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\"Cloud Computing Technologies for Smart Agriculture and Healthcare aims to cover the cloud management and framework. It discusses how cloud computing framework can be integrated with fog computing, edge computing, deep learning and IOT. This book will be divided in two application parts: Agriculture and Healthcare. Discusses fundamentals theories to practical and sophisticated applications of Cloud Technology for Agriculture and Healthcare Includes case studies Concepts are illustrated well with appropriate figures, tables and simple language This book is primarily aimed at graduates and researchers to understand the echo system of cloud technology for agriculture and healthcare\"-- Provided by publisher.

Intrahepatic cholestasis of pregnancy is associated with adverse perinatal outcomes, but the association with the concentration of specific biochemical markers is unclear. We aimed to quantify the adverse perinatal effects of intrahepatic cholestasis of pregnancy in women with increased serum bile acid concentrations and determine whether elevated bile acid concentrations were associated with the risk of stillbirth and preterm birth. We did a systematic review by searching PubMed, Web of Science, and Embase databases for studies published from database inception to June 1, 2018, reporting perinatal outcomes for women with intrahepatic cholestasis of pregnancy when serum bile acid concentrations were available. Inclusion criteria were studies defining intrahepatic cholestasis of pregnancy based upon pruritus and elevated serum bile acid concentrations, with or without raised liver aminotransferase concentrations. Eligible studies were case-control, cohort, and population-based studies, and randomised controlled trials, with at least 30 participants, and that reported bile acid concentrations and perinatal outcomes. Studies at potential higher risk of reporter bias were excluded, including case reports, studies not comprising cohorts, or successive cases seen in a unit; we also excluded studies with high risk of bias from groups selected (eg, a subgroup of babies with poor outcomes were explicitly excluded), conference abstracts, and Letters to the Editor without clear peer review. We also included unpublished data from two UK hospitals. We did a random effects meta-analysis to determine risk of adverse perinatal outcomes. Aggregate data for maternal and perinatal outcomes were extracted from case-control studies, and individual patient data (IPD) were requested from study authors for all types of study (as no control group was required for the IPD analysis) to assess associations between biochemical markers and adverse outcomes using logistic and stepwise logistic regression. This study is registered with PROSPERO, number CRD42017069134. We assessed 109 full-text articles, of which 23 studies were eligible for the aggregate data meta-analysis (5557 intrahepatic cholestasis of pregnancy cases and 165 136 controls), and 27 provided IPD (5269 intrahepatic cholestasis of pregnancy cases). Stillbirth occurred in 45 (0·91%) of 4936 intrahepatic cholestasis of pregnancy cases and 519 (0·32%) of 163 947 control pregnancies (odds ratio [OR] 1·46 [95% CI 0·73–2·89]; I2=59·8%). In singleton pregnancies, stillbirth was associated with maximum total bile acid concentration (area under the receiver operating characteristic curve [ROC AUC]) 0·83 [95% CI 0·74–0·92]), but not alanine aminotransferase (ROC AUC 0·46 [0·35–0·57]). For singleton pregnancies, the prevalence of stillbirth was three (0·13%; 95% CI 0·02–0·38) of 2310 intrahepatic cholestasis of pregnancy cases in women with serum total bile acids of less than 40 μmol/L versus four (0·28%; 0·08–0·72) of 1412 cases with total bile acids of 40–99 μmol/L (hazard ratio [HR] 2·35 [95% CI 0·52–10·50]; p=0·26), and versus 18 (3·44%; 2·05–5·37) of 524 cases for bile acids of 100 μmol/L or more (HR 30·50 [8·83–105·30]; p<0·0001). The risk of stillbirth is increased in women with intrahepatic cholestasis of pregnancy and singleton pregnancies when serum bile acids concentrations are of 100 μmol/L or more. Because most women with intrahepatic cholestasis of pregnancy have bile acids below this concentration, they can probably be reassured that the risk of stillbirth is similar to that of pregnant women in the general population, provided repeat bile acid testing is done until delivery. Tommy's, ICP Support, UK National Institute of Health Research, Wellcome Trust, and Genesis Research Trust.

Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice. This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

Targeted immunotherapy in acute myeloid leukemia (AML) is challenged by the lack of AML-specific target antigens and clonal heterogeneity, leading to unwanted on-target off-leukemia toxicity and risk of relapse from minor clones. We hypothesize that combinatorial targeting of AML cells can enhance therapeutic efficacy without increasing toxicity. To identify target antigen combinations specific for AML and leukemic stem cells, we generated a detailed protein expression profile based on flow cytometry of primary AML ( n  = 356) and normal bone marrow samples ( n  = 34), and a recently reported integrated normal tissue proteomic data set. We analyzed antigen expression levels of CD33, CD123, CLL1, TIM3, CD244 and CD7 on AML bulk and leukemic stem cells at initial diagnosis ( n  = 302) and relapse ( n  = 54). CD33, CD123, CLL1, TIM3 and CD244 were ubiquitously expressed on AML bulk cells at initial diagnosis and relapse, irrespective of genetic characteristics. For each analyzed target, we found additional expression in different populations of normal hematopoiesis. Analyzing the coexpression of our six targets in all dual combinations ( n  = 15), we found CD33/TIM3 and CLL1/TIM3 to be highly positive in AML compared with normal hematopoiesis and non-hematopoietic tissues. Our findings indicate that combinatorial targeting of CD33/TIM3 or CLL1/TIM3 may enhance therapeutic efficacy without aggravating toxicity in immunotherapy of AML.

Background Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver illness with a genetically heterogeneous background that can be accompanied by considerable morbidity and attendant health care costs. The pathogenesis and progression of NAFLD is complex with many unanswered questions. We conducted genome-wide association studies (GWASs) using both adult and pediatric participants from the Electronic Medical Records and Genomics (eMERGE) Network to identify novel genetic contributors to this condition. Methods First, a natural language processing (NLP) algorithm was developed, tested, and deployed at each site to identify 1106 NAFLD cases and 8571 controls and histological data from liver tissue in 235 available participants. These include 1242 pediatric participants (396 cases, 846 controls). The algorithm included billing codes, text queries, laboratory values, and medication records. Next, GWASs were performed on NAFLD cases and controls and case-only analyses using histologic scores and liver function tests adjusting for age, sex, site, ancestry, PC, and body mass index (BMI). Results Consistent with previous results, a robust association was detected for the PNPLA3 gene cluster in participants with European ancestry. At the PNPLA3-SAMM50 region, three SNPs, rs738409, rs738408, and rs3747207, showed strongest association (best SNP rs738409 p  = 1.70 × 10 − 20 ). This effect was consistent in both pediatric ( p  = 9.92 × 10 − 6 ) and adult ( p  = 9.73 × 10 − 15 ) cohorts. Additionally, this variant was also associated with disease severity and NAFLD Activity Score (NAS) ( p  = 3.94 × 10 − 8 , beta = 0.85). PheWAS analysis link this locus to a spectrum of liver diseases beyond NAFLD with a novel negative correlation with gout ( p  = 1.09 × 10 − 4 ). We also identified novel loci for NAFLD disease severity, including one novel locus for NAS score near IL17RA (rs5748926, p  = 3.80 × 10 − 8 ), and another near ZFP90-CDH1 for fibrosis (rs698718, p  = 2.74 × 10 − 11 ). Post-GWAS and gene-based analyses identified more than 300 genes that were used for functional and pathway enrichment analyses. Conclusions In summary, this study demonstrates clear confirmation of a previously described NAFLD risk locus and several novel associations. Further collaborative studies including an ethnically diverse population with well-characterized liver histologic features of NAFLD are needed to further validate the novel findings.

Advanced maternal age (AMA; ≥35 years) is an increasing trend and is reported to be associated with various pregnancy complications. To determine the risk of stillbirth and other adverse pregnancy outcomes in women of AMA. Embase, Medline (Ovid), Cochrane Database of Systematic Reviews, ClinicalTrials.gov, LILACS and conference proceedings were searched from ≥2000. Cohort and case-control studies reporting data on one or more co-primary outcomes (stillbirth or fetal growth restriction (FGR)) and/or secondary outcomes in mothers ≥35 years and <35 years. The effect of age on pregnancy outcome was investigated by random effects meta-analysis and meta-regression. Stillbirth rates were correlated to rates of maternal diabetes, obesity, hypertension and use of assisted reproductive therapies (ART). Out of 1940 identified titles; 63 cohort studies and 12 case-control studies were included in the meta-analysis. AMA increased the risk of stillbirth (OR 1.75, 95%CI 1.62 to 1.89) with a population attributable risk of 4.7%. Similar trends were seen for risks of FGR, neonatal death, NICU unit admission restriction and GDM. The relationship between AMA and stillbirth was not related to maternal morbidity or ART. Stillbirth risk increases with increasing maternal age. This is not wholly explained by maternal co-morbidities and use of ART. We propose that placental dysfunction may mediate adverse pregnancy outcome in AMA. Further prospective studies are needed to directly test this hypothesis.

In this analysis of data from several large cohorts of patients with diabetes, antidiabetic incretin-based drugs were not associated with an increased risk of hospitalization for heart failure, as compared with commonly used combinations of oral antidiabetic drugs. The safety of incretin-based drugs, which include dipeptidyl peptidase 4 (DPP-4) inhibitors and glucagon-like peptide 1 (GLP-1) analogues, is controversial. Although much attention has been focused on adverse pancreatic events, there are new concerns about an increased risk of heart failure. 1 Indeed, in the Saxagliptin Assessment of Vascular Outcomes Recorded in Patients with Diabetes Mellitus–Thrombolysis in Myocardial Infarction 53 (SAVOR-TIMI 53) trial, 2 , 3 patients who were randomly assigned to the DPP-4 inhibitor saxagliptin had a 27% increase in the risk of hospitalization for heart failure as compared with those who received placebo. In contrast, the Examination of Cardiovascular Outcomes with . . .

To evaluate the design characteristics of studies that evaluated the performance of artificial intelligence (AI) algorithms for the diagnostic analysis of medical images. PubMed MEDLINE and Embase databases were searched to identify original research articles published between January 1, 2018 and August 17, 2018 that investigated the performance of AI algorithms that analyze medical images to provide diagnostic decisions. Eligible articles were evaluated to determine 1) whether the study used external validation rather than internal validation, and in case of external validation, whether the data for validation were collected, 2) with diagnostic cohort design instead of diagnostic case-control design, 3) from multiple institutions, and 4) in a prospective manner. These are fundamental methodologic features recommended for clinical validation of AI performance in real-world practice. The studies that fulfilled the above criteria were identified. We classified the publishing journals into medical vs. non-medical journal groups. Then, the results were compared between medical and non-medical journals. Of 516 eligible published studies, only 6% (31 studies) performed external validation. None of the 31 studies adopted all three design features: diagnostic cohort design, the inclusion of multiple institutions, and prospective data collection for external validation. No significant difference was found between medical and non-medical journals. Nearly all of the studies published in the study period that evaluated the performance of AI algorithms for diagnostic analysis of medical images were designed as proof-of-concept technical feasibility studies and did not have the design features that are recommended for robust validation of the real-world clinical performance of AI algorithms.

Objectives To evaluate the implementation and adoption of the NHS detailed care records service in “early adopter” hospitals in England.Design Theoretically informed, longitudinal qualitative evaluation based on case studies.Setting 12 “early adopter” NHS acute hospitals and specialist care settings studied over two and a half years.Data sources Data were collected through in depth interviews, observations, and relevant documents relating directly to case study sites and to wider national developments that were perceived to impact on the implementation strategy. Data were thematically analysed, initially within and then across cases. The dataset consisted of 431 semistructured interviews with key stakeholders, including hospital staff, developers, and governmental stakeholders; 590 hours of observations of strategic meetings and use of the software in context; 334 sets of notes from observations, researchers’ field notes, and notes from national conferences; 809 NHS documents; and 58 regional and national documents.Results Implementation has proceeded more slowly, with a narrower scope and substantially less clinical functionality than was originally planned. The national strategy had considerable local consequences (summarised under five key themes), and wider national developments impacted heavily on implementation and adoption. More specifically, delays related to unrealistic expectations about the capabilities of systems; the time needed to build, configure, and customise the software; the work needed to ensure that systems were supporting provision of care; and the needs of end users for training and support. Other factors hampering progress included the changing milieu of NHS policy and priorities; repeatedly renegotiated national contracts; different stages of development of diverse NHS care records service systems; and a complex communication process between different stakeholders, along with contractual arrangements that largely excluded NHS providers. There was early evidence that deploying systems resulted in important learning within and between organisations and the development of relevant competencies within NHS hospitals.Conclusions Implementation of the NHS Care Records Service in “early adopter” sites proved time consuming and challenging, with as yet limited discernible benefits for clinicians and no clear advantages for patients. Although our results might not be directly transferable to later adopting sites because the functionalities we evaluated were new and untried in the English context, they shed light on the processes involved in implementing major new systems. The move to increased local decision making that we advocated based on our interim analysis has been pursued and welcomed by the NHS, but it is important that policymakers do not lose sight of the overall goal of an integrated interoperable solution.