scholarly journals Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables

2020 ◽  
Vol 17 (3) ◽  
pp. 193-206 ◽  
Author(s):  
Giampaolo Perna ◽  
Alessandra Alciati ◽  
Silvia Daccò ◽  
Massimiliano Grassi ◽  
Daniela Caldirola
Keyword(s):  
Clinical Practice ◽  
Antidepressant Treatment ◽  
Therapeutic Interventions ◽  
Machine Learning Techniques ◽  
Clinical Variables ◽  
Learning Techniques ◽  
Error Treatment ◽  
Computational Resources ◽  
Personalized Psychiatry ◽  
Selection Of

Despite several pharmacological options, the clinical outcomes of major depressive disorder (MDD) are often unsatisfactory. Personalized psychiatry attempts to tailor therapeutic interventions according to each patient’s unique profile and characteristics. This approach can be a crucial strategy in improving pharmacological outcomes in MDD and overcoming trial-and-error treatment choices. In this narrative review, we evaluate whether sociodemographic (i.e., gender, age, race/ethnicity, and socioeconomic status) and clinical [i.e., body mass index (BMI), severity of depressive symptoms, and symptom profiles] variables that are easily assessable in clinical practice may help clinicians to optimize the selection of antidepressant treatment for each patient with MDD at the early stages of the disorder. We found that several variables were associated with poorer outcomes for all antidepressants. However, only preliminary associations were found between some clinical variables (i.e., BMI, anhedonia, and MDD with melancholic/atypical features) and possible benefits with some specific antidepressants. Finally, in clinical practice, the assessment of sociodemographic and clinical variables considered in our review can be valuable for early identification of depressed individuals at high risk for poor responses to antidepressants, but there are not enough data on which to ground any reliable selection of specific antidepressant class or compounds. Recent advances in computational resources, such as machine learning techniques, which are able to integrate multiple potential predictors, such as individual/ clinical variables, biomarkers, and genetic factors, may offer future reliable tools to guide personalized antidepressant choice for each patient with MDD.

scholarly journals The revolution of personalized psychiatry: will technology make it happen sooner?

2017 ◽  
Vol 48 (5) ◽  
pp. 705-713 ◽  
Author(s):  
G. Perna ◽  
M. Grassi ◽  
D. Caldirola ◽  
C. B. Nemeroff
Keyword(s):  
Clinical Decision Making ◽  
Clinical Decision ◽  
Machine Learning Techniques ◽  
Proof Of Concept ◽  
Time Data ◽  
New Approach ◽  
Technological Advances ◽  
Learning Techniques ◽  
Personalized Psychiatry ◽  
Smart Wearable

Personalized medicine (PM) aims to establish a new approach in clinical decision-making, based upon a patient's individual profile in order to tailor treatment to each patient's characteristics. Although this has become a focus of the discussion also in the psychiatric field, with evidence of its high potential coming from several proof-of-concept studies, nearly no tools have been developed by now that are ready to be applied in clinical practice. In this paper, we discuss recent technological advances that can make a shift toward a clinical application of the PM paradigm. We focus specifically on those technologies that allow both the collection of massive as much as real-time data, i.e., electronic medical records and smart wearable devices, and to achieve relevant predictions using these data, i.e. the application of machine learning techniques.

scholarly journals Grain Yield Estimation in Cultivated Land Using Machine Learning Techniques

2021 ◽  
Vol 36 (2) ◽  
pp. 70-75
Author(s):  
Dr.K. Venkata Nagendra ◽  
Dr.B. Prasad ◽  
K.T.P.S. Kumar ◽  
K.S. Raghuram ◽  
Dr.K. Somasundaram
Keyword(s):  
Machine Learning ◽  
Market Price ◽  
Specific Area ◽  
Continuous Variable ◽  
Machine Learning Techniques ◽  
Accurate Information ◽  
Agricultural Crop ◽  
Agricultural Field ◽  
Learning Techniques ◽  
Selection Of

Agriculture contributes approximately 28 percent of India's GDP, and agriculture employs approximately 65 percent of the country's labor force. India is the world's second-largest agricultural crop producer. Agriculture is not only an important part of the expanding economy, but it is also necessary for our survival. The technological contribution could assist the farmer in increasing his yield. The selection of each crop is critical in the planning of agricultural production. The selection of crops will be influenced by a variety of factors, including market price, production rate, and the policies of the various government departments. Numerous changes are required in the agricultural field in order to improve the overall performance of our Indian economy. By using machine learning techniques that are easily applied to the farming sector we can improve agriculture. Along with all of the advancements in farming machinery and technology, the availability of useful and accurate information about a variety of topics plays an important role in the success of the industry. It is a difficult task to predict agricultural output since it depends on a number of variables, such as irrigation, ultraviolet (UV), insect killers, stimulants & the quantity of land enclosed in that specific area. It is proposed in this article that two distinct Machine Learning (ML) methods be used to evaluate the yields of the crops. The two algorithms, SVR and Linear Regression, have been well suited to validate the variable parameters of the continuous variable estimate with 185 acquired data points.

Multi-Fidelity Global-Local Optimization of a Transonic Compressor Rotor

2019 ◽  
Author(s):  
Sudeepta Mondal ◽  
Michael M. Joly ◽  
Soumalya Sarkar
Keyword(s):  
Search Algorithm ◽  
Local Optimization ◽  
Machine Learning Techniques ◽  
High Fidelity ◽  
Aerodynamic Design ◽  
Local Approach ◽  
Transonic Compressor ◽  
Compressor Rotor ◽  
Learning Techniques ◽  
Selection Of

Abstract In aerodynamic design, accurate and robust surrogate models are important to accelerate computationally expensive CFD-based optimization. Machine learning techniques can also enable affordable exploration of high-dimensional design spaces with targeted selection of sparse high-fidelity data. In this paper, a multi-fidelity global-local approach is presented and applied to the surrogate-based design optimization of a highly-loaded transonic compressor rotor. The key idea is to train multi-fidelity surrogates with fewer high-fidelity RANS predictions and more rapid and inexpensive lower-fidelity RANS evaluations. The framework also introduces a global-local search algorithm that can spin-off multiple local optimization threads over narrow and targeted design spaces, concurrently to a constantly adapting global optimization thread. The approach is demonstrated with an optimization of the transonic NASA rotor 37, yielding significant increase in performance within a dozen of optimization iterations.

scholarly journals Sensor, Signal, and Imaging Informatics in 2017

2018 ◽  
Vol 27 (01) ◽  
pp. 110-113 ◽  
Author(s):  
William Hsu ◽  
Thomas Deserno ◽  
Charles Kahn ◽  
Keyword(s):  
Medical Informatics ◽  
Imaging Informatics ◽  
Machine Learning Techniques ◽  
Sensor Signal ◽  
Imaging Data ◽  
Scientific Publications ◽  
Learning Techniques ◽  
Final Selection ◽  
Selection Of

Objective: To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017. Methods: PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook. Results: The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics.

scholarly journals Efficiently Coupling the I-DLV Grounder with ASP Solvers

2018 ◽  
Vol 20 (2) ◽  
pp. 205-224 ◽  
Author(s):  
FRANCESCO CALIMERI ◽  
CARMINE DODARO ◽  
DAVIDE FUSCÀ ◽  
SIMONA PERRI ◽  
JESSICA ZANGARI
Keyword(s):  
Machine Learning ◽  
Answer Set Programming ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Selection Of ◽  
Answer Set

We present ${{{{$\mathscr{I}$}-}\textsc{dlv}}+{{$\mathscr{MS}$}}}$, a new answer set programming (ASP) system that integrates an efficient grounder, namely ${{{$\mathscr{I}$}-}\textsc{dlv}}$, with an automatic selector that inductively chooses a solver: depending on some inherent features of the instantiation produced by ${{{$\mathscr{I}$}-}\textsc{dlv}}$, machine learning techniques guide the selection of the most appropriate solver. The system participated in the latest (7th) ASP competition, winning the regular track, category SP (i.e., one processor allowed).

scholarly journals Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning

2017 ◽  
Vol 58 ◽  
pp. 829-858 ◽  
Author(s):  
Michael Abseher ◽  
Nysret Musliu ◽  
Stefan Woltran
Keyword(s):  
Machine Learning ◽  
Dynamic Programming ◽  
Tree Decomposition ◽  
Machine Learning Techniques ◽  
Problem Instance ◽  
Actual Problem ◽  
Learning Techniques ◽  
Tree Decompositions ◽  
Selection Of ◽  
General Method

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems - that are in general NP-hard - efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; and (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques that provide automated selection based on features of the decomposition rather than on the actual problem instance. Thus, one main contribution of this work is to propose novel features for tree decompositions. Moreover, we report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

Generation and Management of Data for Healthcare and Health Diagnostics

2020 ◽  
pp. 106-132
Author(s):  
Ramalatha Marimuthu ◽  
Shivappriya S. N. ◽  
Saroja M. N.
Keyword(s):  
Decision Making ◽  
Clinical Care ◽  
Unstructured Data ◽  
Machine Learning Techniques ◽  
Patient Records ◽  
X Rays ◽  
Wearable Technologies ◽  
Learning Techniques ◽  
Collection Methods ◽  
Selection Of

Healthcare Analytics deals with patient records, effective management of hospitals, and clinical care. But the big data available is still not enough for focused research as it is complicated to find insights from complex, noisy, heterogeneous, and voluminous data, which takes time and effort, while a small clinical data will be more effective for decision making. The health care data also varies in data collection methods and their processing methods. Data generated through patient records is structured, wearable technologies generate semi structured data, and X rays and images provide unstructured data. Storing and extracting information from the structured, semi-structured, and unstructured data is a challenging task. Different machine learning techniques can simplify the process. The chapter discusses the data characteristics, identifying critical attributes, various classification and optimization algorithms for decision making purposes. The purpose of the discussion is to create a basis for selection of algorithms based on size, temporal validity, and outcomes expected.

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