Implementation and Comparison of Two Pharmacometric Tools for Model-Based Therapeutic Drug Monitoring and Precision Dosing of Daptomycin

Daptomycin is a candidate for therapeutic drug monitoring (TDM). The objectives of this work were to implement and compare two pharmacometric tools for daptomycin TDM and precision dosing. A nonparametric population PK model developed from patients with bone and joint infection was implemented into the BestDose software. A published parametric model was imported into Tucuxi. We compared the performance of the two models in a validation dataset based on mean error (ME) and mean absolute percent error (MAPE) of individual predictions, estimated exposure and predicted doses necessary to achieve daptomycin efficacy and safety PK/PD targets. The BestDose model described the data very well in the learning dataset. In the validation dataset (94 patients, 264 concentrations), 21.3% of patients were underexposed (AUC24h < 666 mg.h/L) and 31.9% of patients were overexposed (Cmin > 24.3 mg/L) on the first TDM occasion. The BestDose model performed slightly better than the model in Tucuxi (ME = −0.13 ± 5.16 vs. −1.90 ± 6.99 mg/L, p < 0.001), but overall results were in agreement between the two models. A significant proportion of patients exhibited underexposure or overexposure to daptomycin after the initial dosage, which supports TDM. The two models may be useful for model-informed precision dosing.

US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model

For the purpose of control health expenditures, there are some papers investigating the characteristics of patients who may incur high expenditures. However fewer papers are found which are based on the overall medical conditions, so this chapter was to find a relationship among the prevalence of medical conditions, utilization of healthcare services, and average expenses per person. The authors used bootstrapping simulation for data preprocessing and then used linear regression and random forest methods to train several models. The metrics root mean square error (RMSE), mean absolute percent error (MAPE), mean absolute error (MAE) all showed that the selected linear regression model performs slightly better than the selected random forest regression model, and the linear model used medical conditions, type of services, and their interaction terms as predictors.

Estimating Substitution and Basket Effects in Retail Stores: Implications for Assortment Planning

Many retailers are reducing store footprint and downsizing their assortments accordingly to improve store productivity. Some of the revenue for items removed from the assortment may be recouped by substitution, but also some of the revenue for items kept in the assortment may be lost due to basket abandonment. For a practical setting where baskets may contain any subset of items from thousands of products, estimating both substitution and basket effects is a challenge. To address this, we develop a demand model that combines a multinomial logit (MNL) model to estimate substitution within a subcategory and a purchase-incidence model to estimate basket retention. Using transaction and product availability data from 12 stores of an office supplies retail chain that were dramatically downsized from large- to small-format stores, we show that (i) storewide basket effects are substantial (our model with basket effects predicts out-of-sample transactions with mean absolute percent error (MAPE) of only 7% compared with 22% for a model with only substitution effects), (ii) poor service level can significantly exacerbate lost profit due to abandoned baskets at these stores, and (iii) consideration of the basket effect in assortment selection for the small stores can significantly improve basket retention and increase profits (by up to 16%) at these stores. This paper was accepted by Vishal Gaur, operations management.

Improving Stratification Procedures and Accuracy of Annual Average Daily Traffic (AADT) Estimates for Non-Federal Aid-System (NFAS) Roads

The 2016 safety Final Rule requires states to have access to annual average daily traffic (AADT) for all public paved roads, including non-Federal aid-system (NFAS) roads. The latter account approximately for 75% of the total roadway mileage in the country making it difficult for agencies to collect traffic data on these roads. Many agencies use stratified sampling procedures to develop default AADT estimates for uncounted segments; however, there is limited guidance and information about how to stratify the network effectively. The goal of this paper is to enhance transportation agencies’ ability to improve existing stratification schemes, design new schemes, and ultimately develop more accurate AADT estimates for NFAS roads. The paper presents the results from five pilot studies that validated and compared the performance of current, updated, and new (traditional and decision-tree-based) schemes using readily available data. According to the results, the median absolute percent error of existing AADT estimates, developed by state agencies, ranged between 71% and 120%. Updating these schemes using recent counts resulted in an AADT accuracy improvement of 25%. The best-performing schemes were developed using DTs that improved the AADT accuracy of existing schemes by 41%. Overall, having more strata and very homogenous strata is better than having fewer strata and more samples within each stratum. The analysis revealed that a key to selecting an effective scheme is to determine a critical point, beyond which creating more strata improves the AADT accuracy marginally but increases the required sample size exponentially.

Prediksi Kuota Pemesanan Bahan Bakar Pada SPBU dengan Metode Regresi Linear Berganda

Bahan bakar minyak menjadi komoditi penting dalam menjalankan roda perekonomian suatu negara, data Badan Pengatur Hilir Minyak dan Gas(BPH MIGAS) mencatat Indonesia menghabiskan 28,25 juta kiloliter selama tahun 2019, angka ini dihimpun dari seluruh Stasiun Pengisian Bahan Bakar Umum (SPBU) yang menjadi hilir distribusi BBM kepada masyarakat, namun disisi lain SPBU sering kehabisan stok karna kurangnya pengendalian terhadap stok, dampaknya adalah antrian panjang masyarakat di SPBU, bagi SPBU yang kehabisan stok jelas akan mengurangi pemasukan karna delay tidak ada penjualan selama proses pengiriman dari hulu ke hilir, maka dibutuhkan adanya sistem yang mampu membantu memprediksi berapa kuota yang harus dipesan sehingga kondisi out of stock tidak terjadi, untuk melakukan peramalan kuota bahan bakar digunakan metode regresi linier berganda yang terdiri dari variabel independent stok sisa (X1), stok masuk (X2) dan variabel dependent stok keluar (Y). Setelah dilakukan uji asumsi klasik dapat disimpulkan bahwa variabel independent (X1 dan X2) berpengaruh positif terhadap variabel dependent (Y). Dari hasil pengujian tingkat error menggunakan metode MAPE (Mean Absolute Percent Error) diperoleh tingkat error untuk peramalan pertalite selama seminggu sebesar 11,0% dan untuk tingkat error peramalan solar sebesar 13,2%.

Prediksi Tingkat Produksi Buah Kelapa Sawit dengan Metode Single Moving Average

Palm oil is one of the largest agricultural products in Indonesia and has a high economic value and can improve the welfare of oil palm farmers. The amount of oil palm fruit production is not always stable or increasing, but increases up and down which is influenced by many factors. This study aims to estimate the average amount of oil palm fruit production every year and prepare anticipatory steps in the event of a decrease in oil palm fruit production. The image processed in this study was the production of palm fruit in a few years which was generated from the results of oil palm plantations. Furthermore, data is processed using the Single Moving Avarage method. This method is a method of forecasting or predictions using a number of actual data to generate predictive values ​​in the future. The results of testing on the single moving average method can be seen forecasts of oil palm fruit production in 2021 using Moving Averge 3 of 200.749 tons with Mean Absolute Deviation 19.604, Mean Squared Error 456.963.281 and Mean Absolute Percent Error 10,0%. Moving Averge 4 was 206.771 tons with the Mean Absolute Deviation 27.333, Mean Squared Error 752.202.579 and Mean Absolute Percent Error 14,2%. Moving Averge 5 was 210.908 tons with Mean Absolute Deviation 26.890, Mean Squared Error 723.072.100 and Mean Absolute Percent Error 14.1%. The test results using the Single Moving Average method can be concluded that forecasting using Moving Average 3 can be used because the relative error level is smaller than Moving Average 4 and 5, with the value of the Mean Absolute Percent error of 10.0% and Mean Absolute Deviation 19.604.

Enhanced Breathing Pattern Detection during Running Using Wearable Sensors

Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (−0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running.

Assessing Heart Rate Using Consumer Technology Association Standards

It is difficult for developers, researchers, and consumers to compare results among emerging wearable technology without using a uniform set of standards. This study evaluated the accuracy of commercially available wearable technology heart rate (HR) monitors using the Consumer Technology Association (CTA) standards. Participants (N = 23) simultaneously wore a Polar chest strap (criterion measure), Jabra Elite earbuds, Scosche Rhythm 24 armband, Apple Watch 4, and Garmin Forerunner 735 XT during sitting, activities of daily living, walking, jogging, running, and cycling, totaling 57 min of monitored activity. The Apple Watch mean bias was within ±1 bpm, and mean absolute percent error (MAPE) was <3% in all six conditions. Garmin underestimated HR in all conditions, except cycling and MAPE was >10% during sedentary, lifestyle, walk-jog, and running. The Jabra mean bias was within ±5 bpm for each condition, and MAPE exceeded 10% for walk-jog. The Scosche mean bias was within ±1 bpm and MAPE was <5% for all conditions. In conclusion, only the Apple Watch Series 4 and the Scosche Rhythm 24 displayed acceptable agreement across all conditions. By employing CTA standards, future developers, researchers, and consumers will be able to make true comparisons of accuracy among wearable devices.

Prospective Validation of Smartphone-based Heart Rate and Respiratory Rate Measurement Algorithms

Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breath/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.

Penerapan Metode Single Exponential Smoothing Pada Prediksi Penjualan Bed Sheet

Prediksi merupakan bagian dari awal suatu proses pengambilan suatu keputusan. Dalam kegiatan produksi, prediksi dilakukan untuk menentukan jumlah permintaan terhadap suatu produk dan merupakan langkah awal dari proses perencanaan dan pengendalian produksi. Permasalahan stok barang yang umum terjadi, seperti stok barang yang tidak terjual atau stok barang dengan merk tertentu menjadi kendala yang dihadapi dalam upaya untuk memenuhi kebutuhan pelanggan. Disamping itu, upaya dalam menghasilkan perencanaan dan pengendalian produksi yang baik juga merupakan salahsatu fungsi prediksi dalam kegiatan produksi. Pada penelitian ini diimplementasikan penggunaan metode Single Exponential Smoothing untuk memprediksi stok bedsheet dari berbagai merk berdasarkan data-data penjualan produk tersebut. Metode yang digunakan untuk menghitung kesalahan prediksi yang dihasilkan adalah metode Mean Absolute Percent Error(MAPE). Nilai prediksi ditentukan dari nilai alpha yang paling cocok dari perhitungan kesalahan prediksi hingga menghasilkan nilai yang paling kecil. Data yang digunakan merupakan data penjualan bed sheet periode Februari 2020 sampai dengan Mei 2020 dari 3(tiga) merk yang cukup diminati pelanggan pada tempat studi kasus. Dari hasil perhitungan yang dilakukan hasil perhitungan akurasi prediksi dari beberapa merk bed sheet tersebut mencapai 94.01%.