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1. Applying AI to 2D Medical Imaging Data /
2. Apply Machine Learning Exercise /
solution.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Worklist prioritization: Emergency Setting"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"## First, read in the file of the current worklist with the probabilities that your two algorithms have\n",
"## generated for the two types of findings you're most concerned with:\n",
"\n",
"worklist = pd.read_csv('probabilities.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Image_Type</th>\n",
" <th>Brain_bleed_probability</th>\n",
" <th>Aortic_dissection_probability</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>0.0</td>\n",
" <td>0.00</td>\n",
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" <th>3</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>wrist_xray</td>\n",
" <td>0.0</td>\n",
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"text/plain": [
" Image_Type Brain_bleed_probability Aortic_dissection_probability\n",
"0 chest_xray 0.0 0.05\n",
"1 chest_xray 0.0 0.17\n",
"2 chest_xray 0.0 0.00\n",
"3 chest_xray 0.0 0.04\n",
"4 wrist_xray 0.0 0.00"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worklist.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, I'm just creating a new column to address the first question in the exercise, showing that every image taking 6 minutes to read will be read in the order that they are presented in this list."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"worklist['time_to_read'] = np.arange(6, 6*(len(worklist)+1),6)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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" <th></th>\n",
" <th>Image_Type</th>\n",
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" <th>Aortic_dissection_probability</th>\n",
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" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>18</td>\n",
" </tr>\n",
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" <th>3</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.04</td>\n",
" <td>24</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>wrist_xray</td>\n",
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"text/plain": [
" Image_Type Brain_bleed_probability Aortic_dissection_probability \\\n",
"0 chest_xray 0.0 0.05 \n",
"1 chest_xray 0.0 0.17 \n",
"2 chest_xray 0.0 0.00 \n",
"3 chest_xray 0.0 0.04 \n",
"4 wrist_xray 0.0 0.00 \n",
"\n",
" time_to_read \n",
"0 6 \n",
"1 12 \n",
"2 18 \n",
"3 24 \n",
"4 30 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worklist.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, for each image, I want to see if brain bleed or aortic dissection are likely. I'll create a new column showing the max probability between the two of them"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"worklist['max_prob'] = worklist[[\"Brain_bleed_probability\", \"Aortic_dissection_probability\"]].max(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
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" <td>chest_xray</td>\n",
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" <td>0.17</td>\n",
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" <td>0.17</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
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" <th>4</th>\n",
" <td>wrist_xray</td>\n",
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" Image_Type Brain_bleed_probability Aortic_dissection_probability \\\n",
"0 chest_xray 0.0 0.05 \n",
"1 chest_xray 0.0 0.17 \n",
"2 chest_xray 0.0 0.00 \n",
"3 chest_xray 0.0 0.04 \n",
"4 wrist_xray 0.0 0.00 \n",
"\n",
" time_to_read max_prob \n",
"0 6 0.05 \n",
"1 12 0.17 \n",
"2 18 0.00 \n",
"3 24 0.04 \n",
"4 30 0.00 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worklist.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great, now I want to re-order my worklist based on probabilities of critical findings:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"worklist_prioritized = worklist.sort_values(by=['max_prob'],ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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" <td>chest_xray</td>\n",
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" <td>0.93</td>\n",
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" Image_Type Brain_bleed_probability Aortic_dissection_probability \\\n",
"25 head_ct 0.99 0.00 \n",
"15 chest_xray 0.00 0.95 \n",
"10 chest_xray 0.00 0.94 \n",
"75 chest_xray 0.00 0.93 \n",
"47 chest_xray 0.00 0.93 \n",
"\n",
" time_to_read max_prob \n",
"25 156 0.99 \n",
"15 96 0.95 \n",
"10 66 0.94 \n",
"75 456 0.93 \n",
"47 288 0.93 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worklist_prioritized.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"worklist_prioritized['time_to_read_prioritized'] = np.arange(6, 6*(len(worklist)+1),6)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"worklist_prioritized['time_delta'] = worklist_prioritized['time_to_read'] - worklist_prioritized['time_to_read_prioritized']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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" <th>Aortic_dissection_probability</th>\n",
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"text/plain": [
" Image_Type Brain_bleed_probability Aortic_dissection_probability \\\n",
"25 head_ct 0.99 0.00 \n",
"15 chest_xray 0.00 0.95 \n",
"10 chest_xray 0.00 0.94 \n",
"75 chest_xray 0.00 0.93 \n",
"47 chest_xray 0.00 0.93 \n",
"\n",
" time_to_read max_prob time_to_read_prioritized time_delta \n",
"25 156 0.99 6 150 \n",
"15 96 0.95 12 84 \n",
"10 66 0.94 18 48 \n",
"75 456 0.93 24 432 \n",
"47 288 0.93 30 258 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worklist_prioritized.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, I want to find places where my algorithm saved at least 30 minutes for brain bleeds:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
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" <th>39</th>\n",
" <td>head_ct</td>\n",
" <td>0.77</td>\n",
" <td>0.0</td>\n",
" <td>240</td>\n",
" <td>0.77</td>\n",
" <td>102</td>\n",
" <td>138</td>\n",
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" <tr>\n",
" <th>45</th>\n",
" <td>head_ct</td>\n",
" <td>0.75</td>\n",
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" <td>276</td>\n",
" <td>0.75</td>\n",
" <td>108</td>\n",
" <td>168</td>\n",
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" <tr>\n",
" <th>76</th>\n",
" <td>head_ct</td>\n",
" <td>0.69</td>\n",
" <td>0.0</td>\n",
" <td>462</td>\n",
" <td>0.69</td>\n",
" <td>144</td>\n",
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" <td>474</td>\n",
" <td>0.44</td>\n",
" <td>204</td>\n",
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" <th>56</th>\n",
" <td>head_ct</td>\n",
" <td>0.22</td>\n",
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" <td>0.07</td>\n",
" <td>0.0</td>\n",
" <td>414</td>\n",
" <td>0.07</td>\n",
" <td>324</td>\n",
" <td>90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>head_ct</td>\n",
" <td>0.05</td>\n",
" <td>0.0</td>\n",
" <td>498</td>\n",
" <td>0.05</td>\n",
" <td>336</td>\n",
" <td>162</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Image_Type Brain_bleed_probability Aortic_dissection_probability \\\n",
"25 head_ct 0.99 0.0 \n",
"84 head_ct 0.91 0.0 \n",
"95 head_ct 0.90 0.0 \n",
"42 head_ct 0.89 0.0 \n",
"59 head_ct 0.89 0.0 \n",
"89 head_ct 0.78 0.0 \n",
"39 head_ct 0.77 0.0 \n",
"45 head_ct 0.75 0.0 \n",
"76 head_ct 0.69 0.0 \n",
"96 head_ct 0.45 0.0 \n",
"78 head_ct 0.44 0.0 \n",
"56 head_ct 0.22 0.0 \n",
"68 head_ct 0.07 0.0 \n",
"82 head_ct 0.05 0.0 \n",
"\n",
" time_to_read max_prob time_to_read_prioritized time_delta \n",
"25 156 0.99 6 150 \n",
"84 510 0.91 36 474 \n",
"95 576 0.90 42 534 \n",
"42 258 0.89 48 210 \n",
"59 360 0.89 54 306 \n",
"89 540 0.78 96 444 \n",
"39 240 0.77 102 138 \n",
"45 276 0.75 108 168 \n",
"76 462 0.69 144 318 \n",
"96 582 0.45 198 384 \n",
"78 474 0.44 204 270 \n",
"56 342 0.22 258 84 \n",
"68 414 0.07 324 90 \n",
"82 498 0.05 336 162 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worklist_prioritized[((worklist_prioritized.time_delta>30)&(worklist_prioritized.Image_Type=='head_ct'))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like there are 14 head CTs that were read more than 30 minutes faster than their original order. All but the last three had a probability of brain bleed < 0.4."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do the same analysis for saving at least 15 minutes with aortic dissections: "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Image_Type</th>\n",
" <th>Brain_bleed_probability</th>\n",
" <th>Aortic_dissection_probability</th>\n",
" <th>time_to_read</th>\n",
" <th>max_prob</th>\n",
" <th>time_to_read_prioritized</th>\n",
" <th>time_delta</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.95</td>\n",
" <td>96</td>\n",
" <td>0.95</td>\n",
" <td>12</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.94</td>\n",
" <td>66</td>\n",
" <td>0.94</td>\n",
" <td>18</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.93</td>\n",
" <td>456</td>\n",
" <td>0.93</td>\n",
" <td>24</td>\n",
" <td>432</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.93</td>\n",
" <td>288</td>\n",
" <td>0.93</td>\n",
" <td>30</td>\n",
" <td>258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.84</td>\n",
" <td>294</td>\n",
" <td>0.84</td>\n",
" <td>60</td>\n",
" <td>234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.83</td>\n",
" <td>234</td>\n",
" <td>0.83</td>\n",
" <td>66</td>\n",
" <td>168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.82</td>\n",
" <td>378</td>\n",
" <td>0.82</td>\n",
" <td>72</td>\n",
" <td>306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.82</td>\n",
" <td>528</td>\n",
" <td>0.82</td>\n",
" <td>78</td>\n",
" <td>450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.81</td>\n",
" <td>270</td>\n",
" <td>0.81</td>\n",
" <td>84</td>\n",
" <td>186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.79</td>\n",
" <td>516</td>\n",
" <td>0.79</td>\n",
" <td>90</td>\n",
" <td>426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.73</td>\n",
" <td>492</td>\n",
" <td>0.73</td>\n",
" <td>114</td>\n",
" <td>378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.72</td>\n",
" <td>324</td>\n",
" <td>0.72</td>\n",
" <td>120</td>\n",
" <td>204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.71</td>\n",
" <td>168</td>\n",
" <td>0.71</td>\n",
" <td>126</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.71</td>\n",
" <td>312</td>\n",
" <td>0.71</td>\n",
" <td>132</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.70</td>\n",
" <td>402</td>\n",
" <td>0.70</td>\n",
" <td>138</td>\n",
" <td>264</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.60</td>\n",
" <td>282</td>\n",
" <td>0.60</td>\n",
" <td>168</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.60</td>\n",
" <td>420</td>\n",
" <td>0.60</td>\n",
" <td>174</td>\n",
" <td>246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.49</td>\n",
" <td>564</td>\n",
" <td>0.49</td>\n",
" <td>186</td>\n",
" <td>378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.48</td>\n",
" <td>426</td>\n",
" <td>0.48</td>\n",
" <td>192</td>\n",
" <td>234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.38</td>\n",
" <td>438</td>\n",
" <td>0.38</td>\n",
" <td>210</td>\n",
" <td>228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.26</td>\n",
" <td>558</td>\n",
" <td>0.26</td>\n",
" <td>234</td>\n",
" <td>324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.26</td>\n",
" <td>372</td>\n",
" <td>0.26</td>\n",
" <td>240</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.15</td>\n",
" <td>450</td>\n",
" <td>0.15</td>\n",
" <td>288</td>\n",
" <td>162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.15</td>\n",
" <td>546</td>\n",
" <td>0.15</td>\n",
" <td>294</td>\n",
" <td>252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.11</td>\n",
" <td>480</td>\n",
" <td>0.11</td>\n",
" <td>306</td>\n",
" <td>174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.11</td>\n",
" <td>504</td>\n",
" <td>0.11</td>\n",
" <td>312</td>\n",
" <td>192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.06</td>\n",
" <td>570</td>\n",
" <td>0.06</td>\n",
" <td>330</td>\n",
" <td>240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>chest_xray</td>\n",
" <td>0.0</td>\n",
" <td>0.04</td>\n",
" <td>468</td>\n",
" <td>0.04</td>\n",
" <td>348</td>\n",
" <td>120</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Image_Type Brain_bleed_probability Aortic_dissection_probability \\\n",
"15 chest_xray 0.0 0.95 \n",
"10 chest_xray 0.0 0.94 \n",
"75 chest_xray 0.0 0.93 \n",
"47 chest_xray 0.0 0.93 \n",
"48 chest_xray 0.0 0.84 \n",
"38 chest_xray 0.0 0.83 \n",
"62 chest_xray 0.0 0.82 \n",
"87 chest_xray 0.0 0.82 \n",
"44 chest_xray 0.0 0.81 \n",
"85 chest_xray 0.0 0.79 \n",
"81 chest_xray 0.0 0.73 \n",
"53 chest_xray 0.0 0.72 \n",
"27 chest_xray 0.0 0.71 \n",
"51 chest_xray 0.0 0.71 \n",
"66 chest_xray 0.0 0.70 \n",
"46 chest_xray 0.0 0.60 \n",
"69 chest_xray 0.0 0.60 \n",
"93 chest_xray 0.0 0.49 \n",
"70 chest_xray 0.0 0.48 \n",
"72 chest_xray 0.0 0.38 \n",
"92 chest_xray 0.0 0.26 \n",
"61 chest_xray 0.0 0.26 \n",
"74 chest_xray 0.0 0.15 \n",
"90 chest_xray 0.0 0.15 \n",
"79 chest_xray 0.0 0.11 \n",
"83 chest_xray 0.0 0.11 \n",
"94 chest_xray 0.0 0.06 \n",
"77 chest_xray 0.0 0.04 \n",
"\n",
" time_to_read max_prob time_to_read_prioritized time_delta \n",
"15 96 0.95 12 84 \n",
"10 66 0.94 18 48 \n",
"75 456 0.93 24 432 \n",
"47 288 0.93 30 258 \n",
"48 294 0.84 60 234 \n",
"38 234 0.83 66 168 \n",
"62 378 0.82 72 306 \n",
"87 528 0.82 78 450 \n",
"44 270 0.81 84 186 \n",
"85 516 0.79 90 426 \n",
"81 492 0.73 114 378 \n",
"53 324 0.72 120 204 \n",
"27 168 0.71 126 42 \n",
"51 312 0.71 132 180 \n",
"66 402 0.70 138 264 \n",
"46 282 0.60 168 114 \n",
"69 420 0.60 174 246 \n",
"93 564 0.49 186 378 \n",
"70 426 0.48 192 234 \n",
"72 438 0.38 210 228 \n",
"92 558 0.26 234 324 \n",
"61 372 0.26 240 132 \n",
"74 450 0.15 288 162 \n",
"90 546 0.15 294 252 \n",
"79 480 0.11 306 174 \n",
"83 504 0.11 312 192 \n",
"94 570 0.06 330 240 \n",
"77 468 0.04 348 120 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worklist_prioritized[((worklist_prioritized.time_delta>=15)&(worklist_prioritized.Image_Type=='chest_xray'))]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"28"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(worklist_prioritized[((worklist_prioritized.time_delta>=15)&(worklist_prioritized.Image_Type=='chest_xray'))])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like there are 28 chest x-rays that were read more than 15 minutes faster than their original order. All but the last nine had a probability of aortic dissection < 0.4."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, I'll take a look at anywhere that my algorithm made brain bleeds or aortic dissections with a probability of 0.5 or higher be read _slower._"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"worklist_prioritized[((worklist_prioritized.time_delta<0)&(worklist_prioritized.max_prob>=0.5))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like there were two cases where my algorithm caused an image to be read slower than the priority order it came in. Given that I had images with probabilities <0.5 that were read faster, it is definitely possible to improve my algorithm by adding some more heuristics. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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