Improving Health and Quality of Life in One-Person Households Using IoT and Machine Learning

2019 ◽  
Author(s):  
Masahide Nakamura
Keyword(s):  
Quality Of Life ◽  
Machine Learning

scholarly journals Analysis of ergonomic and unergonomic human lifting behaviors by using Inertial Measurement Units

2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Angular Velocity ◽  
Musculoskeletal System ◽  
Occupational Diseases ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Main Components

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.

scholarly journals Machine-Learning Points at Endoscopic, Quality of Life, and Olfactory Parameters as Outcome Criteria for Endoscopic Paranasal Sinus Surgery in Chronic Rhinosinusitis

2021 ◽  
Vol 10 (18) ◽  
pp. 4245
Author(s):  
Jörn Lötsch ◽  
Constantin A. Hintschich ◽  
Petros Petridis ◽  
Jürgen Pade ◽  
Thomas Hummel
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Chronic Rhinosinusitis ◽  
Paranasal Sinus ◽  
Endoscopic Sinus Surgery ◽  
Relevant Information ◽  
Olfactory Function ◽  
Sinus Surgery ◽  
Paranasal Sinus Surgery

Chronic rhinosinusitis (CRS) is often treated by functional endoscopic paranasal sinus surgery, which improves endoscopic parameters and quality of life, while olfactory function was suggested as a further criterion of treatment success. In a prospective cohort study, 37 parameters from four categories were recorded from 60 men and 98 women before and four months after endoscopic sinus surgery, including endoscopic measures of nasal anatomy/pathology, assessments of olfactory function, quality of life, and socio-demographic or concomitant conditions. Parameters containing relevant information about changes associated with surgery were examined using unsupervised and supervised methods, including machine-learning techniques for feature selection. The analyzed cohort included 52 men and 38 women. Changes in the endoscopic Lildholdt score allowed separation of baseline from postoperative data with a cross-validated accuracy of 85%. Further relevant information included primary nasal symptoms from SNOT-20 assessments, and self-assessments of olfactory function. Overall improvement in these relevant parameters was observed in 95% of patients. A ranked list of criteria was developed as a proposal to assess the outcome of functional endoscopic sinus surgery in CRS patients with nasal polyposis. Three different facets were captured, including the Lildholdt score as an endoscopic measure and, in addition, disease-specific quality of life and subjectively perceived olfactory function.

Identifying determinants of quality of life in patients undergoing systemic therapy for solid tumors.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6596-6596
Author(s):  
Frank Po-Yen Lin ◽  
Chloe Martin ◽  
Simon Kocbek ◽  
Anthony M. Joshua ◽  
Rachel Fitz-Gerald Dear ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Systemic Therapy ◽  
Cross Sectional Study ◽  
Support Vector ◽  
Tumor Type ◽  
Cross Sectional ◽  
Treatment Characteristics ◽  
Patient Reported

6596 Background: Knowing which factors compromise quality of life (QoL) in patients undergoing cancer treatments can help oncologists provide more effective care. To identify these factors, we conducted a single-centered cross-sectional study examining the relationships between patient-reported QoL, adverse events (AE), and treatment characteristics. Methods: Consecutive patients attending an outpatient chemotherapy unit completed two questionnaires (EORTC QLQ-C30 and National Cancer Institute PRO-CTCAE) per visit to identify factors contributing to the lowest global QoL score [QLQ-C30 QL2, range 0 (worst)–100 (best)] over a 6-week period. QL2 was correlated to each PRO-CTCAE item and treatment characteristic (tumor type, drug class, number of cycles, and treatment intent) using multiple regression, adjusted for age, sex, and use of concurrent radiotherapy. To determine whether QoL can be reliably modeled by machine learning, ten algorithms were compared for performance in classifying patients into dichotomized QL2 subgroups. Results: One hundred and fifteen of 130 patients (157/244 visits) completed up to 6 sets of questionnaires (median QL2: 67, IQR: 50–83). No difference was found between QL2 and treatment characteristics (at α Bonferroni=5×10-4). However, QL2 was significantly associated with AE in gastrointestinal, respiratory, attention, pain, sleep/wake, and mood categories. Using AE as covariates, support vector machine with radial basis kernel was the best at classifying patients into QoL groups (mean bootstrapped area under ROC curve 0.812, 95% CI 0.700–0.925). Conclusions: Patient-reported QoL is associated with multiple AE, but not with characteristics of systemic therapy. Machine learning analysis suggests that a combined AE analysis may reliably characterize a patient’s QoL. [Table: see text]

Augmenting machine learning algorithms to predict mortality using patient-reported outcomes in oncology.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1510-1510
Author(s):  
Ravi Bharat Parikh ◽  
Jill Schnall ◽  
Manqing Liu ◽  
Peter Edward Gabriel ◽  
Corey Chivers ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Mortality Risk ◽  
Patient Reported Outcomes ◽  
Machine Learning Algorithms ◽  
Measurement Information ◽  
Patients With Cancer ◽  
Patient Reported ◽  
Mortality Table

1510 Background: Machine learning (ML) algorithms based on electronic health record (EHR) data have been shown to accurately predict mortality risk among patients with cancer, with areas under the curve (AUC) generally greater than 0.80. While patient-reported outcomes (PROs) may also predict mortality among patients with cancer, it is unclear whether routinely-collected PROs improve the predictive performance of EHR-based ML algorithms. Methods: This cohort study included 8600 patients with cancer who had an outpatient encounter at one of 18 medical oncology practices in a large academic health system between July 1st, 2019 and January 1st, 2020. 4692 (54.9%) patients completed assessments of symptoms, performance status, and quality of life from the PRO version of the Common Terminology Criteria for Adverse Events and the Patient-Reported Outcomes Measurement Information System Global v.1.2 scales. We hypothesized that ML models predicting 180-day all-cause mortality based on EHR + PRO data would improve AUC compared to ML models based on EHR data alone. We assessed univariate and adjusted associations between each PRO and 180-day mortality. To train the EHR-only model, we fit a Least Absolute Shrinkage and Selection Operator (LASSO) regression using 192 EHR demographic, comorbidity, and laboratory variables. To train the EHR + PRO model, we used a two-phase approach to fit a model using EHR data for all patients and PRO data for those who completed assessments. To test our hypothesis, we compared the bootstrapped AUC, area under the precision-recall curve (AUPRC), and sensitivity at a 20% risk threshold for both models. Results: 464 (5.4%) patients died within 180 days of the encounter. Decreased quality of life, functional status, and appetite were associated with greater 180-day mortality (Table). Compared to the EHR-only model, the EHR + PRO model significantly improved AUC (0.86 [95% CI 0.85-0.86] vs. 0.80 [95% CI 0.80-0.81]), AUPRC (0.40 [95% CI 0.37-0.42] vs. 0.30 [95% CI 0.28-0.32]), and sensitivity (0.45 [95% CI 0.42-0.48] vs. 0.33 [95% CI 0.30-0.35]). Conclusions: Routinely collected PROs augment EHR-based ML mortality risk algorithms. ML algorithms based on EHR and PRO data may facilitate earlier supportive care for patients with cancer. Association of PROs with 180-day mortality.[Table: see text]

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