Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment

Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach.

The role of pharmacogenomics in the personalization of Parkinson's disease treatment

Parkinson's disease (PD)-related phenotypes can vary among patients substantially, including response to dopaminergic treatment in terms of efficacy and occurrence of adverse events. Many pharmacogenetic studies have already been conducted to find genetic markers of response to dopaminergic treatment. Integration of genetic and clinical data has already resulted in construction of clinical pharmacogenetic models for prediction of adverse events. However, the results of pharmacogenetic studies are inconsistent. More comprehensive genome-wide approaches are needed to find genetic biomarkers of PD-related phenotypes to better explain the variability in response to treatment. These genetic markers should be integrated with clinical, environmental, imaging, and other omics data to build clinically useful algorithms for personalization of PD management.

IDENTIFIKASI PERILAKU PENCARIAN PENGOBATAN TRANSFER ENERGI ZIKIR: Integrasi Ilmu Kesehatan dan Islam

<p><strong>Abstrak: </strong>Kecenderungan pencarian pengobatan untuk mengatasi gangguan kesehatan dipengaruhi oleh cara pandang individu terhadap penyakit yang diderita. Penelitian ini berupaya mengidentifikasi faktor yang berpengaruh terhadap perilaku pencarian pengobatan transfer energi zikir, dengan desain penelitian <em>cross sectional</em> dan dianalisis dengan metode <em>binary logistic analysis. </em>Hasil penelitian ini menemukan bahwa perilaku pencarian pengobatan transfer energi zikir, mayoritas kategori penyakit sedang (41,9%), responden yang sembuh (71%). Perilaku pencarian pengobatan transfer energi zikir tanpa pengobatan medis sebesar (67,7%). Hasil analisis multivariant variabel <em>percieved suseptibility</em> (kerentanan) nilai p <em>value</em> 0,010&lt;0,05 artinya mempunyai pengaruh secara signifikan (bermakna) terhadap perilaku pencarian pengobatan. Nilai <em>Exp</em>. (B) sebesar 14,799 bahwa responden yang mempunyai persepsi rentan atau berisiko terhadap penyakit mempunyai peluang 14 kali mencari pengobatan dengan transfer energi zikir, dibandingkan variabel lainnya yang secara statistik memberikan kesembuhan lebih tinggi. Penulis mengimplikasikan hal ini sebagai bukti kesempurnaan agama Islam dalam mengatur kehidupan manusia secara lengkap melalui keteladanan Rasulullah menganjurkan pengobatan berdasarkan petunjuk al-Qur’an sebagai penawar dan rahmat bagi orang mukmin. <br /><strong> </strong><br /><strong>Abstract</strong>: <strong>Identification of Behavior of Zikir Energy Transfer Treatment: Integration of Health and Islamic Science</strong>. Abstract: The tendency of searching treatment to overcome health problems is influenced by the individual's perspective on the illness being sufferred. This research seeks to identify factors that influence the behavior of the search for zikir energy transfer treatment, with cross sectional research design and analyzed by binary logistic analysis method. The results of this study found that the search behavior of zikir energy transfer treatment, the majority of moderate disease category (41.9%), respondents who recover (71%). Behavior seeking treatment of zikir energy transfer without medical treatment amounted to (67,7%). The result of multivariant analysis of percieved suseptibility variable (p value) 0,010 &lt;0,05 means that it has significant (significant) effect on treatment seeking behavior. Value Exp. (B) of 14.799 that respondents who have a vulnerable or risky perception of the disease have 14 times the chance to seek treatment with the transfer of zikir energy, compared to other variables that statistically resulted in higher number of healing. The author implies that this prooves the perfection of Islam in regulating the complete human life through the the Prophet who advocated treatment based on the guidance of the Qur'an as a healing and grace for the believers.</p><p><br /><strong>Kata Kunci: </strong>pengobatan Islami, energi zikir, kesehatan, integrasi</p>