Using Ensemble Machine Learning: A Multifaceted Approach to Predicting Rehabilitation Outcomes | Chapter 2 |Disease and Health Research: New Insights Vol. 8

The field of rehabilitation for individuals with neuromuscular diseases has witnessed significant advancements, driven by the convergence of robotics, assistive technologies, and machine learning. These innovations are reshaping how rehabilitation is approached, offering new possibilities for personalized, effective, and efficient treatments that were previously unattainable. This chapter delves into the transformative role of these technologies, emphasizing the development and application of ensemble machine learning models specifically designed to predict rehabilitation outcomes. The study provides a comprehensive overview of the current state of rehabilitation technologies, focusing on the integration of robotics and orthotics into therapeutic practices. Robotics and assistive technologies, such as powered exoskeletons and smart orthoses, are becoming increasingly vital in enhancing the mobility and autonomy of patients with neuromuscular impairments. These devices are not only extending the capabilities of traditional rehabilitation methods but are also enabling more precise and controlled therapeutic interventions that can be tailored to the unique needs of each patient. In the following sections, this study explores the intricate design and functionality of orthoses and assistive robots, illustrating how these devices are engineered to support and augment human movement. The chapter discusses various types of orthotic devices, from simple mechanical braces to advanced robotic systems that interact seamlessly with the human body, adapting to the user's movements and providing real-time feedback. These innovations are shown to significantly improve the effectiveness of rehabilitation exercises, accelerate recovery times, and enhance overall patient outcomes. Central to this discussion is the introduction of a novel, multifaceted approach to predicting rehabilitation outcomes using ensemble machine learning models. These models, which combine the strengths of multiple algorithms, are designed to capture complex patterns in patient data, thereby providing more accurate and reliable predictions of rehabilitation success. This study present a detailed analysis of the development process of these models, including the selection of relevant features, the training and validation of the models, and the implementation of advanced visualization techniques to interpret the results. Through rigorous cross-validation and real-world testing, the robustness of these ensemble models is demonstrated in predicting outcomes across diverse patient populations. The results indicate that these models outperform traditional predictive methods, offering superior accuracy and the ability to generalize across different patient scenarios. The chapter also includes a series of visualizations that illustrate the importance of various features in the prediction process, the relationship between actual and predicted outcomes, and the distribution of model residuals. Furthermore, this study discusses the implications of these findings for clinical practice. The ability to predict rehabilitation outcomes with high precision allows clinicians to tailor interventions more effectively, optimizing treatment plans for individual patients and potentially reducing recovery times. The chapter concludes by considering the broader impact of these technologies on the healthcare system, highlighting the potential for machine learning models to drive innovation in personalized medicine and rehabilitation. Looking forward, several key areas for future research were identified including the integration of real-time data analytics into rehabilitation devices, the development of more sophisticated models that can adapt to changing patient conditions, and the exploration of new forms of human-machine interaction that can further enhance the efficacy of rehabilitation. The ongoing evolution of these technologies promises to open new frontiers in the treatment of neuromuscular diseases, ultimately improving the quality of life for millions of patients worldwide.

 

Author (s) Details

Rocco de Filippis
Institute of Psychopathology, Rome, Italy.

 

Abdullah Al Foysal
Computer Engineering (AI), University of Genova, Genova, Italy.

 

Please see the book here:- https://doi.org/10.9734/bpi/dhrni/v8/2486

 

Basic Research and Major Clinical Trials with Innovations for Individual Treatment of Colon- and Rectal Cancer Patients with a Multidisciplinary International Team: From Translational Research to National Guidelines | Chapter 5 | New Visions in Medicine and Medical Science Vol. 7

With the aim to improve standard treatments of patients with Colon Cancer (CC) and Rectal Cancer (CC), including own developments/tools of basic research transformed to clinical relevance, we formed a team of researchers out of clinical disciplines and basic research groups. The patients with their individual diseases and characters were the centers of our efforts – always aiming at best treatment quality and best quality of life. Rarely, scientific developments centered around the patient as a whole are published. Our multidisciplinary group, headed by gastrointestinal surgeons, applied this research philosophy considering the most important aspects of the diseases “colon- and rectal cancer” in the long-term developments. Good expert cooperation/knowledge at the Comprehensive Cancer Center Ulm (CCCU) were applied in several phase III trials for multimodal treatments of primary tumors (MMT) and metastatic diseases (involving nearly 2000 patients and 64 centers), for treatment individualization of MMT and of metastatic disease, for psycho- oncology/quality of life involving the patients’ wishes, and for disease prevention. Most of our research programs initially were heavily rejected/discussed in the scientific communities, but most of the results now have become standards in treatments and national guidelines or are topics in modern translational research protocols involving molecular biology for e.g., “patient centered individualized treatment” and “response prediction”, e.g. in “multimodal treatment” or “downstaging-resection of primary tumors or metastases”. Our research paths might be of interest for established researchers – but also for young colleagues who might start working in the fields described. Progress can be achieved with clear aims and in a motivated interdisciplinary team.

Author(s) Details:

Karl-Heinrich Link,
Asklepios Tumor Center (ATC) and Surgical Center, Asklepios Paulinen Klinik, Wiesbaden 65197, Germany, FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of General and Visceral Surgery, University of Ulm, Ulm 89081, Germany.

Sakhavat Jafarov,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of General and Visceral Surgery, University of Duesseldorf, Duesseldorf 40225, Germany.

Marko Kornmann,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of General and Visceral Surgery, University of Ulm, Ulm 89081, Germany.

Ludger Staib,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of General and Visceral Surgery, University of Ulm, Ulm 89081, Germany.

Ernst-Dietrich Kreuser,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany.

Wilhelm Gaus,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of Biometry and Medical Documentation, University of Ulm, Ulm 89081, Germany.

Erwin Röttinger,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of Radiotherapy, University of Ulm, Ulm 89081, Germany.

Peter Suhr,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of Radiotherapy, University of Ulm, Ulm 89081, Germany.

Catharina Maulbecker-Armstrong,
Fachbereich Gesundheit, Medizinisches Management, Sozialversicherungssysteme, Internationales Versorgungsmanagement, e-Health, Technische Hochschule Mittelhessen, Giessen 35390, Germany.

Peter Danenberg,
Department of Biochemistry and Molecular Medicine, Keck-USC School of Medicine, Los Angeles, CA 90033, United States.

Kathleen Danenberg,
Norris Comprehensive Cancer Center, Keck-USC School of Medicine, Los Angeles, CA 90033, United States.

Miriam Schatz,
Private Practice for Psychology, Adelsdorf 91325, Germany.

Silvia Sander,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of Biometry and Medical Documentation, University of Ulm, Ulm 89081, Germany.

Zhen-Ling Ji,
Department of General Surgery, Southeast University, Nanjing 210009, Jiangsu Province, China.

Jiang-Tao Li,
Department of Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, Zhejiang Province, China.

Shu-You Peng,
Department of Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, Zhejiang Province, China.

Reinhard Bittner,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Surgical Clinic, Marienhospital, Stuttgart 70177, Germany.

Hans Günther Beger,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of General and Visceral Surgery, University of Ulm, Ulm 89081, Germany.

Benno Traub,
FOGT (Multidisciplinary Study Group on Oncology of Gastrointestinal Tumors), University of Ulm, Ulm 89081, Germany and Department of General and Visceral Surgery, University of Ulm, Ulm 89081, Germany.

Please see the link here: https://stm.bookpi.org/NVMMS-V7/article/view/14222

Personalized Treatment of Rare Cancers Based on Comprehensive Diagnostics: Case Reports of Two Neuroendocrine Breast Cancers | Chapter 11 | Current Innovations in Medicine and Medical Science Vol. 4

 The current case study highlights the diagnostic and treatment difficulties that two patients with NEBC experienced throughout their epicrisis in the aforementioned circumstances. One percent of all neuroendocrine tumours and 0.1% of all breast carcinomas are caused by the rare disease known as neuroendocrine breast cancer (NEBC) [6]. Here, we offer two case reports of NEBC cohorts with large cell and small cell diagnoses in the absence of prospective trial data. When the first treatments failed, thorough tumour profiling was used to produce tailored treatment plans for both patients. Targetable changes of the PI3K/AKT/mTOR pathway, including a PIK3CA mutation and a STK11 mutation that inhibits the mTOR complex, were discovered in both individuals. The two patients' epicrisis serves as an example of how to manage rare and challenging cancers and how modern diagnostic techniques support medical management. The earlier we consider adopting customised medicines based on thorough tumour features, the sooner we can determine whether this strategy will be beneficial. Trials focused on a variety of uncommon cancer types and thorough profiling may contain the secret to an effective cure.

Author(s) Details:

Dörthe Schaffrin-Nabe,
Praxis für Hämatologie und Onkologie, Bochum, Germany.

Anke Josten-Nabe,
Praxis für Hämatologie und Onkologie, Bochum, Germany.

Stefan Schuster,
Datar Cancer Genetics Europe GmbH, Eckersdorf, Germany.

Andrea Tannapfel,
Institute of Pathology, Ruhr-University Bochum, Bochum, Germany.

Rudolf Voigtmann,
Praxis für Hämatologie und Onkologie, Bochum, Germany.

Please see the link here: https://stm.bookpi.org/CIMMS-V4/article/view/8401