that prevention has gained and more specifically, vaccination as a tool to control the pandemic caused by the SARS-CoV-2 virus declared on March 11, 2020. The pandemic has focused much of the prevention efforts on its containment, but the importance of maintaining high vaccination coverage of the rest of the recommended vaccines to maintain good control of vaccine-preventable diseases and avoid complications in particularly vulnerable patients should not be forgotten. In this year's review we present a practical document with the aim of providing tools to primary care professionals who work with adults, to make the indication of each vaccine whether it is systematically recommended or if it is because the patient belongs to some risk group due to their condition or underlying pathology. In this way, throughout the document, we will comment on the most innovative aspects of systematic vaccination (flu, pneumococcus, meningococcal vaccines and vaccines against the human papillomavirus [HPV]), the new vaccines (pandemic vaccines against COVID-19, vaccines against herpes zoster of subunits, vaccines against monkeypox) and the recommended vaccines according to risk condition (pregnancy and lactation, travelers, patients with immunosuppression or underlying pathology). 

[Infectious Disease Prevention Group: Update on vaccines, 2022].

Patients with triple‐negative breast cancer (TNBC) exhibit a fast tumor growth rate and poor survival outcomes. In this study, we aimed to develop and compare intelligent algorithms using ultrasound radiomics features in addition to clinical variables to identify patients with TNBC prior to histopathologic diagnosis.We used single‐center, retrospective data of patients who underwent ultrasound before histopathologic verification and subsequent neoadjuvant systemic treatment (NAST). We developed a logistic regression with an elastic net penalty algorithm using pretreatment ultrasound radiomics features in addition to patient and tumor variables to identify patients with TNBC. Findings were compared to the histopathologic evaluation of the biopsy specimen. The main outcome measure was the area under the curve (AUC).We included 1161 patients, 813 in the development set and 348 in the validation set. Median age was 50.1 years and 24.4% (283 of 1161) had TNBC. The integrative model using radiomics and clinical information showed significantly better performance in identifying TNBC compared to the radiomics model (AUC: 0.71, 95% confidence interval [CI]: 0.65–0.76 versus 0.64, 95% CI: 0.57–0.71, P = .004). The five most important variables were cN status, shape surface volume ratio (SA:V), gray level co‐occurrence matrix (GLCM) correlation, gray level dependence matrix (GLDM) dependence nonuniformity normalized, and age. Patients with TNBC were more often categorized as BI‐RADS 4 than BI‐RADS 5 compared to non‐TNBC patients (P = .002).A machine learning algorithm showed promising potential to identify patients with TNBC using ultrasound radiomics features and clinical information prior to histopathologic evaluation.

Ultrasound Radiomics Features to Identify Patients With Triple‐Negative Breast Cancer: A Retrospective, Single‐Center Study

The integration of Artificial Intelligence (AI) into healthcare has ushered in a new era of transformative advancements, addressing critical aspects such as Medical Image Analysis (MIA), Electronic Health Records (EHR), and AI-driven Chatbots. In this research paper, we present comprehensive developments in these domains, demonstrating their synergistic potential. In the realm of Medical Image Analysis, we have employed transfer learning techniques and harnessed diverse datasets, which include Pneumonia Chest X-Ray Images, and the Tuberculosis (TB) Chest X-ray Database, and the bone cancer Chest X-ray Database. Our models, built using transfer learning, have achieved remarkable accuracy, with some reaching an impressive 98.4% on the datasets collected from various sources. Notably, among the selected models, including InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, DenseNet201, ResNet50V2, ResNet101V2, VGG16, and VGG19. DenseNet201 has displayed exceptional performance, attaining an accuracy of 98.4%. The proposed work encapsulates significant advancements in AI within the healthcare domain, particularly in the fields of Medical Image Analysis, Electronic Health Records, and AI-driven Chatbots. Through rigorous experimentation and innovation, we demonstrate the potential to reshape healthcare delivery, diagnosis, and patient engagement. These developments underscore the transformative power of AI in redefining the healthcare landscape for the better.

Empowering Healthcare with AI: Advancements in Medical Image Analysis, Electronic Health Records Analysis, and AI-Driven Chatbots

Artificial intelligence (AI) has completely disrupted the entirety of the modern life. For researchers in computer science, AI experts, and professionals in a variety of fields, AI is nowadays an intriguing subject of study. However, leveraging AI techniques exposes users to potential risks as these techniques ordinarily function as black boxes, making users prey to inappropriate modeling methods as well as biased decision-making. To address this issue, the key objective of Explainable AI (XAI) frameworks is to render systems’ decisions comprehensible to the users so as to make them trustworthy and dependable. With the goal to present an extensive overview, this systematic review attempts to incorporate the majority of the XAI literature. A framework-based review of 122 articles was performed to accomplish this using the TCM-ADO framework (Theory, Context, Methods and Antecedents, Decision, Outcomes). The study further seeks to accentuate the significant gaps in the literature and offer specific recommendations for further research. These results and recommendations can be used by researchers in the fields of computer science, social sciences and others. By displacing the existing black-box AI with XAI, the integration of XAI into modern businesses has the ability to bring about social transformation by radically altering people’s perceptions of AI.

Decrypting Disruptive Technologies: Review and Research Agenda of Explainable AI as a Game Changer

Abstract Background This study aimed to investigate the effect of the time from diagnosis to breast cancer surgery on breast cancer patients’ prognosis. Methods Of the 1900 patients diagnosed with invasive (stage 1–3) breast cancer who underwent surgery in KUH between 2012 and 2019, 279 patients were enrolled in this study. All patients, including those who received neoadjuvant chemotherapy, were classified as Model 1 subjects, and those who received immediate surgical treatment were classified as Model 2 subjects. We conducted a Cox regression analysis to identify prognostic factors of breast cancer associated with the time from diagnosis to surgery. Results The univariate results indicated a sharp drop in both groups’ survival rates when the time to surgery was delayed for more than 8 weeks (Model 1 p = 0.000; Model 2 p = 0.001). In the multivariate analysis, the hazard ratio (HR) of Model 1increased (HR = 6.84, 95% CI 1.06–44.25) in response to a delay in surgery of more than 8 weeks. Smoking and the American Joint Committee on Cancer (AJCC) staging system had a negative effect on breast cancer prognosis, while hormone therapy had a positive effect. Conclusion For all patients, a delay in breast cancer surgery of more than 8 weeks was inversely associated with survival. 

/pdf/time-to-surgery-and-survival-in-breast-cancer-2br9qid4.pdf

Time to surgery and survival in breast cancer

Explainable AI (XAI) is a methodology that complements the black box of artificial intelligence, and its necessity has recently been highlighted in various fields. The purpose of this research is to identify studies in the field of pharmacovigilance using XAI. Though there have been many previous attempts to select papers, with a total of 781 papers being confirmed, only 25 of them manually met the selection criteria. This study presents an intuitive review of the potential of XAI technologies in the field of pharmacovigilance. In the included studies, clinical data, registry data, and knowledge data were used to investigate drug treatment, side effects, and interaction studies based on tree models, neural network models, and graph models. Finally, key challenges for several research issues for the use of XAI in pharmacovigilance were identified. Although artificial intelligence (AI) is actively used in drug surveillance and patient safety, gathering adverse drug reaction information, extracting drug-drug interactions, and predicting effects, XAI is not normally utilized. Therefore, the potential challenges involved in its use alongside future prospects should be continuously discussed.

https://doi.org/10.1109/access.2023.3271635

Explainable Artificial Intelligence for Patient Safety: A Review of Application in Pharmacovigilance

Explainable Machine Learning Based a Packed Red Blood Cell Transfusion Prediction and Evaluation for Major Internal Medical Condition

Objectives: In this study, we intend to propose a network analysis-based pipeline for drug repositioning using social network service data.Methods: We collected and analyzed 778 final posts on Cozaar-tab, a representative antihypertensive drug, from posts (2008-2022) of Naver Cafe, the largest social channel in Korea. For the analysis, we defined three filter dictionaries of the Cozaar-tab based on WHO-ART, an international classification system for drug side effects, and completed a network map by visualizing the extracted keywords.Results: We discussed to prepare evidence for drug repositioning from Cozaar-tab’s unexpected keyword ‘drowsiness’ to sleep inducing agent.Conclusions: Although this process is a narrow pipeline performance for a specific drug, it is expected to contribute to laying the foundation for data-based drug repositioning by supplementing it through clinical off-label review, additional data acquisition, and network analysis advancement in the future.

/pdf/a-study-on-drug-repositioning-clue-extraction-pipeline-based-h5y38ze4.pdf

A Study on Drug Repositioning Clue Extraction Pipeline Based on Network Analysis in Social Network Services

Objectives: This was a pilot study to investigate the possibility of clinical analysis to support the lack of sample size of real data and to generate synthetic data. Since real data has many limitations, such as ethical issues and costly issues, there have been many attempts to create realistic synthetic data. The focus is on whether synthetic data can be used instead of real data. Methods: This study analyzed 11,978 lung cancer patients who used anticancer drug therapy using synthetic data as a quasi-experimental study. Clinically significant variables were extracted and some tables containing patient status and treatment records were preprocessed. This experiment was applied to the propensity score matching technique to prevent the bias of covariates. Then, the preprocessed data were analyzed using Kaplan-Meier estimation and Cox proportional hazards model. Results: When plotting the survival curves, the curves from the synthetic data did not match the curves for the actual data of the other covariates. In Cohort 1, Gen I had a better 5-year OS than Gen II (S1=0.973, S2=0.953, p<0.05). Similarly, Gen I anti-cancer was better than Gen III in Cohort 2 (S1=0.990, S3=0.884, p< 0.05). In the exploratory sub-group analysis using the Cox regression model, the risk ratio was estimated. We found that Gen I had a better effect on hazard ratio than Gen II and III. However, those results were different from the actual trend. Conclusions: It was found that the analysis based on the DATA-FREE-BOX data was different from the trend of the survival analysis conducted with the real data. The trend of this analysis could be different from the real trend. It will be able to contribute to data-validation. Moreover, it is expected that the same methodology can be applied in clinical studies based on actual data by utilizing the technique used in this study.

/pdf/a-study-on-the-availability-of-survival-analysis-of-lung-3e8wym3g.pdf

A Study on the Availability of Survival Analysis of Lung Cancer Patients Using Synthetic Data

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