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General General

Predicting future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study.

In Journal of affective disorders ; h5-index 79.0

BACKGROUND : The predictive accuracy of suicidal behaviour has not improved over the last decades. We aimed to explore the potential of machine learning to predict future suicidal behaviour using population-based longitudinal data.

METHOD : Baseline risk data assessed within the Scottish wellbeing study, in which 3508 young adults (18-34 years) completed a battery of psychological measures, were used to predict both suicide ideation and suicide attempts at one-year follow-up. The performance of the following algorithms was compared: regular logistic regression, K-nearest neighbors, classification tree, random forests, gradient boosting and support vector machine.

RESULTS : At one year follow up, 2428 respondents (71%) finished the second assessment. 336 respondents (14%) reported suicide ideation between baseline and follow up, and 50 (2%) reported a suicide attempt. All performance metrics were highly similar across methods. The random forest algorithm was the best algorithm to predict suicide ideation (AUC 0.83, PPV 0.52, BA 0.74) and the gradient boosting to predict suicide attempt (AUC 0.80, PPV 0.10, BA 0.69).

LIMITATIONS : The number of respondents with suicidal behaviour at follow up was small. We only had data on psychological risk factors, limiting the potential of the more complex machine learning algorithms to outperform regular logistical regression.

CONCLUSIONS : When applied to population-based longitudinal data containing multiple psychological measurements, machine learning techniques did not significantly improve the predictive accuracy of suicidal behaviour. Adding more detailed data on for example employment, education or previous health care uptake, might result in better performance of machine learning over regular logistical regression.

van Mens Kasper, de Schepper Cwm, Wijnen Ben, Koldijk Saskia J, Schnack Hugo, de Looff Peter, Lokkerbol Joran, Wetherall Karen, Cleare Seonaid, C O’Connor Rory, de Beurs Derek

2020-Jun-15

Pathology Pathology

Artificial intelligence and machine learning in nephropathology.

In Kidney international ; h5-index 87.0

Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.

Becker Jan U, Mayerich David, Padmanabhan Meghana, Barratt Jonathan, Ernst Angela, Boor Peter, Cicalese Pietro A, Mohan Chandra, Nguyen Hien V, Roysam Badrinath

2020-Apr-01

artificial intelligence, computer, convolutional neural network, image recognition, nephropathology

General General

Conformational distributions of isolated myosin motor domains encode their mechanochemical properties.

In eLife

Myosin motor domains perform an extraordinary diversity of biological functions despite sharing a common mechanochemical cycle. Motors are adapted to their function, in part, by tuning the thermodynamics and kinetics of steps in this cycle. However, it remains unclear how sequence encodes these differences, since biochemically distinct motors often have nearly indistinguishable crystal structures. We hypothesized that sequences produce distinct biochemical phenotypes by modulating the relative probabilities of an ensemble of conformations primed for different functional roles. To test this hypothesis, we modeled the distribution of conformations for 12 myosin motor domains by building Markov state models (MSMs) from an unprecedented two milliseconds of all-atom, explicit-solvent molecular dynamics simulations. Comparing motors reveals shifts in the balance between nucleotide-favorable and nucleotide-unfavorable P-loop conformations that predict experimentally measured duty ratios and ADP release rates better than sequence or individual structures. This result demonstrates the power of an ensemble perspective for interrogating sequence-function relationships.

Porter Justin R, Meller Artur, Zimmerman Maxwell I, Greenberg Michael J, Bowman Gregory R

2020-May-29

biochemistry, chemical biology, chicken, conformational heterogeneity, dictyostelium, energy landscapes, human, machine learning, markov sate models, molecular biophysics, molecular dynamics, structural biology

oncology Oncology

Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study.

In Turkish journal of urology

OBJECTIVE : Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed.

MATERIAL AND METHODS : Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded.

RESULTS : The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively.

CONCLUSIONS : In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.

Shah Milap, Naik Nithesh, Somani Bhaskar K, Hameed B M Zeeshan

2020-May-27

General General

Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence.

In Annals of internal medicine ; h5-index 120.0

Increasingly, interventions aimed at improving care are likely to use such technologies as machine learning and artificial intelligence. However, health care has been relatively late to adopt them. This article provides clinical examples in which machine learning and artificial intelligence are already in use in health care and appear to deliver benefit. Three key bottlenecks toward increasing the pace of diffusion and adoption are methodological issues in evaluation of artificial intelligence-based interventions, reporting standards to enable assessment of model performance, and issues that need to be addressed for an institution to adopt these interventions. Methodological best practices will include external validation, ideally at a different site; use of proactive learning algorithms to correct for site-specific biases and increase robustness as algorithms are deployed across multiple sites; addressing subgroup performance; and communicating to providers the uncertainty of predictions. Regarding reporting, especially important issues are the extent to which implementing standardized approaches for introducing clinical decision support has been followed, describing the data sources, reporting on data assumptions, and addressing biases. Although most health care organizations in the United States have adopted electronic health records, they may be ill prepared to adopt machine learning and artificial intelligence. Several steps can enable this: preparing data, developing tools to get suggestions to clinicians in useful ways, and getting clinicians engaged in the process. Open challenges and the role of regulation in this area are briefly discussed. Although these techniques have enormous potential to improve care and personalize recommendations for individuals, the hype regarding them is tremendous. Organizations will need to approach this domain carefully with knowledgeable partners to obtain the hoped-for benefits and avoid failures.

Bates David W, Auerbach Andrew, Schulam Peter, Wright Adam, Saria Suchi

2020-Jun-02

oncology Oncology

Management of oligometastatic and oligoprogressive renal cell carcinoma: state of the art and future directions.

In Expert review of anticancer therapy

INTRODUCTION : The aim of this paper was to perform a narrative review of the literature on the available approaches in the treatment of two emerging subpopulations of metastatic renal cell carcinoma (mRCC) patients: the oligometastatic disease (less than 5 metastasis) and the oligoprogressive disease, defined as worsening in maximum 3-5 sites while all other tumor sites are controlled by systemic therapy.

AREAS COVERED : We explore all possible approaches in these settings of patients: the role of local therapies, considering both surgical metastasectomy and/or ablative techniques, the efficacy of systemic therapies and the rationale behind active surveillance. We also discuss ongoing clinical trials in these settings.

EXPERT OPINION : Two different strategies are emerging as the most promising for the approach to the oligometastatic/oligoprogressive mRCC patient: (1) the use of immunocheckpoint inhibitors following metastasectomy; (2) the use of stereotactic radiotherapy alone or combined with immunotherapy for oligometastatic disease. The lack of validated biomarkers of response in these mRCC patient subpopulations is opening the way to the employment of novel technologies. Among them, the use of artificial intelligence seems to be the candidate to contribute to precision oncology in patients with mRCC.

Donini Maddalena, Buti Sebastiano, Massari Francesco, Mollica Veronica, Rizzo Alessandro, Montironi Rodolfo, Bersanelli Melissa, Santoni Matteo

2020-Jun-01

Active surveillance, loco-regional treatments, metastasectomy, oligometastatic, oligoprogression, oligoprogressive, radiotherapy, renal cell carcinoma