The effects of treatment as usual versus a computerized clinical decision aid on shared decision-making in the treatment of psychotic disorders

Background and objectives People with psychotic disorders can experience a lack of active involvement in their decisional process. Clinical decision aids are shared decision-making tools which are currently rarely used in mental healthcare. We examined the effects of Treatment E-Assist (TREAT), a computerized clinical decision aid in psychosis care, on shared decision-making and satisfaction with consultations as assessed by patients. Methods A total of 187 patients with a psychotic disorder participated. They received either treatment as usual in the first phase (TAU1), TREAT in the second phase or treatment as usual in the third phase of the trial (TAU2). The Decisional Conflict Scale was used as primary outcome measure for shared decision-making and patient satisfaction as secondary outcome. Results A linear mixed model analysis found no significant effects between TAU 1 (β = −0.54, SE = 2.01, p = 0.80) and TAU 2 (β = −1.66, SE = 2.63, p = 0.53) compared to TREAT on shared decision-making. High patient rated satisfaction with the consultations was found with no significant differences between TAU 1 (β = 1.48, SE = 1.14, p = 0.20) and TAU 2 (β = 2.26, SE = 1.33, p = 0.09) compared to TREAT. Conclusion We expected TREAT to enhance shared decision-making without decreasing satisfaction with consultations. However, no significant differences on shared decision-making or satisfaction with consultations were found. Our findings suggest that TREAT is safe to implement in psychosis care, but more research is needed to fully understand its effects on the decisional process. (AU)

The changing face of Capacity legislation in Ireland: algorithms for clinicians.

Capacity legislation in Ireland is evolving. The Assisted Decision-Making (Capacity) Act 2015 has been passed into law, but its main provisions are yet to be commenced. This paper compares the law and its practical implications currently and under the new legislation. Quick reference algorithms for frontline clinicians are proposed.

Decision-Making Algorithm for Advanced Excisional Body Contouring: Dynamic Definition Solutions for Skin Laxity.

BACKGROUND: Excisional body contour surgery is the cornerstone treatment for skin laxity. Decision-making can be challenging when selecting the procedure. Dynamic definition liposculpture allows the surgeon to carve the underlying anatomy and provide more natural results, in which umbilical shape and position play a crucial role. The authors describe their experience using a decision-making algorithm as a tool to ease surgical planning for advanced excisional body contouring. METHODS: Following the algorithm designed by the senior author regarding excisional body contouring procedures, the authors searched their database for patients who were classified according to skin laxity and navel location to undergo one of the following procedures: mixed technologies plus umbilical mobilization, mixed technologies plus sliding mini-abdominoplasty, mini-tummy tuck with muscular plication, full abdominoplasty, reverse bridge abdominoplasty, or reverse full abdominoplasty. RESULTS: A total of 563 women were consecutively operated on from February of 2014 to January of 2020. The six-procedure model algorithm helped the authors achieve very good results with low complication rates in patients with some grade of abdominal skin laxity. Most complications were reported as minor (9.6 percent). Major complications (3.9 percent) included three localized infections, four abnormal skin retractions, two cases of skin flap necrosis, and 13 cases of postoperative anemia. CONCLUSIONS: This algorithm helped the authors choose the best excisional technique based on patients' anatomical features by following skin geometry to enhance aesthetic outcomes. Further studies are needed to support the algorithm validation and aesthetic outcomes. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.

Distributed application of guideline-based decision support through mobile devices: Implementation and evaluation.

BACKGROUND: Traditionally guideline (GL)-based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers, rather than to patients at home. However, managing patients at home is often preferable, reducing costs and empowering patients. Thus, we wanted to explore an option in which patients, in particular chronic patients, might be assisted by a local DSS, which interacts as needed with the central DSS engine, to manage their disease outside the standard clinical settings. OBJECTIVES: To design, implement, and demonstrate the technical and clinical feasibility of a new architecture for a distributed DSS that provides patients with evidence-based guidance, offered through applications running on the patients' mobile devices, monitoring and reacting to changes in the patient's personal environment, and providing the patients with appropriate GL-based alerts and personalized recommendations; and increase the overall robustness of the distributed application of the GL. METHODS: We have designed and implemented a novel projection-callback (PCB) model, in which small portions of the evidence-based guideline's procedural knowledge are projected from a projection engine within the central DSS server, to a local DSS that resides on each patient's mobile device. The local DSS applies the knowledge using the mobile device's local resources. The GL projections generated by the projection engine are adapted to the patient's previously defined preferences and, implicitly, to the patient's current context, in a manner that is embodied in the projected therapy plans. When appropriate, as defined by a temporal pattern within the projected plan, the local DSS calls back the central DSS, requesting further assistance, possibly another projection. To support the new model, the initial specification of the GL includes two levels: one for the central DSS, and one for the local DSS. We have implemented a distributed GL-based DSS using the projection-callback model within the MobiGuide EU project, which automatically manages chronic patients at home using sensors on the patients and their mobile phone. We assessed the new GL specification process, by specifying two very different, complex GLs: for Gestational Diabetes Mellitus, and for Atrial Fibrillation. Then, we evaluated the new computational architecture by applying the two GLs to the automated clinical management, at real time, of patients in two different countries: Spain and Italy, respectively. RESULTS: The specification using the new projection-callback model was found to be quite feasible. We found significant differences between the distributed versions of the two GLs, suggesting further research directions and possibly additional ways to analyze and characterize GLs. Applying the two GLs to the two patient populations proved highly feasible as well. The mean time between the central and local interactions was quite different for the two GLs: 3.95 ± 1.95 days in the case of the gestational diabetes domain, and 23.80 ± 12.47 days, in the case of the atrial fibrillation domain, probably corresponding to the difference in the distributed specifications of the two GLs. Most of the interaction types were due to projections to the local DSS (83%); others were data notifications, mostly to change context (17%). Some of the data notifications were triggered due to technical errors. The robustness of the distributed architecture was demonstrated through the successful recovery from multiple crashes of the local DSS. CONCLUSIONS: The new projection-callback model has been demonstrated to be feasible, from specification to distributed application. Different GLs might significantly differ, however, in their distributed specification and application characteristics. Distributed medical DSSs can facilitate the remote management of chronic patients by enabling the central DSSs to delegate, in a dynamic fashion, determined by the patient's context, much of the monitoring and treatment management decisions to the mobile device. Patients can be kept in their home environment, while still maintaining, through the projection-callback mechanism, several of the advantages of a central DSS, such as access to the patient's longitudinal record, and to an up-to-date evidence-based GL repository.

Gauging the Impact of Artificial Intelligence and Mathematical Modeling in Response to the COVID-19 Pandemic: A Systematic Review.

While the world continues to grapple with the devastating effects of the SARS-nCoV-2 virus, different scientific groups, including researchers from different parts of the world, are trying to collaborate to discover solutions to prevent the spread of the COVID-19 virus permanently. Henceforth, the current study envisions the analysis of predictive models that employ machine learning techniques and mathematical modeling to mitigate the spread of COVID-19. A systematic literature review (SLR) has been conducted, wherein a search into different databases, viz., PubMed and IEEE Explore, fetched 1178 records initially. From an initial of 1178 records, only 50 articles were analyzed completely. Around (64%) of the studies employed data-driven mathematical models, whereas only (26%) used machine learning models. Hybrid and ARIMA models constituted about (5%) and (3%) of the selected articles. Various Quality Evaluation Metrics (QEM), including accuracy, precision, specificity, sensitivity, Brier-score, F1-score, RMSE, AUC, and prediction and validation cohort, were used to gauge the effectiveness of the studied models. The study also considered the impact of Pfizer-BioNTech (BNT162b2), AstraZeneca (ChAd0x1), and Moderna (mRNA-1273) on Beta (B.1.1.7) and Delta (B.1.617.2) viral variants and the impact of administering booster doses given the evolution of viral variants of the virus.

A knowledge graph to interpret clinical proteomics data.

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.

Novel tech throws knock-out punch to ECG improving GP referral decisions to cardiology.

PURPOSE: In a comparator study, designed with assistance from the Food and Drug Administration, a State-of-the-Art (SOTA) ECG device augmented with automated analysis, the comparator, was compared with a breakthrough technology, Cardio-HART (CHART). METHODS: The referral decision defined by physician reading biosignal-based ECG or CHART report were compared for 550 patients, where its performance is calculated against the ground truth referral decision. The ground truth was established by cardiologist consensus based on all the available measurements and findings including echocardiography (ECHO). RESULTS: The results confirmed that CHART analysis was far more effective than ECG only analysis: CHART reduced false negative rates 15.8% and false positive (FP) rates by 5%, when compared with SOTA ECG devices. General physicians (GP's) using CHART saw their positive diagnosis rate significantly increased, from ~10% to ~26% (260% increase), and the uncertainty rate significantly decreased, from ~31% to ~1.9% (94% decrease). For cardiology, the study showed that in 98% of the cases, the CHART report was found to be a good indicator as to what kind of heart problems can be expected (the 'start-point') in the ECHO examination. CONCLUSIONS: The study revealed that GP use of CHART resulted in more accurate referrals for cardiology, resulting in fewer true negative or FP-healthy or mildly abnormal patients not in need of ECHO confirmation. The indirect benefit is the reduction in wait-times and in unnecessary and costly testing in secondary care. Moreover, when used as a start-point, CHART can shorten the echocardiograph examination time.

Personalized next-best action recommendation with multi-party interaction learning for automated decision-making.

Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart's actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer's historical and current states, responses to decision-makers' actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.

Safety control architecture for ventricular assist devices

ABSTRACT: In patients with severe heart disease, the implantation of a ventricular assist device (VAD) may be necessary, especially in patients with an indication for heart transplantation. For this, the Institute Dante Pazzanese of Cardiology (IDPC) has developed an implantable centrifugal blood pump that will be able to help a diseased human heart to maintain physiological blood flow and pressure. This device will be used as a totally or partially implantable VAD. Therefore, performance assurance and correct specification of the VAD are important factors in achieving a safe interaction between the device and the patient's behavior or condition. Even with reliable devices, some failures may occur if the pumping control does not keep up with changes in the patient's behavior or condition. If the VAD control system has no fault tolerance and no system dynamic adaptation that occurs according to changes in the patient's cardiovascular system, a number of limitations can be observed in the results and effectiveness of these devices, especially in patients with acute comorbidities. This work proposes the application of a mechatronic approach to this class of devices based on advanced control, instrumentation, and automation techniques to define a method to develop a hierarchical supervisory control system capable of dynamically, automatically, and safely VAD control. For this methodology, concepts based on Bayesian networks (BN) were used to diagnose the patient's cardiovascular system conditions, Petri nets (PN) to generate the VAD control algorithm, and safety instrumented systems to ensure the safety of the VAD system.

Combination treatment optimization using a pan-cancer pathway model.

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans-that is, optimized sequences of potentially different drug combinations-providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available.

Artificial intelligence in orthopaedics: A scoping review.

There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946-2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.

Computerized history-taking improves data quality for clinical decision-making-Comparison of EHR and computer-acquired history data in patients with chest pain.

Patients' medical histories are the salient dataset for diagnosis. Prior work shows consistently, however, that medical history-taking by physicians generally is incomplete and not accurate. Such findings suggest that methods to improve the completeness and accuracy of medical history data could have clinical value. We address this issue with expert system software to enable automated history-taking by computers interacting directly with patients, i.e. computerized history-taking (CHT). Here we compare the completeness and accuracy of medical history data collected and recorded by physicians in electronic health records (EHR) with data collected by CHT for patients presenting to an emergency room with acute chest pain. Physician history-taking and CHT occurred at the same ED visit for all patients. CHT almost always preceded examination by a physician. Data fields analyzed were relevant to the differential diagnosis of chest pain and comprised information obtainable only by interviewing patients. Measures of data quality were completeness and consistency of negative and positive findings in EHR as compared with CHT datasets. Data significant for the differential of chest pain was missing randomly in all EHRs across all data items analyzed so that the dimensionality of EHR data was limited. CHT files were near complete for all data elements reviewed. Separate from the incompleteness of EHR data, there were frequent factual inconsistencies between EHR and CHT data across all data elements. EHR data did not contain representations of symptoms that were consistent with those reported by patients during CHT. Trial registration: This study is registered at https://www.clinicaltrials.gov (unique identifier: NCT03439449).

To maximize or randomize? An experimental study of probability matching in financial decision making.

Probability matching, also known as the "matching law" or Herrnstein's Law, has long puzzled economists and psychologists because of its apparent inconsistency with basic self-interest. We conduct an experiment with real monetary payoffs in which each participant plays a computer game to guess the outcome of a binary lottery. In addition to finding strong evidence for probability matching, we document different tendencies towards randomization in different payoff environments-as predicted by models of the evolutionary origin of probability matching-after controlling for a wide range of demographic and socioeconomic variables. We also find several individual differences in the tendency to maximize or randomize, correlated with wealth and other socioeconomic factors. In particular, subjects who have taken probability and statistics classes and those who self-reported finding a pattern in the game are found to have randomized more, contrary to the common wisdom that those with better understanding of probabilistic reasoning are more likely to be rational economic maximizers. Our results provide experimental evidence that individuals-even those with experience in probability and investing-engage in randomized behavior and probability matching, underscoring the role of the environment as a driver of behavioral anomalies.

Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma.

The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783-0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning.

Every year cervical cancer affects more than 300,000 people, and on average one woman is diagnosed with cervical cancer every minute. Early diagnosis and classification of cervical lesions greatly boosts up the chance of successful treatments of patients, and automated diagnosis and classification of cervical lesions from Papanicolaou (Pap) smear images have become highly demanded. To the authors' best knowledge, this is the first study of fully automated cervical lesions analysis on whole slide images (WSIs) of conventional Pap smear samples. The presented deep learning-based cervical lesions diagnosis system is demonstrated to be able to detect high grade squamous intraepithelial lesions (HSILs) or higher (squamous cell carcinoma; SQCC), which usually immediately indicate patients must be referred to colposcopy, but also to rapidly process WSIs in seconds for practical clinical usage. We evaluate this framework at scale on a dataset of 143 whole slide images, and the proposed method achieves a high precision 0.93, recall 0.90, F-measure 0.88, and Jaccard index 0.84, showing that the proposed system is capable of segmenting HSILs or higher (SQCC) with high precision and reaches sensitivity comparable to the referenced standard produced by pathologists. Based on Fisher's Least Significant Difference (LSD) test (P < 0.0001), the proposed method performs significantly better than the two state-of-the-art benchmark methods (U-Net and SegNet) in precision, F-Measure, Jaccard index. For the run time analysis, the proposed method takes only 210 seconds to process a WSI and is 20 times faster than U-Net and 19 times faster than SegNet, respectively. In summary, the proposed method is demonstrated to be able to both detect HSILs or higher (SQCC), which indicate patients for further treatments, including colposcopy and surgery to remove the lesion, and rapidly processing WSIs in seconds for practical clinical usages.

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy.

Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to select the best treatment regimen for individual patients. We developed a computational model from 8981 clinical variables, including demographic data, diagnoses, laboratory results, medications, and initial BMD results, taken from 10-year period of electronic medical records to predict BMD response after treatment. We trained 7 machine learning models with 13,562 osteoporosis treatment instances [comprising 5080 (37.46%) inadequate treatment responses and 8482 (62.54%) adequate responses] and selected the best model (Random Forests with area under the receiver operating curve of 0.70, accuracy of 0.69, precision of 0.70, and recall of 0.89) to individually predict treatment responses of 11 therapeutic regimens, then selected the best predicted regimen to compare with the actual regimen. The results showed that the average treatment response of the recommended regimens was 9.54% higher than the actual regimens. In summary, our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and personalising the most appropriate treatment regimen for an individual patient.

Embryo selection with artificial intelligence: how to evaluate and compare methods?

Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.