Data analysis: challenges and specifics in neuroscience and psychiatry
Authors:
Jan Kalina 1; Jana Zvárovᆠ1,2
Authors‘ workplace:
Ústav informatiky AV ČR, Praha
1; Ústav hygieny a epidemiologie 1. LF UK v Praze
2
Published in:
Čas. Lék. čes. 2017; 156: 430-436
Category:
Review Articles
Overview
The amount of available data relevant for clinical decision support is rising not only rapidly but at the same time much faster than our ability to analyze and interpret them. Thus, the potential of the data to contribute to determining the diagnosis, therapy and prognosis of an individual patient is not appropriately exploited. The hopes to obtain benefit from the data for an individual patient must be accompanied by a reliable and diligent biostatistical analysis which faces serious challenges not always clear to non-statisticians. The aim of this paper is to discuss principles of statistical analysis of big data in research and routine applications in clinical medicine, focusing on particular aspects of psychiatry.
The paper brings arguments in favor of the idea that the biostatistical analysis of data in a specialty field requires different approaches and different experience compared to other clinical fields. This is illustrated by a description of common complications of the analysis of psychiatric data. Challenges of the analysis of big data in both psychiatric research and routine practice are explained, which are far from a routine service activity exploiting standard methods of multivariate statistics and/or machine learning. Important research questions, which are important in the current psychiatric research, are presented and discussed from the biostatistical point of view.
Keywords:
biostatistics, big data, psychiatry, decision support
Sources
1. Chen H, Fuller SS, Friedman C, Hersh W. Medical Informatics: Knowledge Management and Data Mining in Biomedicine. Springer, New York, 2005.
2. Borangíu T, Purcarea V. The future of healthcare – information based medicine. J Med Life 2008; 1: 233−237.
3. Hanson A, Levin BL. Mental Health Informatics. Oxford University Press, Oxford, 2013.
4. Levin BL, Hennessy KD, Petrila J. Mental Health Services: A Public Health Perspective. Oxford University Press, Oxford, 2010.
5. Baesens B. Analytics in a Big Data World. Wiley, Hoboken, 2014.
6. Kalina J, Zvárová J. Decision support for mental health: towards the information-based psychiatry. Psychology and mental health: concepts, methodologies, tools, and applications. IGI Global, Hershey, 2016: 1−14.
7. Šedivec V. Přehled dějin psychiatrie. Psychiatrické centrum, Praha, 2009.
8. Thornton T. Clinical Judgment, Tacit Knowledge, and Recognition in Psychiatric Diagnosis. Oxford Handbooks Online, Oxford, 2013.
9. Berka P, Rauch J, Zighed DA. Data mining and medical knowledge management: cases and applications. IGI Global, Hershey, 2009.
10. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning (2nd ed.). Springer, New York, 2009.
11. Dziuda DM. Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data. Wiley, New York, 2010.
12. Whelan R, Garavan H. When optimism hurts: Inflated predictions in psychiatric neuroimaging. Biol Psychiatry 2015; 75: 746−748.
13. Pirooznia M, Seifuddin F, Judy J et al. Data mining approaches for genome-wide association of mood disorders. Psychiatr Genet 2012; 22: 55−61.
14. Kalina J. Classification methods for high-dimensional genetic data. Biocybern Biomed Eng 2014; 34: 10−18.
15. Jurečková J, Sen PK, Picek J. Methodology in Robust and Nonparametric Statistics. CRC Press, Boca Raton, 2012.
16. Heritier S, Cantoni E, Copt S, Feser MPV. Robust Methods in Biostatistics. Wiley, Chichester, 2009.
17. Gschwandtner M, Filzmoser P. Outlier detection in high dimension using regularization. Adv Intel Syst Comput 2013; 190: 237−244.
18. Kalina J, Hlinka J. Implicitly weighted robust classification applied to brain activity research. Commun Comp Inf Sci 2017; 690: 87−107.
19. Lohoff FW. Overview of the genetics of major depressive disorder. Curr Psychiatry Rep 2010; 12: 539−546.
20. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014; 511: 421−427.
21. Kalina J. Implicitly weighted methods in robust image analysis. J Math Imag Vis 2012; 44: 449−462.
22. Wager TD, Keller MC, Lacey SC, Jonides J. Increased sensitivity in neuroimaging analyses using robust regression. Neuroimage 2005; 26: 99−113.
23. Hlinka J, Paluš M, Vejmelka M et al. Functional connectivity in resting-state fMRI: Is linear correlation sufficient? Neuroimage 2011; 54: 2218−2225.
24. Marshall E. Human genome 10th anniversary: Waiting for the revolution. Science 2011; 331: 526−529.
25. Mitchell P, Meiser B, Wilde A et al. Predictive and diagnostic genetic testing in psychiatry. Psychiatr Clin North Am 2010; 33: 225−243.
26. Van Bemmel JH, Musen MA. Handbook of Medical Informatics. Bohn Stafleu van Loghum, Houten, 2000.
27. Suhasini A, Palanivel S, Ramalingam V. Multimodel decision support system for psychiatry problem. Expert Syst Appl 2011; 38: 4990−4997.
28. Deslich S, Stec B, Tomblin S, Coustasse A. Telepsychiatry in the 21st century: transforming healthcare with technology. Perspect Health Inf Manag 2013; 10: 1f.
Labels
Addictology Allergology and clinical immunology Angiology Audiology Clinical biochemistry Dermatology & STDs Paediatric gastroenterology Paediatric surgery Paediatric cardiology Paediatric neurology Paediatric ENT Paediatric psychiatry Paediatric rheumatology Diabetology Pharmacy Vascular surgery Pain management Dental HygienistArticle was published in
Journal of Czech Physicians
Most read in this issue
- Secondary symptoms of disability in international studies
- The contemporary view of the cardiac conduction system
- Traditional medicine and the present: the therapy of gout
- New ways towards the improvement of the seniors’ health literacy