A fuzzy intelligent system to assess midwives' burnout conditions.

INTRODUCTION: Midwives' burnout affects their effectiveness and the quality of the services they provide to pregnant women as well as the quality of the collaboration with medical staff. The burnout depends on a number of factors that can exhibit high variability over time. This creates the necessity of introducing intelligent approaches that assess changes in behavior, environmental factors, working conditions, and to make decisions to optimize the physical and mental health of midwives. The aim of this study was to employ fuzzy logic to design a Fuzzy Intelligent or Inference System (FIS) that assesses midwives' burnout level by emulating the reasoning of human experts. METHODS: The proposed FIS addresses the assessment of midwives' burnout comprehensively since it incorporates findings following a thorough analysis of the relevant literature, as well as assimilates experts' knowledge elicited through semi-structured interviews. Additionally, fuzzy rules are more intuitive and thus easier to understand and modify by human users than dealing and translating numerical results. The FIS performance is compared and evaluated against experienced midwives. RESULTS: Findings confirm the ability of the proposed FIS to produce judgments that are closer to experts' consensus, as expressed by their aggregated assessment. CONCLUSIONS: The proposed FIS is evaluated by comparing its results with judgments made by experts, suggesting that fuzzy logic allows precise and personalized assessment of midwives' burnout levels. The proposed FIS can be used to evaluate burnout, support organizations to develop burnout policies as well as used as a research instrument to investigate interrelationships of burnout factors.

Construction and validation of a preterm birth risk assessment model using fuzzy analytic hierarchy process.

Preterm births account for almost 1 million deaths globally. The objective of this study is to develop and evaluate a model that assists clinicians in assessing the risk of preterm birth, using fuzzy multicriteria analysis. The model allows experts to incorporate their intuition and judgment into the decision-making process and takes into consideration six (6) risk dimensions reflecting the socio-economic, behavioural and medical profile of pregnant women, thus adopting a holistic approach to risk assessment. Each risk dimension is further analysed and measured in terms of risk factors associated with it. Data was collected from a selected group of 35 experts, each one with more than 20 years of obstetric experience. The model criteria were selected after a thorough literature analysis, so as to ensure a holistic approach to risk assessment. The criteria were reviewed by the experts and the model structure was finalised. The fuzzy analytic hierarchy method was applied to calculate the relative importance of each criterion and subsequent use of the model in assessing and ranking pregnant women by their preterm risk. The proposed model utilises fuzzy logic and multicriteria analysis. It addresses the multifactorial nature of decision making when assessing the preterm birth risk. It also incorporates the obstetricians' intuitive judgement during risk assessment and it can be used to classify cases based upon their risk level. Additionally, it can be applied to evaluate the risk of individual cases in a personalised manner. The proposed model is compared and validated for its predictive value against judgments made by experts.

Fuzzy Logic Intelligent Systems and Methods in Midwifery and Obstetrics.

BACKGROUND: Fuzzy logic can be used to model and manipulate imprecise and subjective knowledge imitating the human reasoning. OBJECTIVE: The aim of this systematic review was to analyze research studies pertaining to fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics. METHODS: A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only the papers that discussed fuzzy logic and fuzzy intelligent systems applications in midwifery and obstetrics were considered in this review. Selected papers were critically evaluated as for their relevance and a contextual synthesis was conducted. RESULTS: Twentynine papers were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that fuzzy logic and fuzzy intelligent systems have been successfully applied in midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc. CONCLUSION: This systematic review suggests that fuzzy logic is applicable to midwifery and obstetrics domains providing the means for developing affective intelligent systems that can assist human experts in dealing with complex diagnosis and problem solving. However, its full potential is not yet been examined, thus presenting an opportunity for further research.

Advances of Sentiment Analysis Applications in Obstetrics/Gynecology and Midwifery.

BACKGROUND: Sentiment analysis, which is also referred to as 'opinion mining' or 'emotion AI', processes natural language, analyzes text and employs computational linguistics, and biometrics to identify and analyze emotions and subjective information. Sentiment analysis is mostly applied in domains such as marketing and customer service but also in clinical medicine. Clinical medicine- related sentiment analysis has advanced recently, as more and more researchers are performing studies with the help of this valuable technique, having noticed its ability to contribute in the field. OBJECTIVE: The aim of this review was to present important facts about sentimental analysis described in deposited articles in on-line databases and the relevant articles critically appraised and a narrative synthesis conducted. METHODS: A systematic search of four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect) was performed. This review considered only quantitative, primary studies in English language, without geographical limitations, published from 2006-2021 and relevant to the objective. Searching terms were 'Sentiment analysis' AND 'Obstetrics' OR 'pregnancy', OR 'COVID' OR 'Perinatal distress' OR 'postpartum period' OR 'fetal' OR 'breast feeding' OR 'cervical'. RESULTS AND DISCUSSION: Relevant articles were critically appraised and a narrative synthesis was conducted. As a large number of studies, illustrates the use of sentiment analysis in the domain of clinical medicine, it is proved to be extremely helpful, assisting in the investigation of some highly important and even previously unexplored issues. CONCLUSION: Since pregnant women express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment. Given the vast knowledge sentiment analysis has already offered, further studies employing this technique are expected in the future.

Perinatal Inflammation: Could Partial Blocking of Cell Adhesion Molecule Function Be a Solution?

In spite of the great advances made in recent years in prenatal and perinatal medicine, inflammation can still frequently result in injury to vital organs and often constitutes a major cause of morbidity. It is today well established that in neonates-though vulnerability to infection among neonates is triggered by functional impairments in leukocyte adhesion-the decreased expression of cell adhesion molecules also decreases the inflammatory response. It is also clear that the cell adhesion molecules, namely, the integrins, selectins, and the immunoglobulin (Ig) gene super family, all play a crucial role in the inflammatory cascade. Thus, by consolidating our knowledge concerning the actions of these vital cell adhesion molecules during the prenatal period as well as regarding the genetic deficiencies of these molecules, notably leukocyte adhesion deficiency (LAD) I, II, and III, which can provoke severe clinical symptoms throughout the first year of life, it is anticipated that intervention involving blocking the function of cell adhesion molecules in neonatal leukocytes has the potential to constitute an effective therapeutic approach for inflammation. A promising perspective is the potential use of antibody therapy in preterm and term infants with perinatal inflammation and infection focusing on cases in which LAD is involved, while a further important scientific advance related to this issue could be the combination of small peptides aimed at the inhibition of cellular adhesion.

The Effect of Thyrotropin-Releasing Hormone and Antithyroid Drugs on Fetal Thyroid Function.

A euthyroid pregnant woman will normally have a fetus that displays normal fetal development. However, studies have long demonstrated the role of T3 (Triiodothyronine), T4 (Thyroxine), and TSH (Thyroid Stimulating Hormone) and their degree of penetrability into the fetal circulation. Maternal thyrotropin-releasing hormone (TRH) crosses the placental site and, from mid-gestation onward, is able to promote fetal TSH secretion. Its origin is not only hypothalamic, as was believed until recently. The maternal pancreas, and other extraneural and extrahypothalamic organs, can produce TRH variants, which are transported through the placenta affecting, to a degree, fetal thyroid function. Antithyroid drugs (ATDs) also cross the placenta and, because of their therapeutic actions, can affect fetal thyroid development, leading in some cases to adverse outcomes. Furthermore, there are a number of TRH analogues that share the same properties as the endogenous hormone. Thus, in this narrative review, we highlight the interaction of all the above with fetal growth in uncomplicated pregnancies.

Vaginal Birth After Cesarean Section (VBAC) Model using Fuzzy Analytic Hierarch Process.

BACKGROUND: There is an even more pressing need to address the issue of vaginal birth after cesarean section (VBAC), as an increase of cesarean deliveries (CDs) has been noticed. This increase however was temporarily overturned during the period 1989-1996, when some researchers tried to manifest the advantages of trial of labor after cesarean (TOLAC). Increased VBACs though raised the issue of scar ruptures, causing fear to women and guiding them once again towards the choice of cesarean deliveries. The numbers of Cesarean Sections (CS) still increase, despite the initiatives promoted by the World Health Organization (WHO) to reduce the rates of CS. OBJECTIVE: This research aims to construct two fuzzy models, the Fuzzy vaginal birth after cesarean (VBAC) model and the fuzzy CS model in order to assess the medical profile of pregnant women and to suggest the most suitable type of delivery. METHODS: A preliminary set of factors that affect VBAC or CS was identified after a comprehensive literature review. The final set of factors was used to develop a questionnaire and collect data regarding the identified factors, through a series of semi structured interviews with 29 highly experienced obstetricians. The Fuzzy Analytic Hierarchy Process (FAHP) method is utilized to develop the fuzzy VBAC and the fuzzy CS multicriteria decision models. RESULTS: A set of 36 factors in total are identified as important to consider when judging the most suitable type of delivery. Results show that a subset of 27 factors support the decision to adopt CS, with top 5 most significant to be: vaginal delivery is contraindicated, prior uterine rupture, uterine incision, extensive transfundal uterine surgery and desire for sterilization at the time of delivery. A subset of 9 factors positively affects the decision towards VBAC. The top 5 of the most important factors that positively affect the decisions to adopt VBAC are: Maternal request for VBAC, Mother's personal values/preferences, Previous bladder injury, Health care providers recommendations, Family obligations, and Prior VBAC after CS. CONCLUSIONS: The proposed approach addresses the multi-dimensional nature of judging the most appropriate type of delivery. By utilizing fuzzy logic analysis obstetricians and midwives are allowed to express their expertise and their intuition in a sound methodological approach to assess cases of pregnant women and suggest a valid route of intrapartum care.

Intelligent systems in obstetrics and midwifery: Applications of machine learning.

INTRODUCTION: Machine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics. METHODS: A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted. RESULTS: Thirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc. CONCLUSIONS: This systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.