The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows.

Aim: To ascertain, placenta previa, acreta and per acreta by ultrasonography during antenatal visit of pregnant mother in 3rd trimester with history of risk factors smoking active passive, hypertension previous history of surgery with consents. Design: cross sectional. Duration: December 2020 to May 2022 (one year & Six Months). Site: Radiology, Gynae & Obstetric department of Pak Red Crescent Medical & Dental College and teaching hospital Dina Nath Multan Road Kasur. Method: convenient sampling with written consent for participation in study. Pregnant mother in 3rd trimester were examined with ultrasonography and color Doppler. Keywords: Abnormal placement of placenta, Risk factors, Ultrasonography.

ObjectivesThe aim of this study was to analyse the current use, identify challenges and barriers and propose a way forward for the use of the pager devices in the in-hospital communications.MethodsInitially, 447 studies were identified through database searching. After checking against the eligibility, 39 studies were included. Full-text records were retrieved and reviewed by two authors. After excluding unrelated studies and duplicate records, a total of 12 articles were selected for the final review.ResultsThe use of pagers often lacks standardisation, content, format, urgency level, and clarity within the message. Some studies reported that medical staff preferred in-person interactions with consults instead of communicating over the phone or pagers. Productive communication can reduce the turnaround time by up to 50%. The key challenges are; (1) data security and privacy, (2) timely acknowledgement of received communication, (3) lack of two-way communications causing issues in critical care situations and (4) there is no standard process for the in-hospital communications.ConclusionWe found that the clinicians’ age, experience, speciality and preferences greatly matter and influence the selection of tools and technology in healthcare. With revolutionary advances in technology, smartphones have inevitably become beneficial to healthcare, owing to multiple instant messaging applications (apps) that can streamline encrypted clinical communication between medical teams and could be safely used for in-hospital communications.

The number of spikes, spikelets per spike, number of spikes per square meter are essential metrics for plant breeders and researchers in predicting wheat crop yield. Evaluating the crop yield based on wheat ears counting is still done manually, which is a labor-intensive, tedious and costly task. Thus, there is a significant need to develop a real-time wheat spikes/ears counting system for plant breeders for effective and efficient crop yield predictions. This paper proposed two deep learning-based methods based on EfficientDet and Faster-RCNN to detect and count the spikes. The images are taken using high-throughput phenotyping techniques under natural field conditions, and the algorithms localize and automatically count wheat spikes/ears. Faster R-CNN with Resnet50 as backbone architecture produced an overall accuracy of 88.7% on the test images. We also used recent stateof- the-art models EfficientDet-D5 and EfficientDet-D7, having backbone architectures EfficientNet-B5 and EfficientNet- B7, respectively. A comprehensive quantitative analysis is performed on the standard performance metrics. In the analysis, the EfficientDet-D5 model produces an accuracy of 92.7% on the test images, and EfficientDet-D7 produces an accuracy of 93.6%.

We performed FMEA on the existing RRS with the help of routine users of the RRS who acted as subject matter experts and evaluated the failures for their criticality using the Risk Priority Number approach based on their experience of the RRS. The FMEA found 35 potential failure modes and 101 failure mode effects across 13 process steps of the RRS. The afferent limb of RRS was found to be more prone to these failures (62, 61.4%) than the efferent limb of the RRS (39, 38.6%). Modification of calling criteria (12, 11.9%) and calculation of New Zealand Early Warning Scores (NZEWS) calculation (11, 10.9%) steps were found to potentially give rise to the highest number of these failures. Causes of these failures include human error and related factors (35, 34.7%), staff workload/staffing levels (30, 29.7%) and limitations due to paper-based charts and organisational factors (n = 30, 29.7%). The demonstrated electronic system was found to potentially eliminate or reduce the likelihood of 71 (70.2%) failures. The failures not eliminated by the electronic RRS require targeted corrective measures including scenario-based training and education, and revised calling criteria to include triggers for hypothermia and high systolic blood pressure.

Background: Children with Cerebral Palsypatient have difficulties in performing their ADL’ s. Many factors effects on ADL’ s independence including impairment of upper limb function and fine motor skills. Effect of disability changes over time and severity ranges from mild to severe so there was the need of such study that might help occupational therapist in planning those activities which will make the patient independent in ADL’ s Objective: To find the frequency of problems in daily livings activities in children with cerebral palsy. Methods: It is a Cross-sectional Study conducted in the Department of Developmental & Behavioral pediatrics, The Children’s Hospital and The Institute of Child Health, Lahore, Rising Sun Institute for special children and Hospital of Pakistan Society for Rehabilitation of the disabled (PSRD), Lahore. The study sample was 50 diagnosed Children with Cerebral Palsy (CP) with Activities of daily Livings (ADL’s) problems sampling technique was used for sampling. The Cerebral Palsy (CP) patients were interviewed using Barthel Index Activities of daily livings (ADL’s) scale. Results: This research showed in bowels 52% patient were incontinent whereas in bladder 50% patient incontinent. Grooming skill showed that ability of patient to wear dresses was absent in 50% patient, 34% patient help and 16% patient were independent. Ability of patient to sit on toilet seat was absent in 50% patient, 26% patient need help and 24% patient were independent. Feeding problems showed that 46% Cerebral Palsy (CP) patient have feeding problems, 34% patient needs help in cutting and 20% were independent. In mobility 42% patient were unable to move and 12% patient required major, 20% patient required minor help and 26% patient were independent. Conclusion: The conclusion of the study is cerebral palsy patients have problems in Activities of daily livings (ADL’s). Severe cerebral palsy type has more problems as compared to mild cerebral palsy. Effects of disability often changes over time and severity ranges from mild to severe. These problems mostly due to motor and sensory impairments. Keywords: Activities of Daily Livings, Cerebral Palsy, Gross Motor Function., Selective Dorsal Rhizotomy

Aims and objectives: This study investigated clinical staff perceptions of learning about current monitoring practices and the planned introduction of an electronic system for patient monitoring. The aim of this research was to evaluate the perceptions of clinical staff (nurses and doctors) about the perceived strengths and weaknesses of the current state of the rapid response system (RRS) and how those strengths and weakness would be affected by introducing an electronic RRS.Methods: This research applied a descriptive study methodology. Two detailed sessions on demonstration on the electronic RRS for measuring and recording vital signs and the electronic Early Warning System (EWS) were followed by two structured surveys administered through an online portal (SurveyMonkey) for nurses and doctors working at Taranaki District Health Board. The study was planned and conducted between October 2020 and May 2021 at Taranaki Base Hospital, New Plymouth, New Zealand.Results: We found that the perceptions of clinical staff were a combination of key practice issues with current manual monitoring, expectations of improved visibility of vital sign charts, better communication between staff and thus improved patient care with the introduction of an electronic system. A majority (24, 60%) of nurses reported that, when called to assess deteriorating patients, the responders arrive at bedside within 5-30 min and an additional 11 (27%) said the responders arrive within 5 min. That is a collective 87% responder arrival within 30 minConclusion: Staff believe that an electronic RRS could improve communication, speed up decision making and have a positive impact on patient outcomes.

Chapter Contents: 7.1 Introduction 7.2 Overview of wearable sensor and IoT systems for older adults with long-term conditions 7.3 System overview and challenges 7.4 Evaluations and results 7.5 Discussion, challenges and conclusions Acknowledgments Conflict of interest statement References

Background Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. Objectives This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. Methods We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. Results The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. Conclusion We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.

The objective of this study was to design and develop a predictive model for 30-day risk of hospital readmission using machine learning techniques. The proposed predictive model was then validated with the two most commonly used risk of readmission models: LACE index and patient at risk of hospital readmission (PARR). The study cohort consisted of 180,118 admissions with 22,565 (12.5%) of actual readmissions within 30 days of hospital discharge, from 01 Jan 2015 to 31 Dec 2016 from two Auckland-region hospitals. We developed a machine learning model to predict 30-day readmissions using the model types XGBoost, Random Forests, and Adaboost with decision stumps as a base learner with different feature combinations and preprocessing procedures. The proposed model achieved the F1-score (0.386 ± 0.006), sensitivity (0.598 ± 0.013), positive predictive value (PPV) (0.285 ± 0.004), and negative predictive value (NPV) (0.932 ± 0.002). When compared with LACE and PARR(NZ) models, the proposed model achieved better F1-score by 12.7% compared with LACE and 23.2% compared with PARR(NZ). The mean sensitivity of the proposed model was 6.0% higher than LACE and 41% higher than PARR(NZ). The mean PPV was 15.9% and 14.6% higher than LACE and PARR(NZ) respectively. We presented an all-cause predictive model for 30-day risk of hospital readmission with an area under the receiver operating characteristics (AUROC) of 0.75 for the entire dataset.

Early detection of clinical deterioration and timely escalation are critical for improving patient safety and preventing subsequent events in hospitals/acute care settings. This paper reviewed existing Rapid Response Systems (RRS) and their applications that incorporate Early Warning Score (EWS) for escalating patient deterioration and rapid response. We identified and analysed response applications and key factors impacting on ward nurses' ability to recognise and respond to patients deterioration. The literature reports comprise of adoption of various EWS protocols, communication policies, and educational strategies to the specialised healthcare team that have been introduced in the acute care settings to improve patient deterioration workflow. It was found that there are gaps and limitations at different stages of RRS from vital signs collection to escalation and response. Currently, there is no single solution available for clinicians to adopt an efficient escalation workflow. Therefore, there is a clear need for a connected, integrated, user-friendly and effective solution for collection of vital signs to timely escalation and response. Finally, we discussed key gaps and limitations of RRS with future direction and recommendations.

One of the most prominent statistical distributions is the Weibull distribution. The recent modifications in this distribution have enhanced its application but only in specific fields. To introduce a more generalized Weibull distribution, in this work beta exponentiated modified Weibull distribution is established. This distribution consolidate the exponential, skewed and symmetric shapes into one density. The proposed distribution also contains nineteen lifetime distributions as a special case, which shows the flexibility of the distribution. The statistical properties of the proposed model are derived and discussed, including reliability analysis and order statistics. The hazard function of the proposed distribution can have a unimodal, decreasing, bathtub, upside-down bathtub, and increasing shape that make it effective in reliability analysis. The parameters of the proposed model are evaluated by maximum likelihood and least squares estimation methods. The significance of the beta exponentiated modified Weibull distribution for modeling is illustrated by the study of real data. The numerical study indicates that the new proposed distribution gives better results than other comparable distributions.

The objective of this study was to design and develop a 30-day risk of hospital readmission predictive model using machine learning techniques. The proposed risk of readmission predictive model was then validated with the two most commonly used risk of readmission models - LACE index and patient at-risk of hospital readmission (PARR). The study cohort consisted of 180,118 admissions with 22565 (12.5%) of actual readmissions within 30-day of hospital discharge, from 01 Jan 2015 to 31 Dec 2016 from two Auckland-region hospitals. We developed a machine learning model to predict 30-day readmissions using the model types: XGBoost, Random Forests and Adaboost with decision stumps as a base learner with different feature combinations and preprocessing procedures. The proposed model achieved the F1-score (0.386 ± 0.006), sensitivity (0.598 ± 0.013), positive predictive value (PPV) (0.285 ± 0.004) and negative predictive value (NPV) (0.932 ± 0.002). When compared with LACE and PARR (NZ) models, the proposed model achieved better F1-score by 12.5% compared to LACE and 22.9% compared to PARR (NZ). The mean sensitivity of the proposed model was 6.0% higher than LACE and 42.4% higher than PARR (NZ). The mean PPV was 15.9% and 13.5% higher than LACE and PARR (NZ) respectively.

We developed a machine learning model to predict 30-day readmissions using the model types; XGBoost, Random Forests and Adaboost with decision stumps as a base learner with different feature combinations and preprocessing procedures. The proposed model achieved the F1-score (0.386 ± 0.006), sensitivity (0.598 ± 0.013), positive predictive value (PPV) (0.285 ± 0.004) and negative predictive value (NPV) (0.932 ± 0.002). When compared with LACE and PARR (NZ) models, the proposed model achieved better F1-score by 12.5% compared to LACE and 22.9% compared to PARR (NZ). The mean sensitivity of the proposed model was 6.0% higher than LACE and 42.4% higher than PARR (NZ). The mean PPV was 15.9% and 13.5% higher than LACE and PARR (NZ) respectively.

Background: Mandibular defects may result from trauma, infections, cancer ablation or radiation necrosis. These defects may vary according to the content and nature of the tissues that require reconstruction. The objective of this study was to evaluate the outcome of vascularised free fibula flap for mandibular reconstruction in terms of flap success rate and complications.Material and Methods: This prospective clinical study was carried out at Department of Plastic Surgery, Hayatabad Medical Complex, Peshawar from January 2014 to December 2018. The study included patients of either gender who underwent mandibular reconstruction with free fibula oseteocutaneous flap during the study period. Diabetic patients with underlying vascular pathologies and cachectic patients were excluded from the study.Results: There were 56 patients with 38 males and 18 females. Their age ranged from 24-66 years with a mean age of 36 years. The mandibular defects resulted from various etiologies and included: Squamous cell carcinoma (n=27, 48.21%), Giant cell granuloma (n=3; 5.35%), Ameloblastoma (n=2; 3.57%), Road traffic accidents (n=10; 17.85%), Firearm injury (n=9; 16%), Bomb blast injury (n=3; 5.35%) and osteoradionecrosis (n=2; 3.57%). Primary reconstruction of the mandibular defects was performed in 44 patients, whereas delayed reconstruction was performed in 12 patients. Out of the 56 flaps, 49 flaps survived completely. Our share of complications was as follows: Wound infections (n=13; 23.21%), Skin graft loss at donor site (n=5; 8.95%), Complete flap loss (n=4; 7.14%), Orocutaneous fistulae (n=3; 5.35%), Ankle instability (n=2; 3.57%), Skin paddle necrosis (n=1; 1.78%) and sensory deficit distal to donor site (n=1; 1.78%).Conclusions: Microvascular free fibula is a reliable tool for mandibular reconstruction following tumor resection or trauma. The flap has a high success rate and relatively fewer complications.

Motivation: Imputation of untyped SNPs has become important in Genome-wide Association Studies (GWAS). There has also been a trend towards analyzing rare variants, driven by the decrease of genome sequencing costs. Rare variants are enriched in pedigrees that have many cases or extreme phenotypes. This is especially the case for large pedigrees, which makes family-based designs ideal to detect rare variants associated with complex traits. The costs of performing relatively large family-based GWAS can be significantly reduced by fully sequencing only a fraction of the pedigree and performing imputation on the remaining subjects. The program GIGI can efficiently perform imputation in large pedigrees but can be time consuming. Here, we implement GIGIu0027s imputation approach in a new program, GIGI2, which performs imputation with computational time reduced by at least 25x on one thread and 120x on eight threads. The memory usage of GIGI2 is reduced by at least 30x. This reduction is achieved by implementing better memory layout and a better algorithm for solving the Identity by Descent graphs, as well as with additional features, including multithreading. We also make GIGI2 available as a webserver based on the same framework as the Michigan Imputation Server. Availability: GIGI2 is freely available online at https://cse-git.qcri.org/eullah/GIGI2 and the websever is at https://imputation.qcri.org/

Cardiac signals especially, electrocardiogram (ECG) becomes more vulnerable to external noises as compared to other biomedical signal and this is due to the non-invasive nature of ECG and the environment in which it is being recorded. Among other external noises, power line interference (PLI) is the most disquieting noise. This paper proposes an adaptive noise canceller (ANC) based on State Space Least Mean Square (SSLMS) for removal of PLI. Nevertheless, SSLMS is employed for adaptively tracking of amplitude, phase of known frequency of PLI sinusoids. Moreover, SSLMS is a model-dependent recursive algorithm whose convergence rate and tracking capability increase in instinctive manner when system's a priori knowledge is provided. Furthermore, the proposed algorithm does not require any reference, hence can effectively be applied for real time scenario. The purposed SSLMS adaptive filter performs effectually even when signal to noise ratio of contaminated ECG signal is low. The results show that proposed algorithm successfully removes PLI of 50 Hz frequency.